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You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "Complaint against : Our mailing address is : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NY XXXX Phone : ( XXXX ) XXXX Email : XXXX I was on month to month, new owner took over fraudulently forged a new contract, which I did not know until I demanded a copy otherwise I was sueing, When I got it on XX/XX/XXXX she renewed it till XXXX. I NEVER agreed to renew with her. I went there multiple times in person owner only comes in between 12- 5, when normal people work. She is the only one who can give me " official '' cancelation form. the girl at the desk new and said owner blocked her from printing but I could take a photo. I reached out on XX/XX/XXXX again in person because I had bought a bottle of water she kept my XXXX on file I NEVER AGREED TO and charged it again. I hand wrote one on XX/XX/XXXX Sent text emails she is trying to charge me monthly. I have not been to gym in months, and still getting payment emails and phone calls."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was result of identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I sent a letter on XX/XX/XXXX to remove items that are UNKNOWN to me. I came across this law today and according to the Per FCRA 605B ( 15 U.S.C. 1681c-2 ) you are required to remove/block any accounts not later than 4 business days. Ive already included this in my previous Complaint # XXXX, XXXX, XXXX & I am including it again., XXXX XXXX XXXX XXXX XXXX XXXX Date opened XX/XX/XXXX Balance {$0.00} XXXX XXXX SERVICES XXXX Date opened XX/XX/XXXX Balance {$55.00} US DEPT OF EDXXXX XXXX Date opened XX/XX/XXXX {$89000.00} XXXX XXXX Date opened XX/XX/XXXX Balance {$0.00}"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "When I investigated my credit report, I realized that some of the information was erroneous. Equifax is required to authenticate these accounts under Sections 609 ( a ) ( 1 ) ( A ) and 611 ( a ) ( 1 ). ( A ). It is not acceptable to treat these reporting accounts as unconfirmed information without producing proof within the legal time range. You have NOT provided me a copy of ANY original documentation ( a consumer contract with my signature on it ) as required under Section 609 ( a ) ( 1 ) ( A ) Disclosures to consumers [ 15U.S.C. 1681g ] ( ( a ) Information on file ; sources ; report recipients. Equifax shall, upon request, and subject to 610 ( a ) ( 1 ) [ 1681h ], clearly and accurately disclose to the consumer. If the consumer to whom the file relates requests that the first 5 digits of the social security number ( or similar identification number ) of the consumer not be included in the disclosure and the consumer reporting agency has received appropriate proof of the identity of the requester, the consumer reporting agency shall so truncate a such number in such disclosure. & Section 611 ( a ) ( 1 ) ( A ) Procedure in case of disputedaccuracy [ 15 U.S.C. 1681i ] ( a ) Reinvestigations of Disputed Information. If the completeness or accuracy of any item of information contained in my file at Equifax is disputed by the consumer and the consumer notifies the agency directly, or indirectly through a creditor, the Bureaus shall, free of charge, conduct a reasonable reinvestigation to determine whether the disputed information is inaccurate and record the current status of the disputed information, or delete the item from my file in accordance with paragraph ( 5 ), before the end of the 30-day period beginning on the date on which the agency receives the notice of the dispute from the consumer or creditor Furthermore, you have failed to provide the method of verification as required under 15 USC1681i ( a ) ( 7 ). Please be advised that under 15USC1681i ( 5 ) ( A ) of the FCRA you are required to ... promptly REPAIR all information which can not be verified. Sections 609 ( a ) ( 1 ) ( A ) and 611 ( a ) ( 1 ). ( A ), please investigate the unverifiable accounts : XXXX XXXX XXXX XXXX XXXX XXXX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Last XX/XX/XXXX of this year, I sent a letter regarding inaccurate and unrecognizable accounts being reported on my credit report. It has been more than 60 days, and I haven't received any response from them and the accounts are still on my credit report. I am more than frustrated at this point. The law clearly states that an investigation must be had for these accounts that are being disputed and failure to do so within 30 days or more will result in the removal of those accounts. This is plainly in violation of the law at the expense of me and my peace. I demand that the accounts be deleted immediately or I will file for a lawsuit and seek compensation for damages. In addition, my information was also affected by the XXXX and XXXX data breach and may have gotten to the wrong people."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Money transfer, virtual currency, or money service",
"Sub-product": "Debt settlement",
"Consumer complaint narrative": "i recently had a fraudulent wire charge for XXXX XX/XX/XXXX and was denied my phone was stolen and i thi then bbva denied the dispute"
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "On XX/XX/2021 I contacted Capital one and asked for my annual fee of {$39.00} to be waived due to not being able to work the past 3 weeks because of COVID. To which I was basically told Capital one is offering no means of help to card holders in these rough times. The representatives were rude, unbothered and inconsiderate stating " Maybe they can push off the charges to another month. '' I don't know if I'll start back work next month I don't even know how I'm going to pay my utility bills. I wasn't even asking for my charges to be waived just their ridiculous annual fee that I don't have the money for. It's ridiculous I've been a customer for 5 years of on-time payments, never had to ask for something like this before and this is how I'm treated. After the card is cleared I'm canceling the account, cold hearted company."
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Problem with fees"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XXXX, XXXX, I became aware of fraudulent activity against my credit through my membership in XXXX XXXX XXXX through XXXX XXXX XXXX Two CRAs, XXXX and XXXX are listing 2 separate attempts from XXXX XXXX XXXX XXXX XXXX, XXXX, XXXX XXXX XXXX MO XXXX to obtain money in the amounts of {$4100.00} and {$3300.00}. XXXX lists the original debt as from ChecknGo, location unknown. This is likely a check cashing/payday loan operation. I have never used this company 's services, nor that of any other such entity. It should be noted that I filed fraud complaints in XXXX that were of a similar nature e.g., loan shops. Those complaints were investigated and resolved as fraud against my name and confidential information.
The XXXX XXXX XXXX I spoke with on XXXX said that sometimes these old files are resurrected and sold to another entity. The amounts in the prior fraud were approximately {$4000.00} and {$2500.00}."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I initiated a dispute with Equifax in XX/XX/XXXX regarding a derogatory remark indicating a past due amount of {$58.00} with a medical facility in XXXX Texas. This past due bill was paid in XX/XX/XXXX/XX/XX/XXXX.
I received an email telling me that my dispute had been completed ; however, when trying to access the information, the 'system ' would not let me in. I called Equifax on two occasions ... the most recent call being today, XX/XX/XXXX.
I shared my concern with a female employee who told me only that the dispute had been completed but she would n't tell me the outcome. She transferred me to XXXX and he told me that I had called the wrong number and to call XXXX between XXXX XXXX and XXXX XXXX. I called the number at about XXXX XXXX and, through an automated system was told that the center was n't open and to call between XXXX XXXX and XXXX XXXX!
I check my credit score routinely. Currently, it is XXXX with XXXX and XXXX with Equifax. While many might view this as insignificant, I do not. I am concerned about Equifax 's information coupled with the gap between the two agencies. I am also concerned about the lack of customer support/customer information provided ( or not provided ) by Equifax.
I appreciate any aide from CFPB. I can be reached via email to XXXX or by phone to XXXX.
Regards, XXXX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I sent in a letter and I haven't received a response. What am I supposed to do to resolve this situation? This company XXXX XXXX has closed my account, charged it off, and ssold it but they are still reporting that I'm late every month! They said my monthly amount is {$0.00}, but they're reporting me every month on this closed account, and they're trying to get paid again by doing this. This is false and inaccurate information they're reporting.
I found out that in the Fair Credit Reporting Act, section 611 ( a ), it is plainly stated that a failure to investigate these items within 30 days gives a reason immediately remove those items from my credi report, its been over 60 days so they should be deleted immediately. If they're not files as soon as possible I will file for litigation due to the stress you caused me. My information was also impacted by the XXXX data breach and may have got iinto the hands of the wrong person."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "My Oregon Tax refund check deposited in XX/XX/XXXX was cashed by B of A from the dept of revenue, but never posted to my account balance. In XX/XX/XXXX ' they verified this with no explanation, and requested I re deposit check electronically, Which I did. They posted the {$300.00} to my acct. for several days, then reversed it and told me Oregon dept of revenue rejected it, due to being more than 120 days old. Oregon says it Was withdrawn from Their account and won't reissue. B of A told ME to have Oregon file a claim against Them, to get MY money back!"
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem making or receiving payments"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Citi Bank advertise the promotion called Citi XXXX Account Package. I have copied their promo terms & eligibility. According to their promo, I'm eligible to receive {$700.00} cash bonus and I supposed to received the cash bonus by end of XX/XX/XXXX but they haven't give cash bonus yet. I have called several times since XXXX to confirm the eligibility and they confirmed that I'm eligible to receive cash bonus everytime I called however, they haven't paid me the cash bonus yet.
Per their requirement, I opened a new checking account on XX/XX/XXXX and I have to deposit and maintain minimum required balance for 60 days then The Cash Bonus will be credited within 90 calendar days from the date I complete all Required Activities.
Today, I called the bank and they confirmed me again but they have not credit the cash bonus. I believe they are publishing false advertising and this is a scam.
I also copy their current promo below. Only the date of offer expiration is different than in XXXX.
1 TERMS AND CONDITIONS Offer Eligible Customers can earn the {$200.00}, {$400.00}, {$700.00} or {$1500.00} Cash Bonus during the Offer Period by completing the following Required Activities.
By participating in the Checking Cash Bonus Offer, you agree to be bound by the following Terms and Conditions.
If you choose to change packages within 30 days of account opening ( see Package Options below ), your change reflects 1 ) that you have reviewed the new package offers terms and conditions, which appear in this document and 2 ) have consented to participate in the offer for that package.
Eligible Customers Checking Offer Eligible Customers are New-to-Citibank Checking Customers. New-to-Citibank Checking Customers have not been a signer on a Citibank checking account within the past 180 days.
Required Activities If Eligible Customers open their eligible accounts by any other means than those described, they will not be enrolled in this Offer and will not be eligible for the Cash Bonus.
Eligible Customers must : First, open a new consumer regular or interest checking account between XX/XX/XXXX and XX/XX/XXXX in an eligible Account Package listed in the Chart below ( Eligible Checking Account ). Certain Cash Bonus offers also require opening a new Citi Savings Account ( see Chart below ) in the same account package as the new Eligible Checking Account ( Eligible Savings Account ). The Eligible Savings Account must be opened within 30 days after the checking accounts Account Opening Date.
Second, enroll in this offer at the same time as account opening pursuant to the instructions provided. Customer is automatically enrolled when applying online Third, any owner of the eligible accounts must make at least the Minimum Deposit ( see Chart below ) in New-to-Citibank Funds, within 30 calendar days after the Account Opening Date of the checking account, into either the new Eligible Checking Account and/or the new Eligible Savings Account. Multiple deposits allowed.
New-to-Citibank Funds are 1 ) funds deposited from external accounts or payees other than Citibank , N.A . and 2 ) must be deposited using domestic ACH transfer, Direct Deposit, checks drawn on banks other than Citibank , N.A . or wire transfer. Cash deposits, Citi Global Transfers, international ACH transfers, and person-to-person transfer services such as XXXX XXXX, XXXX, XXXX, and XXXX, do not qualify as New-to-Citibank Funds.
Fourth, the Minimum Deposit ( see Chart below ) must be maintained for 60 consecutive calendar days from the date the Minimum Deposit is met ( Minimum Balance ). The Minimum Balance may be held entirely in one of the Eligible Accounts or across both of the Eligible Accounts.
Note : If you choose to keep your entire Minimum Balance in one account, any account that maintains a zero balance for 90 days is subject to closure in Citibanks sole discretion.
The Cash Bonus will be credited within 90 calendar days from the date you complete all Required Activities. At the time the Cash Bonus is paid, it will be credited to the new Eligible Checking Account. If the Eligible Checking Account is closed, then the Cash Bonus will be credited to the new Eligible Savings Account. Eligible accounts must be open and in good standing from the time they are opened until the Cash Bonus is credited or you will not be eligible to receive the Cash Bonus.
Chart Cash Bonus Account Package Minimum Deposit and Minimum Balance Savings Account Required {$200.00} Basic Banking Package {$5000.00} no {$400.00} Citibank Account Package Citibank Account Package {$15000.00} {$700.00} Citi XXXX Account Package {$50000.00} {$1500.00} Citigold Account Package Citigold Account Package {$200000.00} Package Options Even though Citibank allows customers to change their package type at any time, changing your package may impact your eligibility for this promotion. You are only eligible to earn the Cash Bonus for the package associated with your Eligible Checking Account on the 30th calendar day after the Account Opening Date of your checking account.
In addition, if you change your package type to one with a lower Minimum Deposit and Minimum Balance during the 60 day maintenance period, you will not fulfill Required Activities. For instance, you may not convert your Citigold Account Package to the Citi XXXX Account, Citibank Account, or Basic Banking Packages and still be eligible for the Cash Bonus. You may not convert your Citi XXXX Account Package to the Citibank Account or Basic Banking Package, nor can you convert from your Citibank Account Package to the Basic Banking Package and still be eligible for the Cash Bonus.
The Citigold Account Package Cash Bonus offer is available in branch, by calling XXXX, or online at www.citi.com/citigoldoffer.
Applicable Fees For the Basic Banking Package, to waive the XXXX XXXX monthly service fee, make one qualifying direct deposit per statement period and one qualifying bill payment per statement period, or maintain a {$1500.00} or more combined average monthly balance in eligible linked accounts. A monthly service fee of {$25.00} and a {$2.00} non-Citibank ATM fee apply to the checking account in the Citibank Account Package if a combined average monthly balance of {$10000.00} or more is not maintained. A monthly service fee of {$30.00} applies to the checking account in the Citi XXXX Account Package if a combined average monthly balance of {$50000.00} or more is not maintained. There is no monthly service fee for a checking account in the Citigold Account Package. If you do not maintain a minimum combined average monthly balance of {$200000.00} in eligible linked deposit, retirement and investment accounts, your Citigold Account Package will be converted to the Citi XXXX Account Package and your accounts will be subject to the terms and conditions then in effect for that package. Fees could reduce account earnings. For all account pricing details please visit citi.com/compareaccounts.
IMPORTANT TAX INFORMATION ( {$200.00}, {$400.00}, {$700.00} and {$1500.00} Offers ) : The bonus will be reported to the IRS as interest to the first signer on the account, in the year received, as required by applicable law. Bonus payments received by U.S. persons will be reported on IRS Form 1099-INT for the year received. Bonus payments received by non-U.S. persons will be reported on IRS Form 1042-S for the year received. Customer is responsible for any applicable taxes and consulting a tax advisor. Citi is not a tax advisor. To be eligible for the bonus, U.S. persons must furnish Citibank with a valid IRS Form W-9 ( Request for Taxpayer Identification Number and Certification ). Foreign individuals must furnish a valid IRS Form W-8BEN ( Certificate of Foreign Status of Beneficial Owner for United States Tax Withholding and Reporting ( Individuals ) ).
As applicable, variable rates subject to change before and after account opening. Terms, conditions and fees for accounts, products, programs and services are subject to change and may be withdrawn at any time prior to enrollment. All accounts subject to applicable terms, fees, and charges. Fees may reduce earnings. Accounts subject to approval. Participation and enrollment in a promotional offer does not guarantee eligibility or fulfillment of the promotional offer. Annual Percentage Yields ( APYs ) are variable and subject to change without notice after the account is opened.
APYs are accurate as of XX/XX/XXXX Interest Checking APY is 0.03 % for Citigold and Citi XXXX and 0.01 % for the Citibank Account Package. Depending on balances, APYs in Citi Savings Accounts in the Citigold and Citi XXXX packages range from 0.04 % to 0.15 %, and 0.04 % to 0.13 % in the Citibank Account Package.
Speak with a Bank representative at your local branch for more details.
This offer is subject to the disclosure available at www.citi.com/offerdetails.
XXXX XXXX XXXX XXXX XXXX XXXX. All rights reserved. XXXX and the XXXX marks used herein are trademarks of XXXX XXXX XXXX XXXX XXXX. Other product and company names mentioned herein are the property of their respective owners. XXXX Transfers through Citibank Online and the Citi Mobile App are subject to terms, conditions and restrictions in the Citibank XXXX Transfer Terms and Conditions and the Citibank Client Manual-Consumer Accounts.
XXXX is a service of XXXX XXXX XXXX, a licensed provider of money transfer services ( NMLS ID : XXXX ). All money transmission is provided by XXXX XXXX XXXX pursuant to XXXX XXXX XXXX licenses. XXXX XXXX XXXX XXXX XXXX
XXXX Fees charged by other institutions for your transactions on non-Citibank ATMs are beyond Citibank 's control and are in addition to the fees listed here.
XXXX Separate enrollment of eligible consumer checking account in Citi XXXX Rewards required. Eligible customers with a checking account in the Citi XXXX Bank Account, Citigold Account, Citi XXXX Account or the Citibank Account Packages can enroll their checking accounts in XXXX Rewards. Eligibility and participation are subject to terms, conditions and restrictions. Customer must be a citizen or resident alien of the United States ( U.S. ) with a valid U.S. taxpayer identification number. Taxes, if any, are customer 's responsibility. You must complete 1 qualifying Bill Payment and 1 qualifying Direct Deposit each statement period in order to earn monthly XXXX Points for eligible products and services linked to your checking account. Citigold, Citi XXXX and Citi XXXX Bank clients who do not complete these transaction requirements can earn points at a reduced level. For complete details, see the Citi XXXX Rewards Terms and Conditions.
4 Requires a checking account in a Citigold or Citi XXXX Account Package. Accounts are subject to approval.
5 As a Citi XXXX client, you can receive banking and lending services, including the Citi XXXX Account Package, from Citibank , N.A . ( Citibank ), Member FDIC and XXXX XXXX XXXX XXXX # XXXX, along with financial planning and investment products as a client of Citi XXXX XXXX XXXX, a business of Citigroup XXXX, that offers investment guidance, products, and services through Citigroup XXXX XXXX XXXX XXXX XXXX XXXX, member XXXX. Citigroup XXXX XXXX XXXX ( XXXX ) offers insurance products. In California, XXXX does business as Citigroup XXXX XXXX XXXX, XXXX XXXX license number XXXX ). Citibank, XXXX, and XXXX are affiliated companies under the common control of Citigroup Inc .
6 Claim is based on locations in the United States where customers can make cash withdrawals with no surcharge ( usage ) fee by the ATM operator. Citibank customers can get cash, get information and make transfers between their eligible linked Citibank accounts with no surcharge fee when they use their Citibank Banking Card at ATMs in the U.S. located at Citibank branches, ATMs at select retail store locations, and ATMs participating in the XXXX XXXX. ATMs in retail stores and at other non-Citi locations are not owned or operated by Citibank and offer fewer functions. Use the Citi XXXX XXXX Locator on Citibank Online or the Citi Mobile App to find the nearest ATMs and branches, including non-Citibank ATMs in the U.S. with surcharge-free access.
7 Regular account charges apply. Citibank does not charge you a fee for using the Citi Mobile App or the Mobile Check Deposit service. You must have Internet access through your mobile device and charges from your wireless carrier may apply. The Mobile Check Deposit service is for personal checking accounts only and is subject to eligibility. Deposit limits and other restrictions apply.
XXXX Citigroup XXXX Citi , Citi and XXXX XXXX and other marks used herein are service marks of Citigroup XXXX or its affiliates, used and registered throughout the world."
}
Output:
{
"Issue": "Opening an account",
"Sub-issue": "Didn't receive terms that were advertised"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I am filing the complaint against UNIVERSITY OF ARIZONA because they have ignored my request for a reinvestigation since XX/XX/2021. I have not had one response from this agency! Per FCRA 623 ( b ) ( 2 ) and 611 ( a ) ( 1 ) and section 611 ( 5 ) ( a ) of the FCRA, they are required to " promptly delete all information which can not be verified '' and that has taken over 30 days to investigate that I have disputed. PLEASE DO NOT CHANGE THIS TO A DISPUTE ISSUE! This is a VIOLATION OF MY CONSUMER RIGHTS ISSUE! Your assistance in resolving this concern as soon as possible is appreciated. Thank you."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "So I have been noticing money slowly being taken out of my account. Also on XX/XX/2023 I made an transfer for {$4000.00} and was only allowed to use XXXX so I sent {$1500.00} back to my account and now I cant find my money."
}
Output:
{
"Issue": "Problem with a lender or other company charging your account",
"Sub-issue": "Money was taken from your account on the wrong day or for the wrong amount"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In XX/XX/2021, I pulled my credit report from XXXX and XXXX and to my amazement, saw that a creditor was reporting me as a charge off. I immediately sent a dispute letter via certified mail dated XX/XX/2021 ( Tracking # : XXXX ) to validate this debt. I never received a written response to this inquiry.
After not receiving a response, I disputed this information with XXXX and XXXX directly. I got a letter back about three months past my original inquiry date stating that my debt was verified. However, this debt is still not validated per the requirements of the FCRA."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Investigation took more than 30 days"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "I currently have a mortgage with regions bank. I recently got a letter in the mail about my mortgage increasing due to homeowners insurance increase nationwide. I called regions and request that they terminate my mortgage insurance ( PMI ) which would help maintain my mortgage payments to where it was manageable. The representative I spoke to at the time told me that he was going to put this request in which is due to me base on the length of my mortgage with them. a few weeks later I got a denial letter from regions stating that they are unable to terminate my PMI due to delinquency on my account. my account is not in any delinquency back in XX/XX/2014 I lost my job and was going through a hardship I missed one payment and it was due to the fact I was in a process of applying for a modification to help me from losing my house then. Now with this increase and regions declining to terminate this insurance I am having a hard time making my payments and fear I may fall behind on my payments"
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Good day, I am XXXX XXXX XXXX and the reason I am reaching out and writing to complain on XXXX and their lack of response and reporting. I have continued to follow every step on removing false accounts that are reporting and they have yet to comply. They have exceeded the 30 days multiple times and at this point, the issue has not been resolved. They keep reporting accounts that do not belong to me. I have mailed several letters via certified mail, that are dated, that have everything listed in detail, that have everything required and they still are not doing their job in removing their items.
The following are some of the dates of the letters I have mailed out : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX ALL THESE LETTERS HAVE BEEN SENT VIA CERTIFIED MAIL AND HAVE A STAMP WHEN THEY HAVE RECEIVED MY LETTERS."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "This is XXXX XXXX XXXX, who is submitting this CFPB complaint myself and to inform you that there is no third party involve in the process. Upon reviewing my updates, incorrect information was found in my reports. The XXXX Credit Bureau has not complied with the Fair Credit Reporting Act, 15 USC Sections 1681i, and continued reporting as unverified information without any proof provided, within the time allowed by law, is not authorized. Below is the account that is reporting on my Credit Reports : JEFFERSON CAPITAL SYST Balance {$300.00} Date opened XXXX XXXX, 2021"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Payday loan debt",
"Consumer complaint narrative": "This debt has been paid in full and should be removed."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was paid"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Payday loan debt",
"Consumer complaint narrative": "I had an unpaid balance. They keep calling me and my family members threatening to they are going to sue me, the police is going to my house and they they're going to call my employer speak with my supervisor and garnish my wages."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Used obscene, profane, or other abusive language"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "As of XX/XX/XXXX, there are inaccurate items reporting on my EQUIFAX credit profile. I am asking that CFPB investigate Equifax for failure to protect my consumer rights. Equifax 's actions are clearly illegal as described in the information listed below.
How can Equifax state that it has completed its investigation, including contacting the furnisher of the information, where applicable, and not provide me the exact details of their communication and validation information received?
A summary of the discrepancies listed on my Equifax credit report are listed below : Trade : XXXX XXXX XXXX - balance {$13000.00} EQUIFAX states that this information was verified and belongs to me. EQUIFAX also states it verified the information is reported correctly. How is it possible that EQUIFAX missed the double reporting of debt on my credit profile? There is a collection account on my credit report listed by XXXX XXXX XXXX for {$10000.00} listing the original creditor name as XXXX XXXX XXXX. The XXXX XXXX XXXX account states it was opened on XX/XX/XXXX. The XXXX XXXX XXXX Payment history lists their account as " late '' during the months of XXXX XXXX. The XXXX XXXX XXXX account lists the responsibility as an individual account while XXXX XXXXXXXX XXXX lists the responsibility as a joint account.
Once again, how is it possible that EQUIFAX reviewed my credit report and investigated my concerns, and completely overlooked this major issue? According to fair reporting practices, if the original creditor sold the debt to a collection agency, it should not continue to report a balance owed. PLEASE DELETE THE ALIVE CREDIT UNION INFORMATION AND PROFESSIONAL DEBT INFORMATION AS THEY BOTH ARE NOT ACCURATELY REPORTED ON MY EQUIFAX CREDIT REPORT.
NOT AUTHORIZED INQUIRIES : PLEASE DELETE!
Inquiry : XXXX - XX/XX/XXXX Inquiry : FACTUAL DATA - XX/XX/XXXX Inquiry : XXXX - XX/XX/XXXX Inquiry : XXXX XXXX - XX/XX/XXXX Inquiry : XXXX XXXX, XXXX - XX/XX/XXXX NON REPORTING DISPUTES Trade : XXXX XXXX THE DISPUTED ITEM IS CURRENTLY REPORTING ON THE EQUIFAX CREDIT FILE."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I recieved my back pay of almost 30000 usd I got XXXX in cashiers checks and after buying a vehical and some online purchases that I returned a loan company made bad deposits and I got charged for them the chase bank closed my account with a remainder of at the least XXXX usd still in there and didn't give me the rest of my money plus this loan company has followed me from wells Fargo to chase bank to key bank and is still calling saying now that they are the us government grant department and I got approved for a loyalty grant of XXXX usd which is about the amount they have stolen from me over about a ten year period. I would really like to get my money back and I have made police reports and they haven't done anything please help me out"
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Funds not received from closed account"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "The bureau is reporting an account for XXXX XXXX as closed/ charged off that does not belong to me and it's affecting my ability to obtain new credit. I have disputed this account already months ago to no avail."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "My XXXX XXXX went from XXXX to XXXX for no apparent reason, I only have XXXX lines of credit and they all get paid on time and don't build up at all. Then XXXX days ago it went down another XXXX points, I went XXXX to XXXX in a short amount of time. This has NEVER happened before and it's leaving me to wonder."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Opened a TD XXXX Checking account ending XXXX in XX/XX/2022, completed required direct deposit of {$2500.00} in 60 days but never got the advertised {$400.00} new account bonus. There was also a fraud on the account so the TD fraud department set the account to " no check '' status. I went to the local branch and opened a new TD XXXX Checking account ending XXXX and the local branch agent told me the {$400.00} bonus will be credited to this new XXXX account, but still never received it."
}
Output:
{
"Issue": "Opening an account",
"Sub-issue": "Didn't receive terms that were advertised"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Lease",
"Consumer complaint narrative": "This complaint is regarding an egregious situation that is occurring to my wife and I by GM Financial with regards to a XXXX XXXX XXXX and a sequence of events that occurred during our lease return process.
I have mailed, by certified mail with return receipt request a very detailed summary of the events that occurred. I am attaching my correspondence from XX/XX/XXXX for your reference, along with the supporting certified mail documentation that proves this letter was received by GM Financial.
In summary, I visited my mortgage lender to take advantage of the current historic low interest rates and reduce my current mortgage from XXXX XXXX to XXXX XXXX, a move that represents a substantial savings to my family both in the current monthly payment and the long term finance cost for my mortgage. I was rejected for this mortgage, as GM Financial is reporting several late payments and a past due status on my returned lease. I left his office thoroughly embarrassed and extremely upset.
All my life I have been extremely responsible with my credit obligations and I have never missed not one payment. I've always enjoyed XXXX credit scores and the easy access to favorable financing terms this credit score provides. Now, I am facing the possibility of losing out on a savings of tens of thousands of dollars due to this egregious error by GM Financial.
As you can see in my correspondence, GM Financial fumbled on three seperate occasion the pre lease return Vehicle Inspection appointment that caused my wife to miss several days of work so that she could be home for this appointment. The inspector was a no call, no show on two separate occasions, and because of this, after much back and forth XXXX whom was presented as the supervisor of the department granted a credit of {$390.00} to cover the disposition fee. I urge you to read the letter from XX/XX/XXXX to understand the context of this situation and how exactly GM financial fumbled the lease return experience.
Well, now it seems that this was never documented, ( everything else is documented but conveniently the disposition fee credit is not ). I implore GM Financial to simply LISTEN TO THE RECORDINGS to corroborate my version of the story. They clearly state that calls are being recorded, this is a great time to reach into the recording vault. My detailed notes referenced in the XX/XX/XXXX Correspondence will make it very easy for you to locate the specific call.
I have also on numerous occasions informed GM Financial of this, in writing, by phone, etc. I truly thought this was resolved with my XX/XX/XXXX letter but now they are destroying my wife and my credit history that is impeccable for 30+ years. We will not tolerate this any longer and want to make very clear should we miss out on the significant savings of refinancing our home mortgage we will pursue a claim for these damages. Easily, valued at XXXX dollars over the life of a 30 year mortgage."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "On XX/XX/2022 I was fraudulently charged extra fees without being explained to what the charge was for. As well as a fraudulent {$1000.00} dollar down payment, a {$1400.00} dollar XXXX XXXX fee, a {$96.00} dollar roadside Assistance fee, and a list of other fraudulent fees adding up to {$640.00} dollars. And the vehicle interest rate to jump to 23.01 % that was not mentioned or explained to me. Resulting in the vehicle purchase price going from {$12000.00} dollars to {$33000.00} dollars."
}
Output:
{
"Issue": "Managing the loan or lease",
"Sub-issue": "Problem with fees charged"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional fixed mortgage",
"Consumer complaint narrative": "i received my mortgage statement today noting that my mortgage statement has been changed out of the blue from XXXX to XXXX effective XXXX XXXX. I am current waiting on my recast to reduce my mortgage. I do n't understand why is it so difficult to give me my recast, It is just unbelievable that I have been trying to get this simple reduction since XXXX XXXX. Please explain this monthly increase."
}
Output:
{
"Issue": "Application, originator, mortgage broker",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Bank account or service",
"Sub-product": "Checking account",
"Consumer complaint narrative": "To whom this may concern, I am writing in response to claims by Scottrade that they will not respond to the CFPB. This bad behavior by Scottrade appears to be a repeat of other bad behavior.
In the attached you will see that Scottrade for a period of no less than 2 years hide behind the OCC while defrauding regulatory agencies by maintaining the veil that their company has less than XXXX in assets."
}
Output:
{
"Issue": "Deposits and withdrawals",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have accounts on my report that inaccuracies they have been disputed late payments due to Covid forbearance and also collection accounts have not been validated I have also been harassed repeatedly these accounts need to be remove these claims are fcra violations"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Barclaycard would not take a loan check from my credit union to pay off my credit card ; thus, saving me interest and letting me close the account with a {$0.00} balance. Barclaycard said my account was not open long enough to take a payment that way."
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "Problem during payment process"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Prepaid card",
"Sub-product": "Transit card",
"Consumer complaint narrative": "I have been waiting for my direct deposit from Rush XXXX. I 've been trying to contact them since XXXX and all I get is 5 rings then a disconnection. The company has failed to inform me or a t other users as to what the issue is. There is a severe lack of communication from Rush Card. Not able to access my account online or through their mobile app."
}
Output:
{
"Issue": "Unauthorized transactions/trans. issues",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "There is a entry on my credit report from Vance & Hoffman. They claim I have a debt, but when I called, they are mean and will not provide me any documents pertaining to this loan. I am in the process of cleaning up my credit report and I seen there a many hard inquiries that are not mine. I have disputed this twice and they refuse to remove it but yet, they will not provide me any information.
I have no details to report because I have no knowledge of this loan they are claiming I owe. Any help with resolution, would be appreciated.
Thanks."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "On XXXX/XXXX/2013, XXXX ( at XXXX XXXX ) in XXXX XXXX, Indiana told me that my upcoming XXXX would cost me ( out of my pocket ) {$25.00} deductible and either {$12.00} or {$26.00} extra based on whether it was an easy XXXX or a difficult one. XXXX said that " the rest will be written off because of our contract with XXXX XXXX. '' They XXXX out on XXXX/XXXX/13. Afterwards, despite having what I was told was an easy XXXX, I was told that I owed the higher {$51.00} amount. Fine. So I paid it off before leaving the office that same day. Then, in XXXX, I get a bill from XXXX XXXX corporate office for another {$60.00}, still owed for my procedure!!!! I called XXXX at the XXXX XXXX office where I had my procedure done, and explained that I paid the bill in full ... ... .The {$51.00} that I was told was my share of the complete bill. She left the phone, and came back shortly. XXXX exact words to me when she returned to the phone was " Yes, you are correct. The total charge should have only been {$51.00}! '' which I had already paid! So I also called " XXXX '', back from the corporate office, and informed him what XXXX said. Next thing I know, I 'm getting another {$60.00} bill in XXXX 2013 from a DEBT COLLECTION company! So I called " XXXX '' from this IC SYSTEM debt collection company, and explained this entire scenario to her. I emphasized that XXXX, from XXXX, told me TWICE on 2 different occasions that my total bill of {$51.00} was paid up, and I owed nothing further!!! What kind of company is this " XXXX XXXX??? '' Now it 's affecting my CREDIT SCORE!! iT 'S GONE FROM XXXX DOWN TO XXXX! That 's not fair!! Please get back to me ASAP. I 'm considering calling the BBB. XXXX XXXX XXXX"
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Payroll card",
"Consumer complaint narrative": "XXXX, 2021 to present day XXXX, 2021. Net spend skylight has my pin blocked on my permanent card they had sent me in the mail. I had just transferred the funds from my temporary card to my permanent card XXXX XXXX, 2021 used for one week and it's blocked... i have continually sent them document after document through emails and constantly it is too dark, my ss. CARD is not the right color. They ask for a copy of bank statement. My I.D . HAS been sent numerous times and can't even tell me if there is money in there 3 of my pay checks are in there. And Will not release it.
This is the no. I callXXXXXXXX I will have to move unable to pay my rent... i have no money. I have to borrow to eat ... ..its XXXX ridiculous"
}
Output:
{
"Issue": "Trouble using the card",
"Sub-issue": "Trouble using the card to spend money in a store or online"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX - XXXX XXXX {$1300.00} XXXX XXXX XXXX {$1300.00} Experian XXXX XXXX {$1300.00} XX/XX/XXXX, The account was transferred/sold to a debt collector, i informed all 3 CRA 's that there shouldn't be a balance, past due balance, and a scheduled monthly payment on a closed, and transferred/sold account. But there have responded saying the account meets FCRA even after i sent them proof of the letters from the collectors. Additionally, 3 years worth of payment history is missing, they removed from XX/XX/XXXX to XX/XX/XXXX and are reporting XX/XX/XXXX as the date the account was charged off/closed when the actual date the account closed is XX/XX/XXXX.
XXXX - XXXX XXXX XXXX {$870.00} XXXX - XXXX XXXX XXXX {$870.00} Experian- XXXX XXXX XXXX {$870.00} The account was sold to XXXX XXXX XX/XX/XXXX but it is still reporting a balance, and a past due amount. There are also other various inaccuracies that i have pointed out to the credit reporting companies such as the date which the account was closed, on XXXX it shows XX/XX/XXXX but then there is payment history after that date, Experian says account was charged off in XX/XX/XXXX after the account defaulted for 60 days, XXXX says the account was charged off XX/XX/XXXX after being late for 90 days."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "My wife is currently unable to work full-time due to our children being in hybrid schooling because of the covid pandemic. I reached out to Comenity bank because we are struggling as she used her unemployment due to a previous lay off due to covid she has very little income. They are refusing to help us at all with our payments, I have 3 credit cards through them, XXXX XXXX XXXX, XXXX and XXXXXXXX XXXX XXXX. They will not give any help. There is no way feasible at this time that I am able to pay these credit card payments at this time. I only asked for the one month we missed last month on both and the one we can not make this month, they will not help. This is unacceptable in times like this that they are so uncaring and heartless toward their customer 's."
}
Output:
{
"Issue": "Struggling to pay your bill",
"Sub-issue": "Credit card company won't work with you while you're going through financial hardship"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "Receivables Performance Management , LLC is trying to collect for a debt that they are saying is owed. I spoke on the phone with them for XXXX minutes on XX/XX/2021. The companies representative said they can not provide me proof of verification of the debt I owe. I informed the representative that is a violation of fair debt collection act. The end result the representative never provided me with any validation of the debt."
}
Output:
{
"Issue": "Written notification about debt",
"Sub-issue": "Didn't receive enough information to verify debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX Reported : XXXX XXXX, 2019 was transferred to XXXX Reported : XXXX XXXX, 2019 which was paid off and closed on XX/XX/2019 but it does not reflect that on my credit report."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Other personal consumer report",
"Consumer complaint narrative": "I have identified information in my credit file that is inaccurate. The findings are inaccurate from Diversified Consultants for a XXXX XXXX XXXX account. The balance was originated in XX/XX/2017 and paid in full in XX/XX/2017. I never received a collection notice from the above named Agency. I spoke only with the XXXX XXXX XXXX representatives. This is the sole reason that I am disputing this inaccurate information reported against my credit as being negative, it has adversely affected my credit which has been denied in the last 60 days. I request that the inaccurate report put on my file be corrected or deleted inaccurate, incomplete or unverifiable information. This is my right as a consumer."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information is incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "ARS ACCOUNT RESOLUTION is claiming that I owe them {$1400.00} but I didn't recognize this debt. Please Remove this inaccurate collection from my report."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Non-federal student loan",
"Consumer complaint narrative": "XX/XX/XXXX, my payment was lost. When I called customer service I was greeted by a nasty customer service respresentative who refused to help me. I then called the office XXXX, XXXX New Jersey, as Hesaa is a state agency. They told me that there was no one who oversaw the agency and no one to file a formal complaint with. They then transferred me to someone within the agency. I spoke XXXX and also emailed with them. Neither of them was responsive. At that point I made requests to have the notes on my account released to me. I have in writing that they would do this. This began XX/XX/XXXX. I did not receive the notes until I reached out to the Office XXXX XX/XX/XXXX. I was told XXXX, that I was not ignored, but I should be happy I received the information. I sent monthly emails XXXX with no response or limited response. He only responded to me once XXXX office told him that I had reached out to complain during her lunch break. I received an email from him a half hour after his lunch break. I also requested my records XX/XX/XXXX, which was documented in the notes provided to me by the XXXX of the Ombudsman. When I spoke with him on the phone he denied that I requested the information XX/XX/XXXX. The notes state that the information was sent to me, which is a lie. I never received any correspondence from Hesaa at that time. I also inquired why phone calls were missing from the notes. He stated that was all they had. The phone calls that were missing were when Hesaa made errors on my account. Hesaa has refused to provide in writing that they made any errors, even when they misapplied funds. They are also refusing to admit that my requests were ignored. They are unresponsive, unprofessional, disrespectful and lack common courtesy when dealing with the public. There is also an environment of cronyism. The Office XXXX is more in the business of covering up mistakes of its employees than investigating their wrong doing. In searching XXXX background he worked for a mortgage company, I do n't know that this qualifies him to be the Chief Compliance Officer for a State Agency for student loans. Also, the director of the agency is appointed XXXX. Is it a coincidence that the director went to the same XXXX school as the Chief Compliance Officer? Also how is there no one to file a complaint with against the agency in the state? Also, should n't the office XXXX be separate from the other staff? How is someone from the office XXXX XXXX XXXX mentioning my case to another staff member XXXX XXXX XXXX XXXX during lunch? Is this appropriate?"
}
Output:
{
"Issue": "Dealing with my lender or servicer",
"Sub-issue": "Having problems with customer service"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hi I am submitting this XXXX XXXX without any influence and this is not a third party. XXXX has low and unfair credit number for me in their report. I have complained. The problem has not been resolved. my fico has me at a credit score over XXXX has me at a score around XXXX. That is a huge difference. XXXX paints me as a XXXX. XXXX XXXX say I have good credit. What the heck is going on here. i have almost no debt and my identity was stolen causing my score to drop n i made this clear for 60 days straight with XXXX i spoke to a representative agent name XXXX and XXXX and XXXX from the fraud department I prefer to speak to a us rept but they refused they had me on mute for 4 hours which was hurtful I have a perfect repayment record. I have very low credit utilization. I have three negative credit items outstanding debt now. I have modest but ok income. Social Security. Something is wrong with XXXX. I do not understand why they are abusing consumers .This was a fist step towards attempting resolution. They kept lying telling me they disputed n its not reporting but it keep reporting this inaccurate information without my authorization. They refused or were unable to verify n remove the inquiries and its been XXXX n they record the calls n admitted they had my police report n ftc and affidavit That was after attempting to contact XXXX more than 21 times. XXXX is an abusive company. They are supposed to be protecting consumers. They need to be reigned in. they are causing me XXXX XXXX and stopping me from getting this job offer n now XXXX XXXX n cant provide to my XXXX XXXX XXXX daughter PLEASE HELP ME PLEASE"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Original case from 2019 is XXXX I am not making any progress with this bank and its still on my credit report.
This is a nightmare, and this bank is putting the burdon on me for a mustake they made.
In a nutshell, XXXX XXXX XXXX XXXX pulled an inquiry on my report though I did not authorize it nor request any lines of credit with them. This was on XX/XX/2019. It was apparanetly fraud and they were supposed to congact the credit reporting agency to get it removed.
After mulitple calls, they said thry would remove and here we are 7 months later and I am still waiting and noone is replying to me anymore.
Please help me XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX policy requires adverse reporting to credit bureaus for seven years for accounts which have been PAID IN FULL following placement into profit and loss status. XXXX is taking advantage of protection provided by a FCRA that does n't exist. FCRA Section 1681c relating to Accounts placed for collection or charged to profit and loss does not directly or specifically include accounts charged to profit and loss which have a CURRENT PAID IN FULL status. The law clearly states Accounts placed for collection or charged to profit and loss WITHOUT specifying reporting of accounts which have a PAID IN FULL status following a charge to profit and loss. I do not believe the INTENT of this FCRA law is to penalize a debtor who has PAID HIS DEBT IN FULL AT ANY TIME. Continued adverse reporting to credit bureaus penalizes the consumer AFTER he has paid his account IN FULL. Additionally, as a result of the lack of inclusion of accounts which are paid in full status under FCRA, XXXX has no right to maintain and adversely report information which the government has deemed nonexistent and not covered under this law."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "My name is XXXX XXXX. My fiance ' shares the same last name ( XXXX ) with my neighbor. " Credit Control LLC '' ( formerly known as " XXXX XXXX XXXX XXXX '' ) has been repeatedly calling our household asking for this neighbor.
Yesterday ( XX/XX/XXXX ), they called again, and for the first time, I answered the phone to clear up the misunderstanding. The representative thanked me, and then hung up.
Today, ( XX/XX/XXXX ), one day later, they called again. I answered, but this time asked for their mailing address. They promptly hung up the phone.
Can you please tell me what my options are at this point, to end these daily phone calls. Thank you in advance for your help Sincerely, XXXX XXXX"
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Frequent or repeated calls"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "I, XXXX XXXX, entered into a consumer credit transaction with WELLS FARGO AUTO on XX/XX/2021. Not only did the company give me false and deceptive forms, they failed to assure a meaningful disclosure of credit terms. According to the congressional findings of 15 USC 1601, I, the consumer/natural person, am protected against inaccurate and unfair credit billing and credit card practices. Congress also found that there has been a recent trend toward leasing automobiles for consumer use as an alternative to installment credit sales and that these leases have been offered without adequate cost disclosures. My contract does not say anything about my right to rescind. On XX/XX/2021, I mailed WELLS FARGO AUTO an Affidavit of Truth, Cease and Desist, and my right of rescission paperwork. By law, the company has 20 days to respond. Today makes the 20th day because they received my certified mail on XX/XX/2021 at XXXX XXXX A gentleman by the name of XXXX called but I had contracted COVID and didnt feel too good when he called. I havent heard from them since. Whenever I try to call them back, a representative says someone will call me back. I just want what is lawfully owed to me. I have security interest that secured ALL obligations, meaning the car was paid for when I signed for it. The Truth in Lending Act informed me of all of this fraudulent activity."
}
Output:
{
"Issue": "Getting a loan or lease",
"Sub-issue": "Fraudulent loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "During process of securing a XXXX. construction loan I find out U.S. Bank reported my account 30 days late in XX/XX/XXXX to all the credit bureaus. My due date is the XXXX of each month and my usual minimum payments are around {$120.00}.
On XX/XX/XXXX I made a payment of {$150.00} On XXXX XXXX another payment of {$160.00} On XX/XX/XXXX made a payment of {$200.00} on XX/XX/XXXX made a payment of {$260.00} and XX/XX/XXXX Payment of {$2500.00} was made.
All these payments were successful and easily covered my minimum.
-- -- There were a couple larger payments one on XX/XX/XXXX for {$4400.00} that was returned. This payment was in error as the number " XXXX '' somehow duplicated. I even contacted US.Bank to inform them the payment was meant to be {$480.00}.
Additionally, on XX/XX/XXXX a payments for {$2500.00} was made twice on the same day due to internet glitches that seemed to have been nation wide. One of the {$2500.00} payments was returned as expected. But again that payment was an extra payment. However, U.S. Bank reported me 30 days late in XX/XX/XXXX to all three credit bureaus. I contacted U.S. Bank on XX/XX/XXXX at XXXX and talked to a representative and a supervisor ( XXXX ), I clearly explained the situation to both However, both the representative and Supervisor ( XXXX ) refused to change or correct the 30 day late reported.
My history : I have been in business since XX/XX/XXXX have my credit history goes back 25 years and have easily over XXXX different accounts. I have NEVER EVER been 30-days late on a payment with any of my past accounts. I have had XXXX or XXXX reported incorrectly and resolved those. I have a successful XXXX in the range of {$400000.00} and XXXX dollars, and an impeccable reputation. US. Bank 's reckless reporting will cost me hundreds of thousands in higher interest charges. I made every reasonable effort to resolve this but US Bank refused on several attempts."
}
Output:
{
"Issue": "Delinquent account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I noticed some inaccurate and unverified accounts on my credit report, I sent a dispute to Experian and they refuse to do a proper investigation. they keep sending me back notices saying my accounts were verified, I know this is not true because I reached out to the data furnishers and they were unable to provide me with any debt validation. Experian keep using their data matching system e Oscar and is adamant about not doing a proper investigation. The data furnishers stated they had no information on the accounts, so hw in the world did Experian verify these accounts. I reached out to the XXXX to complain and the XXXX said Experian did not respond to them. please help my next step is to file a lawsuit."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Mortgage debt",
"Consumer complaint narrative": "I refinance my home loan in XX/XX/XXXX, and was mailed a letter by International Capital Group , Inc. ( the loan servicer ), stating that I was not required to make a XX/XX/XXXX payment since they sold the loan to XXXX XXXX in XXXX. I was contacted today ( XX/XX/XXXX ) by an attorney in the legal office of International Capital Group stating that they made a mistake in mailing me that letter, and if needed to pay it immediately otherwise International Capital Group could take legal action against me in court to extract payment for the amount of {$1500.00}."
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Threatened to sue you for very old debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "I had my car repossessed by Wells Fargo illegally in 2008. 2 years ago I received notice of the CPI settlement, and by the terms I am entitled to around $ XXXX. They have refused to communicate with me for almost 2 years now. When I call I get passed around, placed on hold forever, or hung up on. I have been told they cant find my account yet I just received a {$24.00} check for overpayment of repo fees from 2008. If they can audit something from 13 years ago then they have my account info and are just refusing to pay what they were required to by the CPI settlement."
}
Output:
{
"Issue": "Struggling to pay your loan",
"Sub-issue": "Lender trying to repossess or disable the vehicle"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "To whom it may concern, YOU BROKE THE LAW!!!
Pursuant to 15 USC 16814 ( a ) ( 1 ) the banking system is dependent upon fair and accurate credit reporting information. This inaccurate information was furnished, and I was not notified when during the investigation process.
Pursuant to 15 USC 1681 ( a ) ( 1 ) This grave error undermined my confidence in your companies procedures, and as a result of this I will no longer extend credit and want these items removed IMMEDITLEY.
Pursuant to 15 USC 1681 ( a ) ( 2 ) Your company is a powerhouse elaborate mechanism. This mishap that your company has caused has ruined my reputation, and it has adversity my credit worthiness.
Pursuant to 15 USC 1681 ( a ) ( 3 ) Your company assumes this role. You were not given permission by me, the consumer, or a competent court of jurisdiction. Not only did you fail the initial investigation of this information, you also assumed this role, and assumed this information was valid. Which is a violation of my consumer rights.
Pursuant to 15 USC 1681 ( a ) ( 4 ) You did not insure the information is accurate and you did not exercise your grave responsibility with fairness and impartiality. You took one side of the story, never consulted with me to find out if this transaction even took place. As a result of this my privacy was breached, and violated my right to privacy.
( SEE EXHIBIT A ) Pursuant to 15 USC 1681 a ( d ) ( 2 ) ( a ) ( I ) Congress makes it clear that a consumer report should not include transactions between the consumer and the person making the report. The alleged transactions that you are reporting are false and have failed to investigate, weather or not it factually took place between the consumer, and the person making the report. Which means it should not be included in my consumer report and in fact is a violation of my consumer rights.
Pursuant to 15 USC 1681 a ( d ) ( 2 ) ( A ) In the initial investigation I was never given the opportunity to deny this information being communicated on my consumer report. This is in fact another violation.
Pursuant to 15 USC 1681 a ( B ) Any authorization of a specific extension of a credit or credit card or similar device should be excluded from my consumer report. This alleged transaction that your company clearly did not investigate is categorized as a transaction that was issued from a credit card. Credit as defined as 15 USC 1602 ( 1 ) Therefore I am requesting IMMEDIATE REMOVAL OF ALL ITEMS FROM MY CONSUMER RFEPORT ACCORDING TO EXHIBIT A BELOW XXXX XXXX XXXX Experian XXXX Account # : XXXX XXXX XXXX Account Type : Revolving Revolving Revolving Account Type - Detail : Credit Card Credit Card XXXX XXXX XXXX : Individual Individual Individual Account Status : Derogatory Derogatory Derogatory Monthly Payment : {$0.00} {$0.00} {$0.00} Date Opened : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Balance : {$3900.00} {$3900.00} {$3900.00} No. of Months ( terms ) : XXXX XXXX XXXX High Credit : {$3900.00} {$0.00} {$3900.00} Credit Limit : {$3000.00} {$3000.00} {$0.00} Past Due : {$3900.00} {$3900.00} {$3900.00} Payment Status : Collection/Chargeoff Collection/Chargeoff Collection/Chargeoff Last Reported : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Comments : Charged off as bad debt Canceled by credit grantor Account has been closed due to inactivity.
Unpaid balance reported as a loss by the credit grantor.
Charged off account Accounts closed by credit grantor Date Last Active : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Date of Last Payment : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXXXXXX XXXX XXXX XXXXXXXX Experian XXXX Account # : XXXX XXXX XXXX Account Type : Revolving Revolving Revolving Account Type - Detail : Flexible spending credit card Credit Card - XXXX XXXX XXXX XXXX Individual Individual Account Status : Derogatory Derogatory Derogatory Monthly Payment : {$0.00} {$0.00} {$0.00} Date Opened : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Balance : {$15000.00} {$15000.00} {$15000.00} No. of Months ( terms ) : XXXX XXXX XXXX High Credit : {$15000.00} {$0.00} {$15000.00} Credit Limit : {$13000.00} {$13000.00} {$0.00} Past Due : {$15000.00} {$15000.00} {$15000.00} Payment Status : Collection/Chargeoff Collection/Chargeoff Collection/Chargeoff Last Reported : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Comments : Charged off as bad debt Canceled by credit grantor Account has been closed due to inactivity.
Unpaid balance reported as a loss by the credit grantor.
Charged off account Accounts closed by credit grantor Date Last Active : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Date of Last Payment : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXXXXXX XXXX XXXX XXXXXXXX Experian XXXX Account # : XXXX XXXX XXXX Account Type : Revolving Revolving Revolving Account Type - Detail : Credit Card Credit Card XXXX XXXX XXXX : Individual Individual Individual Account Status : Derogatory Derogatory Derogatory Monthly Payment : {$0.00} {$0.00} {$0.00} Date Opened : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Balance : {$2100.00} {$2100.00} {$2100.00} No. of Months ( terms ) : XXXX XXXX XXXX High Credit : {$2100.00} {$0.00} {$2100.00} Credit Limit : {$1500.00} {$1500.00} {$0.00} Past Due : {$2100.00} {$2100.00} {$2100.00} Payment Status : Collection/Chargeoff Collection/Chargeoff Collection/Chargeoff Last Reported : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Comments : Charged off as bad debt Canceled by credit grantor Account has been closed due to inactivity.
Unpaid balance reported as a loss by the credit grantor.
Charged off account Accounts closed by credit grantor Date Last Active : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Date of Last Payment : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXXXXXX XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX Experian XXXX Account # : XXXX XXXX Account Type : Collection Collection Account Type - Detail : Collection Collection XXXX Code : Individual Individual Account Status : Derogatory Derogatory Monthly Payment : - - Date Opened : XX/XX/XXXX XX/XX/XXXX Balance : {$190.00} {$190.00} No. of Months ( terms ) : - - High Credit : {$190.00} {$190.00} Credit Limit : - - Past Due : - - Payment Status : Collection/Chargeoff Collection/Chargeoff Last Reported : XX/XX/XXXX XX/XX/XXXX Comments : Affected by natural disaster Affected by natural disaster.
Date Last Active : XX/XX/XXXX XX/XX/XXXX Date of Last Payment : - - XXXX XXXX ( Original Creditor : XXXXXXXX XXXX ) XXXX Experian XXXX Account # : XXXX XXXX XXXX Account Type : Collection Collection Collection Account Type - Detail : Collection Collection Collection Bureau Code : Individual Individual Individual Account Status : Derogatory Derogatory Derogatory Monthly Payment : - - - Date Opened : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Balance : {$590.00} {$590.00} {$590.00} No. of Months ( terms ) : - - - High Credit : {$550.00} {$550.00} {$550.00} Credit Limit : - - - Past Due : - - - Payment Status : Collection/Chargeoff Collection/Chargeoff Collection/Chargeoff Last Reported : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Comments : Placed for collection - Medical Subject has not satisfied debt.
Date Last Active : XX/XX/XXXX XX/XX/XXXX XX/XX/XXXX Date of Last Payment : -"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Please see attached documents"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Public record information inaccurate"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "1.I have never had an account with this company CCB/ALPCOS XXXX XXXX REMOVE from all credit agencies from reporting.
2.consumer means any natural person obligated or allegedly obligated to pay any debt. According to 15 U.S. Code 1692a ( 3 ) 3.This letter is in pursuing 15 U.S. Code 1692c ( c ) Ceasing communication 4.I am the consumer mentioned in 15 U.S. code 1692a ( 3 ) You are COMENITY CAPITAL BANK the debt collector 15 U.S. Code1692a ( 5 ) I am invoking specified remedies under use 15 U.S. code 1692c ( c ) ( 2 ) as a creditor I am demanding you to terminate all farther efforts pursing to 15 U.S. Code 1692c ( c ) ( 1 ) 5.I am the executor under the 15 U.S. Code 1692c ( d ) pursing to 15 U.S. Code 1692d A debt collector may not engage in any conduct the natural consequence of which is to harass, oppress, or abuse any person in connection with the collection of a debt. Without limiting the general application of the foregoing, the following conduct is a violation of this section : ( 1 ) ( 2 ) and ( 4 ) 6.According to 15 U.S. Code 1962e ( 8 ) Communicating or threatening to communicate to any person credit information which is known, or which should be known to be false, including the failure to communicate that a disputed debt is disputed.
7.15 U.S. Code 1692e ( 12 ) The false representation or implication that accounts have been turned over to innocent purchasers for value.
8.As mentioned in the 15 U.S. Code 692f Unfair practices ( 8 ) 9.While pursuing to 1692g ( b ) while the debt collector shall cease collection of the debt, or any disputed portion thereof, until the debt collector obtains verification of the debt.
10.15 U.S. Code 1692k Civil Liability ( a ) Amount of damages Except as otherwise provided by this section, any debt collector who fails to comply with any provision of this subchapter with respect to any person is liable to such person in an amount equal to the sum of ( 1 ) ( 2 ) ( A ) ( B ) ( 3 ) 11. Mentioned in 15 U.S. Code 1681a ( 3 ) ( 3 ) Restriction on sharing of medical information. Except for information or any communication of information disclosed as provided in section 1681b ( g ) ( 3 ) of this title, the exclusions in paragraph ( 2 ) shall not apply with respect to information disclosed to any person related by common ownership or affiliated by corporate control, if the information is ( A ) ( B ) ( C ) Ive pointed out many violations your company is in pertaining to the U.S. Code 1692 and 1681."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I received an e-mail from XXXX, the servicer, on XXXX/XXXX/XXXX. I logged into my account to read the message and observed that the site listed my current payment as past due. I contacted XXXX chat support the same day and inquired as to why my payment did not post on time. I explained that I have been subscribed for automatic debits for well over a year and I have not made any changes to my accounts.
The representative, XXXX, explained that it was not my fault or their fault. He explained that " Your due date was on the weekend, and payments are only process on business days ( Monday-Friday ). It takes 2-3 full business days to process auto debit payments. Your payment will be effective on XXXX/XXXX/XXXX, and should be visible on your account by XXXX/XXXX/XXXX. No late fees, or negative credit reporting will occur because of this issue. '' I inquired as to whether the company would continue to list this as a past due payment on my account. The representative said " Your payment will appear to be 3 days late, but that will not be reported to your credit, and is a non-issue once the payment is processed. '' Unfortunately, the claim that this is a non-issue is false. Records of my payment history can be required for future financial applications. Potential loan refinance offers may be contingent on perfect on-time payment history. There are other circumstances where it is plausible that this " late payment '' can cause me harm. Yet, the company claims there is no mechanism to reverse the status on my account to reflect their responsibility for this situation. The representative explained that, if I wish to prevent this in the future, I need to end my auto debit plan and make manual payments.
I contend that, if this company offers an auto debit service through their site, they have a legal responsibility to conduct their debits in a way that does not occasionally but permanently tarnish my payment history."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Trouble with how payments are being handled"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "SPS is trying to dictate who I have as an authorized 3rd party on my file. SPS can not dictate who I choose to have handle my financial matters, this is not up to SPS this is up to the borrower. SPS must immediately CEASE and DESIST all demands as to who they say I have to authorize for my short sale request. If I do not wish my listing agent to be an authorized 3rd party then they will not be an authorized 3rd party."
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "this week while checking my email I received an update on my credit report upon viewing I notice a hand full of inquiries that were not made by me. I tried disputing and received no response this is a violation of my rights as a consumer. I'm currently stationed at XXXX XXXX with the US XXXX I've been here since XX/XX/2022 there no way these inquiries were made be me.
XXXX XXXX XXXX XXXX Experian XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Of XXXX"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Legal Department, This credit dispute is being submitted as a new evidence dispute. The accounts that are being disputed, does not belong to me. The below is my sworn statement concerning these credit issues.
It is my sworn statement that I declare under penalty of perjury ( " under the laws of the United States " if executed outside of the United States '' ) that the foregoing is true and correct to the best of my knowledge. Further, I certify that I am qualified and authorized to file this dispute. I also understand that knowing and a willful misstatement or omissions of material facts constitutes a federal criminal violation punishable under 18 U.S.C. 1001. Additionally, these misstatements are punishable l1S perjury under 18 U.S.C. 1621.
The accounts listed below are items currently within my credit file, and none of these are related to any transaction that was made by me. It is my official statement that someone and/or several people have opened these accounts and made transactions on these accounts without my knowledge or authorization, my personal information has been used to obtain goods, services, or money : and the person ( s ) responsible for creating these accounts.
I have filed a report with the Federal Trade Commission, which I have enclosed within this dispute package. I have reached out to all of these creditors and have made them aware that these accounts were fraudulently opened, and they have known this for several months, but have yet to remove these accounts from my credit reports. Some of the creditors have been cooperative and have removed the accounts, but others have refused to remove the fraudulent accounts. Therefore, I have come to you, the credit reporting agencies to resolve these issues. The law says that you must block these accounts while being investigated, and I expect that you will do so.
I made an attempt to file a police report, but I was informed by my county that it did not constitute as an emergency and that they would not file the report because I didn't have any information on the person who used my information, nor did I know how my information was obtained by this person.
Likewise, these COLLECTION ACCOUNTS are also not mine. None of these belong to me. This means that I was not the person, which had an account with the original creditor, which has sold these accounts to these collection agencies that are now reporting the debts.
COLLECTION AGENCY : Bankruptcy ACCOUNT NUMBER : XXXX COLLECTION AGENCY : XXXX XXXX ACCOUNT NUMBER : XXXX In addition, it has also been discovered that these STUDENT LOAN ACCOUNTS were also being listed within my credit file. The fact is that these Student Loan Accounts are not mine. I did not open these accounts and did not even seek a high education or attend college. So these accounts do not belong to me.
STUDENT LOAN CREDITORS : XXXX ACCOUNT NUMBER : XXXX Policy states ...
According to the FAIR CREDIT REPORTING ACT " FCRA '' 611 ( 15 U.S.C. 1681 ), Procedure In Case of Disputed Accuracy ...
( a ) Reinvestigations of Disputed Information ( 1 ) Reinvestigation Required ( a ) In general. Subject to Subsection <t,, if the completeness or accuracy of any item of information contained in a consumer 's file at a consumer agency is disputed by the consumer and the consumer notifies the agency directly, or indirectly through a reseller, of such dispute, free of charge, conduct a reasonable reinvestigation to determine whether the disputed information is inaccurate and record the current status of the disputed information, or delete the item from the file in accordance with paragraph ( 5 ), before the end of the 30day period beginning on the date on which the agency receives the notice of the dispute from the consumer or reseller.
Therefore, my position is being clearly stated, these accounts do not belong to me, meaning that you are reporting inaccurate account information within my credit file. The fact that these are inaccurate account information means that you, this credit reporting agency, are reporting incorrect accounts.
Finally, you are reporting the INQUIRIES that are the result of the same type of fraudulent activities. It must be noted that I did not apply for credit accounts with the below-listed companies and creditors, some of these are the result of attempts to open accounts, but the companies did not approve these accounts. Therefore, these inquiries must be deleted from within my credit file. You can not delete the fraudulently opened accounts without also deleting the fraudulent inquiries.
Inquiries that are made by someone else without my authorization is not a factual record of access to my credit file, it is a factual record of fraudulent activity, and it must be addressed as such. The reason that these inquiries are being disputed is because they were not made by me, and when I report this to you, it is your responsibility to investigate my claims, and when you can not prove that these inquiries were made by me, they must be removed from my credit file. It is not my responsibility to go directly to each creditor, the obligation lies at your feet because you are reporting inaccurate information within my credit report, and this violates the FCRA.
It should be noted that an inquiry that was not made by me is not a factual record, just as a fraudulently opened account is not a factual record and not referred to the creditor to be disputed directly with them. The credit reporting agencies have attempted to address an inquiry as a factual record of file access, but if it was done through fraudulent means, it is not a factual record. As a consumer, I should not be required to dispute each fraudulent inquiry directly with each company. These inquiries are the result of fraudulent activity and therefore inaccurate information in which the credit reporting agencies or the creditors can not verify, meaning that these inquiries are unverifiable information and must be deleted from out of my credit file.
INQUIRIES : XXXX | XX/XX/XXXX XXXX | XX/XX/XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX | XX/XX/XXXX XXXX | XX/XX/XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX I am well aware of my rights as a consumer. The Fair Credit Reporting Act requires that you, the credit-reporting agency, report accurate and/or correct account information within my credit file. My credit issues are very specific, these accounts and/or items do not belong to me. This means that you are reporting incorrect account information within my credit report. It is with this in mind that I request that these listed accounts and items be BLOCKED and DELETED!
Policy states ...
According to the FAIR CREDIT REPORTING ACT " FCRA '', Section 1681c ( 2 ) Block of information resulting from identity theft.
1. Block. Except as otherwise provided in this section, a consumer reporting agency shall block the reporting of any information in the file of a consumer that the consumer identifies as information that resulted from an alleged identity theft, not later than four ( 4 ) business days after the date of receipt by such agency of ...
1. Appropriate proof of identity of the consumer ; 2. A copy of an identity theft report 3. The identification of such information by the consumer ; and A statement by the consumer that the information relating to the transaction by the consumer.
All furnishers of information to consumer reporting agencies must comply with all applicable regulations. Information about applicable regulations currently in effect can be found at the Consumer Financial Protection Bureau 's website, www.cons umerfinance.gov/learnmore.
NOTICE TO FURNISHERS OF INFORMATION : OBLIGATIONS OF FURNISHERS UNDER THE FCRA The federal Fair Credit Reporting Act ( FCRA ), 16 U.S.C 1681168l y, imposes responsibilities on all persons who furnish information to consumer reporting agencies ( CRAs ). These responsibilities are found in Section 623 of the FCRA, 16 U.S.C 168ls2. State law may impose additional requirements on furnishers. All furnishers of information to CRAs should become familiar with the applicable laws and may want to consult with their counsel to ensure that they are in compliance. The text of the FCRA is available at the website of the Consumer Financial Protection Bureau ( CFPB ) : www.consumerfinanoe.gov/learnmore. A list of tile sections of tile FCRA cross-referenced to the U.S Code is at the end of this document.
Section 623 imposes the following duties upon furnishers : Accuracy Guidelines The FCRA requires furnishers to comply with federal guidelines and regulations dealing with the accuracy of information provided to CRAs by furnishers. Federal regulations and guidelines are available at www.consumerfinance.gov/learnmore. Section 623 ( e ).
General Prohibition on Reporting Inaccurate Information The FCRA prohibits information furnishers from providing information to a CRA that they know or have reasonable cause to believe is inaccurate. However, the furnisher is not subject to this general prohibition if it clearly and conspicuously specifies an address to which consumers may write to notify the furnisher that certain information is inaccurate. Sections 623 ( a ) ( l ) ( A ) and ( a ) ( l ) ( C ).
Duty to Correct and Update Information If at any time a person who regularly and in the ordinary course of business furnish.es information to one or more CRAs determines that the information provided is not complete or accurate, the furnisher must promptly provide complete and accurate information to the CRA. In addition, the furnisher must notify all CRAs that received the information of any corrections and must thereafter report only the complete and accurate information. Section 623 ( a ) ( 2 ).
Duties After Notice of Dispute from Consumer If a consumer notifies a furnisher, at an address specified for the furnisher for such notices, that specific information is inaccurate, and the information is, in fact, inaccurate, the furnisher must thereafter report the correct information to CRAs. Section 623 ( a ) ( l ) ( B ).
If a consumer notifies a furnisher that the consumer disputes the completeness or accuracy of any information reported by the furnisher, the furnisher may not subsequently report that information to a CRA without providing notice of the dispute. Section 623 ( a ) ( 3 ).
Furnishers must comply with federal regulations that identify when an information furnisher must investigate a dispute made directly to the furnisher by a consumer. Under this regulation, s furnishers mu.et complete an investigation within 30 days ( or 45 days, if the consumer later provides relevant additional information ) unless the dispute is frivolous or irrelevant or comes from a credit repair organization. Section 62S ( a ) ( 8 ). Federal regulations are available at www.consumerfinance.gov/learnmore. Section 623 ( a ) ( 8 ) Duties After Notice of Dispute from Consumer Reporting Agency If a CRA notifies a furnisher that a consumer disputes the completeness or accuracy of information provided by the furnisher, the furnisher has a duty to follow certain procedures. The furnisher must : Conduct an investigation and review all relevant information provided by the CRA, including information given to the CRA by the consumer. Sections 623 ( b ) ( l ) ( A ) and ( b ) ( 1 ) ( B ).
Report the results to the CRA that referred the dispute and, if the investigation establishes that the information was in fact, incomplete or inaccurate, report the results to all CRAs to which the furnisher provided the information that compile and maintain files on a nationwide basis. Section 623 ( b ) ( l ) ( C ) and ( b ) ( l ) ( D ).
Complete the above steps within 30 days from the date the CRA receives the dispute ( or 45 days, if the consumer later provides relevant additional information to the CRA ). Section 623 ( b ) ( 2 ).
Promptly modify or delete the information or block its reporting. Section 623 ( b ) ( l ) ( E ), Duty to Report Voluntary Closing of Credit Accounts If a consumer voluntarily closes a credit account, any person who regularly and in the ordinary course of business furnished information to one or more CRA.s must report this fact when .it provides information to CRA.s for the time period in which the account was closed. Section 623 ( a ) ( 4 ).
Duty to Report Dates of Delinquencies If a furnisher reports information concerning a delinquent account placed for collection, charged to profit or loss, or subject to any similar action, the furnisher must, within 90 days after reporting the information, provide the CRA with the month and the year of the commencement of the delinquency that immediately preceded the action, so that the agency will know how long to keep the information in the consumer 's file. Section 623 ( a ) ( 5 ).
Any person, such as a debt collector, that has acquired or is responsible for collecting delinquent accounts and that reports information to CRA.s may comply with the requirements of Section 623 ( a ) ( 5 ) ( until there is a consumer dispute ) by reporting the slime delinquency date previously reported by the creditor. H the creditor did not report this date, they may comply with the FCRA by establishing reasonable procedures to obtain and report delinquency dates, or, if a delinquency date can not be reasonably obtained, by following reasonable procedures to ensure that the data reported precedes the date when the account was placed for collection charged to profit or loss, or subjected to any similar action. Section 623 ( a ) ( 5 ).
Duties of Financial Institutions When Reporting Negative Information Financial institutions that furnish information to " nationwide '' consumer reporting agencies, as defined in Section 603 ( p ), must notify consumers in writing if they may furnish or have furnished, negative information to a CRA. Section 623 ( a ) ( 7 ). The CFPB has prescribed model disclosures, 12 CFR Part 1022, App. B.
Duties When Furnishing Medical Information A furnisher whose primary business is providing medical services, products, or devices ( and such furnisher 's agents or assignees ) is a medical information furnisher for the purposes of the FCRA and must notify all CRAs to which it reports of this fact. Section 623 ( a ) ( 9 ). This notice will enable CRAs to comply with their duties under Section 604 ( g ) when reporting medical information.
Duties When ID Theft Occurs All furnishers must have in place reasonable procedures to respond to notifications from CRAs that information furnished is the result of identity theft and to prevent refurnishing the information in the future. A furnisher may not furnish information that a consumer has identified as resulting from identity theft unless the furnisher subsequently knows or is informed by the consumer that the information is correct. Section 623 ( a ) ( 6 ). If a furnisher learns that it has furnished inaccurate information due to identity theft, it must notify each CRA of the correct information and must thereafter report only complete and accurate information. Section 623 ( a ) ( 2 ). When any furnisher of information is notified pursuant to the procedures set forth in Section 605B that a debt has resulted from identity theft, the furnisher may not sell, transfer, or place for collection the debt except in certain limited circumstances. Section 61500.
The CFPB 's website, www.consumerfinance.gov/learnmore, has more information about the FCRA, including publications for business and the full text of the FCRA.
Citations for FCRA sections in the U.S. Code, 15 U.S.C. 1681et seq. : I have provided all of the above-listed items within this package. Therefore, I expect these listed accounts to be deleted and blocked within four ( 4 ) business days. This is clearly an identity theft issue. I am also entitled to receive a free copy of my credit report, so I am requesting a free credit report and that all of these accounts, public records items, and inquiries be permanently blocked and deleted."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I Have sent a letter and called CASHNET USA to Remove FALSE Inaccurate Credit Information that is being reported on my credit reports from XXXX XXXX XXXX : Alleged acct number XXXX To whom it may concern : I have received a notice from XXXX XXXX XXXX for debt owed in the amount of {$3100.00}, which I am declaring completely fraudulent. I obtained a copy of my credit report and after reviewing the information, I noted that inaccuracies have been placed again on my credit report regarding CASHNET USA. I have NEVER had an account or heard of CASH NET USA. This information has been placed on my credit report now for the second time after initially being corrected. How was this verified and approved?, Where are my signatures, where was the money deposited or a check written to?
Here are the factual items in which I am disputing : 1. This Debt is NOT and NEVER has been associated with me 2. I am reporting this a FRAUD 3.Re-insertion of fraudulent ; incorrect information, AFTER it was corrected when it was sent to a previous debt collector.
I formally wrote notice to XXXX to correct my credit report and now I have to send the XXXX and XXXX. Please note that this inaccuracy has been reported twice.
Following the conclusion of your investigation, please send me the results of the corrections via MAIL.
Thank you, XXXX XXXX"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I have earned rewards money ( in XXXX ) using my XXXX Citibank Visa credit card and I am unable to redeem it because XXXX employees are telling me ( on XX/XX/XXXX ) there is an error code. Citibank 's website is also giving me an odd error message when I try getting the rewards reissued."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Problem with rewards from credit card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Payday loan, title loan, or personal loan",
"Sub-product": "Payday loan",
"Consumer complaint narrative": "XXXX was taking money from my account on Paydays and they even overdrew my account to the point where my XXXX XXXX account was closed due to the excessive fees I was being charged. Today I owe XXXX XXXX over {$600.00} because of XXXX. And yet and still they have a collection account on my credit report for {$1300.00}. I never received this amount from the company. I have paid back the original amount I received but due to the excessive interest and other fees they claim I still owe them money. I do not believe this to be true. I have disputed it with the XXXX credit bureaus and it was deleted from XXXX but updated on XXXX and XXXX. This is preventing me from qualifying for a mortgage loan.The collection agency acting on behalf of XXXX is Ad Astra."
}
Output:
{
"Issue": "Charged fees or interest you didn't expect",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional fixed mortgage",
"Consumer complaint narrative": "Applied for a loan 5 months ago, home purchase with a construction loan to make improvements. NOT an fha loan. Underwriter demanded a HUD inspection ( {$750.00} ) then refused to accept his findings and demanded an Engineer Inspection ( {$700.00} ). Underwriter has delayed beyond reasonable time standards, even refusing appraisal and sending it back three time. Last time for a non-essential typo. Now four days before closing the closing costs are {$3400.00} higher than original estimate. All due to the excessive delay by underwriter."
}
Output:
{
"Issue": "Application, originator, mortgage broker",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "equifax has recently deleted positive accounts on my credit report without my permission.
please reinstate all positive accounts and remove all negative accounts"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information is missing that should be on the report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "Loan Assumption closing on XXXX XXXX , XXXX This has been a horrific process! Original Loan f rom XXXX and XXXX XXXX XXXX AFR/ XXXX . XXXX XXXX XXXX XXXX , In XXXX was assumed by XXXX XXXX XXXX on XXXX XXXX , XXXX File # XXXX through XXXX , XXXX . We requested an assumption package from AFR per their request and was completed per their request in XXXX this process took approximate 5 months with numerous paperwork requests from both parties. I have contacted XXXX and left numerous messages with no return calls. I have been receiving bills still on this home from AFR mortgage XXXX when XXXX XXXX XXXX has received bills from his assumed loan from AFR and is making payments, but yet I am still receiving bills. When I called AFR I was told they needed a copy of the Recorded agreement, XXXX , and XXXX . This should have all been taken care of through their own company/title company and subcontract company. I spoke with a supervisor XXXX who stated he was unable to assist me through customer service. Please Help! We closed on XXXX XXXX , XXXX and this is still an issue. XXXX XXXX XXXX or XXXX XXXX XXXX I have sent all the information that was given to me at closing, XXXX has all the copies because we closed at the house and he was the buyer/assumee."
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Ive been cosistent with paying my bills on time. Im saying this because I went to XXXX XXXX the other day and applied for an account. I got approved but with a {$400.00} deposit. I didnt understand until I checked my credit report. I see a couple of items that are not mine. These items need to be deleted from my credit report."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "TODAYS DATE:XX/XX/XXXX XXXX, XXXX XXXX SOC SEC # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX XXXX, XXXX XXXX, FL XXXX XXXX XXXX. XXXX XXXX XXXX XXXX, GA XXXX XXXX Dispute Department XXXX. XXXX XXXX XXXX, Texas XXXX TransUnion Consumer Solutions, XXXX. XXXX XXXX XXXX, PA XXXX XXXX.
I recently submitted a request for investigation of an Acct Number : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Ive submitted enough information regarding the fraudulent account thats open in my name I have provided information for your company to have carried out a reasonable investigation of this dispute. If you had investigated properly rather then using your XXXX system you would have noticed that this account is not mine which your companies have claimed to Verified. Since youve obviously neglected to investigate this account thoroughly I am demanding you remove this account off my profile. It is at this time that I will point out the in XXXX v TransUnion, XXXX v. XXXX ( XXXX ), and XXXX v. XXXXXXXX XXXX XXXX, the courts ruled each and every time that the CRA couldnt merely Parrot information from the creditors and collection agencies that they have conduct an independent REASONABLE investigation to ensure the validity of the debt and honesty/integrity of the creditors/CA in question. Sending out a generic form through the XXXX system that doesnt even contain my reasons for the dispute is not reasonable.
If you dont initiate an investigation regarding my dispute, as it is my right under the Fair Credit Reporting Act, I will have to take legal action to protect my credit rating and myself. Which Im sure you are aware each violation of the Fair Credit Reporting Act allows damages of {$1000.00} should this matter ends up in court.
I look forward to an expedite resolution of this matter Thank you.
XXXX, XXXX XXXX SOC SEC # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX TER, XXXX XXXX, FL XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "This is XXXX XXXX XXXX, who is submitting this CFPB complaint myself, and to inform you that there is no third party involved in the process. Upon reviewing my updates, incorrect information was found in my reports. The Equifax Credit Bureau has not complied with the Fair Credit Reporting Act, 15 USC Sections 1681i, and continued reporting as unverified information without any proof provided, within the time allowed by law, is not authorized. Below are the inquiries that are reporting on my Credit Reports : XXXX of inquiry XX/XX/XXXX XXXX XXXX-Date of inquiry XX/XX/XXXX XXXX-Date of inquiry XX/XX/XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I received a phone call from one of my past colleges ( XXXX XXXX XXXX College ) notifying me that I had loans that were close to defaulting. I had never elected loans and had not been aware of any student loans in my name. I got in contact with the loan provider and found out that there were 12 loans in my name totaling nearly {$30000.00}. I made two payments soon after the initial phone call totaling {$2000.00}. After seeking advice, I mailed the Great Lakes two letters requesting copies of the loan document ( s ) and proof that I had taken the loan and requested that my credit score be restored due to being unaware of the loans. The letters were dated XX/XX/XXXX.
On XX/XX/XXXX, Great Lakes provided a copy of the loan document. The loan document bore a digital signature, providing no proof of the identity of the person who took the loan. The document listed my name, but was missing all other personal information ( birthdate, social security number, and driver 's license number ). Furthermore, the document did not suggest that I would have ever received any communication regarding the loan as the document listed a mailing address, email address, and phone number that were not my own.
Legal counsel was sought and I am mailing an additional letter to Great Lakes in response to the loan document provided. I am requesting a response from Great Lakes within 30 days of receipt."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Received bad information about your loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose prepaid card",
"Consumer complaint narrative": "On XXXX / XXXX / XXXX I looked up the balance to m y American Express Gift Card, and the record showed that it had been used fraudulently. I received this card months ago as a holiday gift, and it had not left my wallet. The charge that I disputed was made at a XXXX XXXX at XXXX XXXX XXXX XXXX in XXXX XXXX , CA with a transaction date of XXXX / XXXX / XXXX and a post date of XXXX / XXXX / XXXX . The disputed amount was {$21.00}. I contacte d American Express customer services, gift card division, and they said that my o nly recourse was to contact the merchant directly. The merchant could do nothing to help me."
}
Output:
{
"Issue": "Unexpected or other fees",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "Since XX/XX/XXXX I have been asking for the legal documents that established the terms and conditions of my mortgages where I appear as co-participant. I did receive one of them but the other was never sent it to me. Instead they send me an statement letter without any customer friendly information. I have called again in XX/XX/XXXX and requested the documents again. They stated at that time that the request was wrong because I used the term buying instead of refinancing documents. At that point they reported that they will send the documents on XX/XX/XXXX. Today, XX/XX/XXXX I called them again since I have not received the documents requested. This time they stated that they do not have the documents and that I will have to call the notary who notarized the documents in XX/XX/XXXXin order to request it from his office. It should be noted that this mortgage went to a modification process that I did not participated. However, I continued to be responsible for the payments but they have failed to provide the statement that I have signed and that makes me responsible in the credit report. If it is possible, I would like to speak to an Spanish speaking agent. Thank you."
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "Our home was destroyed by XXXX XXXX. We finally got policy limits from insurance, and the check was made payable to my wife and me and our mortgage company, PNC Bank. It was for {$110000.00} more than what we owed on the mortgage. PNC Bank has used the money to pay off the mortgage as we requested, However, they insist that their " policy '' is to take at least 20 days to send us the balance we are owed. So, they are profiting from our loss by getting interest on our money. Our home needs to be torn down, as it is dangerous. But, we need that money to tear it down and begin the rebuilding process.
PNC Bank should issue our check immediately and pay us back interest on the money for the time they held it to comply with their own self serving " policy ''.
How many other people do they take advantage of in this way?"
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "When I investigated my credit report, I realized that some of the information was erroneous. The XXXX credit bureau is required to authenticate this account under Sections 609 ( a ) ( 1 ) ( A ) and 611 ( a ) ( 1 ). ( A ). This is not a duplicate nor is this complaint being filed by a third party, I am filing this complaint myself. Please see this complaint is processed to the letter of the law.
It is not acceptable to treat this reporting account as unconfirmed information without producing proof within the legal time range. Sections 609 ( a ) ( 1 ) ( A ) and 611 ( a ) ( 1 ). ( A ), please investigate the unverifiable account/items information below : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX XXXX XXXXXXXX XXXX XXXXXXXX XXXX XXXXXXXX XXXX XXXXXXXX XXXX XXXX XXXXXXXXXXXX XXXX XXXXXXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX XXXX XXXXXXXX, XXXX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Consumer Loan",
"Sub-product": "Vehicle loan",
"Consumer complaint narrative": "I have recently paid this account off yet i still see late payments on this account. initial issue was in XX/XX/XXXX, when making my payment a suntrust representative asked if i wanted to skip a payment and put to end of term. I agreed because i trusted was as innocent as he portrayed. i went on vocation and came back to realize that not only did this guy not push payment to end of term but my car got repossessed. XXXX assured me and apologized for inconvenience and allowed me to get car back with payment all fees. Advised that they will be able to push payment out to end of term and be able to reduce payments to XXXX at no time was this explained that this would be considered a " Modified loan ''. I was tricked into believing i was refinancing. few years later when i was declined for many banks to get a new car loan after my son was born i realized that this account had XXXX late and a repo that was not suppose to show. I called suntrust to also find out that the so called modification they had was for a different vehicle. when i mentioned this to legal they immediately tried to coerce me into signing another modification with 07 nissan 350z because in exchange to remove all late fees on the account. keeping from me important information like GAP coverage may be forfeited, higher overall paid for loan and potential negative aspect on credit report. My vehicle was later involved in a accident and vehicle was paid off. At this time no Late payment were suppose to show on this account and loan modification was suppose to be taken off since they had wrong loan modification paperwork signed on my account and i was not aware i even had a loan modification. I was even told that if i refused to sign new loan modification that they would make me pay the account current by retro modifications back at its so called inception. i called yesterday XXXX and spoke with a lovely representative whom could n't even pull up my information yet suntrust continues to verify this account with credit reporting companies as " Valid ''. When i asked her if she could send information pertaining to account and modification that was on wrong vehicle she said they had nothing. So how can they continue to report information that they ca n't even verify?"
}
Output:
{
"Issue": "Managing the loan or lease",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "XXXX XXXX XXXX XXXX XXXX Thank you Consumer Finance.gov for addressing my concerns with CITIBANK On XX/XX/XXXX I XXXX XXXX XXXX XXXX was informed by Citibank via an upload on consumer finance that : " On XX/XX/XXXX, we will debit your account for the {$89.00} provisional credit we issued while we investigated your dispute. For the next five business days, we will continue paying checks and pre-authorized debits on your account up to the amount of the provisional credit '' Please note that my Citibank Account ending in # XXXX was never credited the incidental amount from the XXXX. Thus, Citibank completed the investigation without issuing the reimbursement from the XXXX incidental in the amount of {$89.00}.
On the other hand, the issue with the scam or false impersonation of the XXXX XXXX and XXXX continues, these con-artists were reported to the Federal Bureau of Investigation in XXXX, when they hacked into XXXXXXXX XXXX. XXXX and XXXX XXXX claim to work for Citibank from XXXX, XXXX. XXXX XXXX and XXXX request personal and card number, social security, maiden name, security code on card to 'answer or provide information ' to the consumer.
I contacted CITIBANK on XXXX via private phone number XXXX Please address and review the following information : XXXX PM XXXX XXXX On call with CITIBANK, placed on hold -- XXXXvia private line XXXX XXXX XXXX picked up the line for CITIBANK stating she was a XXXX XXXX ! From XXXX ( XXXX as in XXXX XXXX as in XXXX XXXX as in XXXX XXXX XXXX XXXX and XXXX as in XXXX ) from the XXXX.
I XXXX XXXX XXXX XXXX 1 stated : " XXXX XXXX is on the line! '' XXXX XXXX stated " Correct! '' Screaming could be heard stating " XXXX XXXX is not CORRECT! Burning CITIBANK! '' XXXX XXXX XXXX picked up the phone line for CITIBANK again falsely impersonating a worker.
XXXX PM A XXXX XXXX ( false impersonation ) provided the SAME ID as XXXX XXXX impersonating a XXXX XXXX.
XXXX USA False Impersonation : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, XXXX XXXX stated that their supervisors in the XXXX were ( XXXX, XXXX, XXXX, XXXX, XXXX XXXX XXXX was impersonating XXXX.
One could hear XXXX XXXX in the back of the CALL, with another CITIBANK customer arguing with her telling her to get off her phone.
I XXXX XXXX XXXX 1 explained to CITIBANK representative that XXXX XXXX is NOT authorized to work in the United States ; XXXX impersonating XXXX XXXX -- - CITIBANK ID provided : XXXX They claim to be in XXXX, XXXX ; XXXX PM XXXX XXXX XXXX XXXX interrupted my private phone line XXXX once again to state that " To Russians -- -I am the XXXX! '' Please note that XXXX XXXX IS a Reported STALKER-NOT RELATED/CRIMINAL ACTIVITY -- - XXXX XXXX XXXX 1 XXXX XXXX XXXX XXXX XXXX do expect for these false impersonators to be apprehended.
XXXX"
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Banking errors"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I had a XXXX XXXX Costco card which I had set up on automatic bill pay and rarely used. Recently, I noticed my credit score had dropped significantly and saw that it was for a Citi account which I had never opened. I contacted Citi asking what the account was connected to and they told me that my XXXX XXXX account was transferred to them and my auto pay did not transfer creating multiple late payments and severely damaging my credit score. I had recently moved so Citi may not have properly forwarded the information to me but when speaking with them they said the would not remove the late payments from my credit report because it was not the banks error. I am extremely dismayed that a company is able to have such business practices and additionally angry at Costco for letting this happen to their customers."
}
Output:
{
"Issue": "Billing disputes",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I am a new Macy 's card holder. I made XXXX purchase for {$92.00}. My husband made XXXX payment. The balance was {$58.00}. I am diligent about our credit, we are saving for a new home and want to keep me credit score strong, so I paid the balance online on XXXX XXXX (? ), early.
I received a phone call from Macy 's yesterday, saying my account payment did not go through and now the payment is late, late charges, interest and returned check fees adding to over {$200.00}.
I was devastated! They did refund the late fee and interest fee but would not remove the returned check fee because it was an error on my part. Clearly, I was trying to maintain my account by paying early. Why could n't they call me sooner?
I paid the balance of {$58.00} and {$130.00} in returned fees over the phone and vow to never shop at Macy 's again!
This is a disgrace!
XXXX XXXX"
}
Output:
{
"Issue": "Payoff process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "This account was originally placed on my credit report for equipment they said I didn't return, which was actually returned. I disputed it and it was removed. It was later added by another company, I disputed it and it was removed again. Now it is showing up under another company 's name. This is utterly ridiculous!"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was paid"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "XXXX XXXX XXXX , We have had a mortgage with Astoria Bank since XXXX . It is on a condo in XXXX FL. The community is located in a flood zone. For the pa st 7-8 years ( maybe more ) our association has provided a fax to confirm the flood insurance is in place. During this time Astoria continuously discards the policy ( some black hole ) and then proceeds to harass for weeks on end about obtain ing proof of insurance. My wife and I take it upon ourselves, each year, to fa x ( to a generic fax ) the policy over. This too becomes lost at times and they wont provide email addresses for us to scan so we can confirm receipt. Sometimes this process takes weeks and then we receive a " forced '' flood policy of {$1900.00} and they then want an increased payment. I spoke today to XXXX ( today ) regarding the increased payment and she wanted to scurrying me along to the insurance department, which is wrong so I asked for a manager. XXXX XXXX , claims to be a supervisor, told me I should have just asked for an escrow analysis, my response was " that was XXXX 's job ''. In closing this is an ongoing and chronic situation where a mortgage bank is failing to provide the necessary service and bullying their clients."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Consumer Loan",
"Sub-product": "Vehicle loan",
"Consumer complaint narrative": "In XX/XX/2016 I took out an auto loan with Peoples Financial Company at XXXX XXXX XXXX XXXX, XXXX Georgia, XXXX. I was told by the loan officer XXXX XXXX that they did not have an online payment system set up. I could email him each month with a credit card authorization form and he would credit my account. The due date was the XXXX of each month. I did this in XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX and XX/XX/XXXX. In XX/XX/XXXX the email was returned saying that XXXX XXXX email account with Peoples Finance was no longer valid. So I went and paid it in person for the XX/XX/XXXX and XX/XX/XXXX payment. In XX/XX/XXXX I went to their location to pay the loan off early thinking I had 5 payments left and a loan officer said I missed the XX/XX/XXXX payment and now had XXXX late fees amounting to XXXX. I said that I had not missed any payments or had been late. They said that the loan officer XXXX XXXX no longer worked there and they were not responsible for emails sent to him. I said that I was doing what their employee told me to do but they said they would not remove the late fees because he did not work there any longer. I tried to talk to the branch manager and he will not return my calls. Ive talked to 2 other people in that office and they tell me that it is my fault that I sent an email to their employee who no longer worked there and therefore missed the XX/XX/XXXX payment. I want my late fees reversed and do not want my credit report to show me as being late on this account."
}
Output:
{
"Issue": "Managing the loan or lease",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "My issues with B of A began in XX/XX/XXXX, when I changed my XXXX personal checking account into a joint checking account with my soon to be wife. When I did this, I wasn't made aware that my account number would be changing.
Following our creation of the joint checking account, I attempted to set up an auto-pay in XX/XX/XXXX that unbeknownst to me, failed to go through. Bank of America never communicated this so I didn't think about it again for quite a while.
Since I had set up auto pay and wasn't informed that there was anything wrong, I didn't bother looking at my account again until XX/XX/XXXX, after my wedding in XX/XX/XXXX. It was then that I discovered that My account was suspended and I was prevented from submitting payment over the phone through the automated system or online. Once I was able to reach a representative, I found out that my account had been suspended due to failure to collect payment. I explained to the representative over the phone that I had sufficient balance in the payment account ( Attached ) and that it must have been a mistake. The representative informed me that all he could see was that the payment didn't go through and nothing further. With the representatives ' help, I submitted a payment over the phone for XXXX on XX/XX/XXXX using the same account that B of A was claiming had been closed. After submitting payment, the phone representative told me that my account should now be unlocked once the payment is posted and that I should check in a few days online to make sure everything was going alright, but that an agent would follow up with a phone call just in case.
A week later, I tried to submit payment online and found that I was still blocked from receiving payment. Around that time, I received the only communication from B of A that I have gotten on this issue, and that was a paper letter stating my account was suspended. Unfortunately, at this point, I was moving and unable to attend to the problem. Through USPS I set up mail forwarding so that I didn't miss any further communication, but none was sent. I also did not receive any follow-up phone call from B of A. Since Moving, I've discovered that my wife is XXXX and COVID-19 has brought the world to a standstill, so it has honestly slipped my mind to check back in on the status. That is my fault. Now I have some time to check back in on my finances and realized I still have this issue.
I reached out to B of A again on XX/XX/XXXX. The phone representative I spoke with this time explained to me that the person I spoke with last never created an account suspension removal request, and so no one had ever looked at my case or knew to get back to me. I again submitted payment over the phone and was told that the removal request would take 3-5 business days to be reviewed and that I should check back on XX/XX/XXXX to see if I can submit an online payment. The phone agent was kind enough to waive the late fee associated with my account at this point. He also explained that the reason my account had been suspended was that it was being reported as closed when they attempted to bill. This had not been explained to me until that point. When I double-checked my XXXX account number, I discovered that while my payment account wasn't closed, the acct number and routing number were different than what B of A had on file. I promptly corrected this with help from the phone agent.
Unfortunately during this time, my account has been accruing interest and negative credit reports that could have been avoided had B of A made any effort to communicate a problem with me or follow up on the phone call further explaining the issue.
Bank of America 's lack of communication that a problem even existed, their failure to explain what the problem was when I made concerted effort to resolve the problem, their failure to follow up on the first call, and finally, their failure to properly manage the incident, has resulted in charges on my account as well as negative marks on my credit that will take years to no longer be a part of my score. This is not fair and will meaningfully impact me as we attempt to purchase a home for our now growing family in the future. I would like B of A to make things right.
Account suspension removal request ID : XXXX Payment confirmation # : XXXX - amt : {$2000.00} - date : XX/XX/XXXX ( Over the phone ) XX/XX/XXXX payment : {$320.00} ( Over the phone ) B of A credit dept : XXXX I have attached the following : - Bank account history going back to before the failed payment on XX/XX/XXXX, showing that my account had remained open and not closed and that sufficient funds were available.
- Statement from XX/XX/XXXX showing my over the phone payment on the first date I called B of A to try and resolve the issue.
- Screenshot of my B of A inbox showing that there haven't been any messages to indicate that there's a problem with my account or payment method - Screenshot of the error message stating my account is suspended, preventing me from making payment and directing me to their automated phone payment system, which also wouldn't take payment.
The phone agent that assisted me on XX/XX/XXXX was very professional, understanding and helpful, despite Bank of America 's policies."
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "Problem during payment process"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Money transfer, virtual currency, or money service",
"Sub-product": "Virtual currency",
"Consumer complaint narrative": "I have submitted a complain last week, but the wrong money transfers were reviewed. Please read this thoroughly, as well as looking at the screenshots to ensure proper case review.
Commencing on or about XX/XX/2021, I fell victim to a multilayered scam operation orchestrated by XXXX ( the Company ), with the design, development, manufacture, promoting, marketing, distribution, labeling, and/or sale of illegal and outright fraudulentinvestment services, all of which aim at contributing to the goal of robbing and defrauding clients, through a predetermined cycle of the clients losses to their gains. Money was transferred from my account via bank transfer and debit card and through an intermediary named XXXX in the total amount of XXXX USD utilizing Branch Banking and Trust services."
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "There are several things on my consumer report that were the result of identity theft. I sent the consumer reporting agency Transunion and identity theft affidavit. According to 15 U.S. Code 1681c2, these accounts and inquiries should have been blocked from my consumer report. However, they were not. Transunion sent a letter stating this block was an error. It was not. Transunion has violated my consumer rights. According to 15 U.S. Code 1681i Transunion is supposed to delete the disputed items during a full and complete investigation. Transunion violated this federal law. Transunion is still furnishing information without my consent. That is a violation of 15 U.S. Code 1681b. I have never given Transunion to furnish anything on my consumer report. You also keep re-inserting " XXXX XXXX '' on my consumer report without the proper notification according to federal law. 15 U.S. Code 1681n lets me know that any person who willfully fails to comply will be responsible for actual damages up to {$1000.00} per violation. Please note that I will not hesitate to file a suit against Transunion for willfully and knowingly violating my consumer rights if this is not rectified within 15 days."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "XXXX XXXX XXXX is attempting to recover a debt of {$790.00} that is not mine. I have never had a contract or account with them.
Thy even state in their letter that they are not the original account holder and that it was Pendrick Capitol Partners II LLC. I'm not familiar with them either.
If I have no written contract with XXXX XXXX why are they trying to collect a debt from me for another company? I request validation of this account with original documents showing itemized charges. Not photocopies, but originals with wet ink signatures on it.
I doubt that they can produce these documents. And if they do have them and they are not a licenced medical facility but a collection agency then that is a clear case of a HIPAA violation and possibly FDCPA violation.
I have sent a letter directly to XXXX disputing these charges and account a week ago."
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Threatened or suggested your credit would be damaged"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "I have been working with AmeriHome Mortgage since XX/XX/2021 to figure out why my mortgage payments have been raised substantially. They told me in XXXX that my account was being investigated and that my account would not be penalized in any way in the meantime. I have another complaint about that issue with your organization that is still pending.
This complaint is about AmeriHome continuously calling me regarding a debt collection over this mortgage. I informed both XXXX XXXX ( AVP, Customer Experience Office of the President ) and XXXX XXXX in writing about this harassing behavior. They call during work hours, they call in the early morning. They leave long rambling messages that fill up my voicemail.
XXXX XXXX did not reply to my notification of the harassment and XXXX XXXX said there was nothing she would do to stop it.
I have also written several times to XXXX XXXX and XXXX XXXX that I want any and all exchanges with AmeriHome to be in written form due to the lack of follow through or institutional memory on my case.
This is harassment. I am actively working with this company and have pursued relentlessly getting answers regarding my case. All delays have been from the side of AmeriHome. They are threatening my credit score and my home. This predatory and harassing behavior needs to stop."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "Let me first start off by stating this is my XXXX attempt to submit an complaint against Wells Fargo Loan Modification Department in regards to case number XXXX.
It 's my right to state that Wells Fargo Loan dept. has a policy that is federally mandated to help those who are in need or having hardships in their lives. However in our case, I will prove how Wells Fargo ignored that policy that boldly stand by and deliberately mislead my husband and I into thinking we were being helped but in fact Wells Fargo had no intentions on helping anyone.
Within the 16 years we have had our mortgage with Wells Fargo we have paid on time. Due to the economic downfall and loss of XXXX 's employment in XXXX, we found ourselves unable to pay our mortgage. Therefore in XXXX we had asked Wells Fargo for assistance and received a modification loan with NO issues or NO concerns. At the end of our loan modification we found ourselves still trying to manage our obligated debt and requested for an extension to our loan modification. At that time ww were told to resubmit another loan application to Wells Fargo Loan dept requesting a completely new loan.
This is where the nightmare started. We were assigned a new home preservation specialist, XXXX XXXX who was very rude and deceitful from the beginning. Every time I called to speak with XXXX, I was either ignored by being placed on hold too long or I was told she was not at her desk and that I could leave a message on her voicemail. In addition to my frustration on not given the chance to speak with the XXXX person who was supposedly going to help save our home for us, I was told that all my calls and concerns would be noted in my file for all to see see. That was completely untruthful because every time I called in regarding my loan, I was asked the exact same questions as if I never called in. Also when I asked what documents were supposedly missing, the reps had no clue on what documents I was referring too or if any documents were received via fax.
After multiple calls and left messages I finally got to speak with XXXX on XXXX XXXX, XXXX and was told she never received any faxed documents from me and told me then what missing documents were needed to continue my loan request. Without any delay I re-faxed all the missing documents to XXXX XXXX attention that evening and kept the faxed confirmation sheet stating all documents were completely delivered.
I have legiable documented proof that all documents i faxed over on XXXX XXXX, XXXX and submitted to Wells Fargo Loan Mitigation Dept. were completely delivered to XXXX XXXX.
Sadly, Wells Fargo Loan Mitigation Department and XXXX XXXX, would turn their backs on our situation by ignoring any further calls or email attempts I made on behalf of my home of 16 years. In the end, Wells Fargo had foreclosed on our home and auctioned off to the highest bidder on XXXX XXXX, XXXX Without legal notification or any required signatures made by either my husband or I. became a statistic as XXXX of the millions of struggling homeowners in XXXX and sadly had worked so hard to keep this home of 16 years.
So I hope and pray this 3rd attempt to be heard is taken seriously. I do not want a response back stating what Wells Fargo said happened. I want this case to be opened up and looked into the non communication and non responsive XXXX XXXX was to our case. In addition, I want someone to step in and investigate the documents that were faxed over and just misplaced. I want justice and I want to believe Wells Fargo is not a SCAM and that they do want to help people who are having hardships paying their mortgages."
}
Output:
{
"Issue": "Application, originator, mortgage broker",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "I purchase a XXXX XXXX XXXX XXXX from Yes Auto on XX/XX/XXXX by placing XXXX down and making payments on the rest. On XX/XX/XXXX they came and repo the vechile stating I defaulted on the first payment. The issue I have is I never know when my first payment was due or where to make the payment. I searched through my paperwork that I signed and couldnt find any of that information.
On XX/XX/XXXX I contacted Yes Auto to firgure out if they had repo the car because it was not parked where so parked it when went to work. Thats when I was informed they had repo it because my payment was due on XX/XX/XXXX. I tried explainning I didnt know when the payment was due and it was a mishap. I was informed that I had signed the paperwork and it was in my contract. They told me it would be XXXX repo fees plus my car payment of XXXX. I then informed them that I wasnt going to pay all of that considering I just paid a XXXX down payment on XX/XX/XXXX and wasnt intentionally trying to not make payment I simply didnt know when it was due.
When I got to lot to obtain my things I staff person came out and got in my personal space about the key. I explained I was getting my thing. He got confrontional with me and I then told him I had a right to get my stuff in peace. Once I obtain my property I got into the car I drove in and being to leave. Then the same guy that was confrontational came running and jumped into the front of my car trying to prevent from me leaving then another person came and jumped in front of my car as well on the other side. Both were banging on my car. The first guy then started banging hard on my car staring stop the XXXX XXXX XXXX!
Due to his deposition earlier I did not feel comfortable dealing with him and I also wanted to speak with the person that sold me car to discuss this matter and talk about my XXXX I had paid two weeks prior.
I was informed that they tried calling me but I never got any calls to my job or personal number. I had called my reference the day that Yes can stated my payment was due and reference 1 told me that reference 2 had informed them earlier in the week that they had received a call from the car lot stating to the affect that they werelooking for their car.
The issue I have is I didnt have the vechile 30 days before they took it back and I would have been more than happy to pay reasonable late fee if in fact I missed a payment. I dont understand how they can repo a car two weeks after someone gives them a down payment.
My other concern is of the staff members jumping in front of my car and cursing me and behaving in a threatening manner and also trying to hold me against my will at the lot over a key, that is not worth much compared to my XXXX I gave them for a down payment on a vechile I intended on having longer than 2 weeks."
}
Output:
{
"Issue": "Struggling to pay your loan",
"Sub-issue": "Lender trying to repossess or disable the vehicle"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "I received several calls from Unifin during business hours while I was at my job, but never any voicemails. After receiving 5 calls on a XXXX afternoon from the same number, I called back and was greeted with " How may I direct your call? '' - I let them know that I had received several missed calls from this number and was calling back to see what it was in regards to. I was then directed to a gentleman by the name of XXXX XXXX who told me that I had just been put into collections for a hospital bill that was over 5 years old. Long story short, ( over the course of a week ) I was told to call 4 different doctors offices and physicians groups to see where the unpaid debt had come from. I was under the impression that debt collectors had this information prior to contacting you, but because I was desperate to be taken out of collections, I called. After calling all 4 places, they all let me know that I was in good standing, and was never delinquent. I called XXXX back for 4 days where I was only able to leave messages. The following XXXX, I had called in again and had spoken to XXXX where he told me yet again to call another doctor to see where my debt had come from. I asked him why that number was n't provided to me originally? He put me on hold where another gentleman got on the line and let me know he was the manager. When I asked his name and the collections agency he and XXXX were with, there was silence on the other line. When I asked again, he very defensively let me know that he was just trying to help me settle my debt and that I was responsible to pay. I told him that I was happy to pay, but I needed to know this information. Again, silence on the other line. This went on for about 3 minutes before I began to get very nervous and started crying. Why are these people harassing me at work and then refusing to tell me who they are when I am trying to do right by my debts and pay them off? I hung up and called back into the main line, asked the man who picked up what the name of the company was. He told me Unifin and that was the only way I was able to find out who was calling me since the other Unifin employees refused to tell me any information. I ended up finding out that this information was false and I did not owe money for a medical debt. This is harassment and terrible business practice. Please beware and make sure to do your research if this company ever calls you!"
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Frequent or repeated calls"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX account # XXXX 15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than seven years.
15 U.S.C. 1681s-2 ( a ) ( 1 ) ( a ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or has reasonable cause to believe that the information is inaccurate.
15 U.S.C. 1681s-2 ( a ) ( 5 ) ( a ) which states In general a person who furnishes information to a consumer reporting agency regarding a delinquent account being placed for collection, charged to profit or loss, or subjected to any similar action shall, not later than 90 days after furnishing the information, notify the agency of the date of delinquency on the account, which shall be the month and year of the commencement of the delinquency on the account that immediately preceded the action.
The IRS clearly states a charge off as income.
Income DOES NOT get reported on the consumer which makes the reporting inaccurate."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Bank account or service",
"Sub-product": "Other bank product/service",
"Consumer complaint narrative": "My wife and I invested in XXXX, XXXX, XXXX, ( parent company : XXXX ) in 2001. We purchased the stock @ {$10.00} share and added more stock over the next couple of years @ {$10.00} to {$11.00} per share. The stock grew to over {$20.00} and settled around $ XXXX/share. One Friday morning, with no notice before, during or after, the FDIC closed the doors of the bank, turning the assets over to Umpqua Bank of XXXX. We lost approx. {$280000.00} of our retirement and never heard a word from anyone at FDIC, the bank, it 's board or anyone else. The only contact left was Umpqua who were not responsive to our requests for more information and did we have any alternatives to what happened. This destroyed our XXXX as we are now XXXX years old and have very limited funds. Do we have any recourse? We desperately need the money or some portion of it to sustain our lives. The closure was a few years ago now, but we only recently found out about CFPB. Please help."
}
Output:
{
"Issue": "Account opening, closing, or management",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "I signed a promissory note with a bank called XXXX XXXX in 2008. Citimortgage inc.who isforeclosing on me, is the plaintiff. The note it hasattached to its complaint and motion for defaultjudgment is a copy of what I signed with XXXX. It has no indorsements on it. I haveasked for proof that citi mortgage acquired the loan andhave not gotten any."
}
Output:
{
"Issue": "Loan modification,collection,foreclosure",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "XX/XX/2015 Reported Limit {$1700.00} Reported as Identity Theft Filed complaint with Credit reporting to have it removed. They have not removed it.
Contacted the credit card company and they can not verify the debt belongs to me.
Portfolio Recovery is saying I owe them {$2300.00} took me to court and provided false documents and could not verify the debt was mine. I filed and appeal with the court and a counterclaim was placed against Portfolio Recovery. awaiting court date."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was result of identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I am in disagreement with the items listed below which still appear on my credit report, even after your Investigation. I would like these items immediately re-investigated and DELETED. LET 'S READ ABOUT A REAL INVESTIGATION. ( FACT ) Improper investigation and/or no investigation conducted as prescribed by the following terms of investigation. 1681 ( a ) ( e ) ( e ) The term investigative consumer report means a consumer report or portion thereof in which information on a consumers character, general reputation, personal characteristics, or mode of living is obtained through personal interviews with neighbors, friends, or associates of the consumer reported on or with others with whom he is acquainted or who may have knowledge concerning any such items of information. YOU HAVEN'T CONTACTED ANY OF MY NEIGHBORS WHICH I HAVE THEIR STATEMENT FOR PROOF IN COURT. In addition,, you allowed the furnishers to break the FCRA law under Section 623 ( a ) which states prohibits furnishers from reporting information to a CRA if the furnisher knows or has reasonable cause to believe that the information is inaccurate.14 The statute defines reasonable cause to believe that the information is inaccurate to mean specific knowledge, other than solely allegations by the consumer, that would cause a reasonable person to have substantial doubts about the accuracy of the information. Please investigate these items and DELETE. I gave you both credit reports with before and after errors.
1. XXXX XXXX Account # : XXXX Check the Account Status, Date Opened, No. of Months ( terms ), Date Last Reported, Date Last Active, and Date of Last Payment for accuracy.
XXXX XXXX expedtiously due to they have been reinserted again inaccurate which is against the law. 15 U.S. Code 1681 ( a ) 5 ( B ) ( i ) ..."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical",
"Consumer complaint narrative": "This alleged debt was created and contracted with my employer I have no responsibility or obligation to pay this alleged debt, because this a workman s comp case. The responsible party is my employer XXXX medical claims. This company has being harassing me and still have not contact the responsible party."
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "Hello I had a loan serviced by Freedom Mortgage. They claimed to issue an XXXX refund of {$5600.00} on XX/XX/XXXX. I never received this payment. I called in early XXXX and was told that I would receive a replacement check by XX/XX/XXXX. I never received the check. I called on XX/XX/XXXX and was told they could not reissue the check since there is a new servicer, XXXX. Its not been almost XXXX months and I have not received my money and I keep getting bounced around with no line of sight of my money. I cant keep spending my time chasing this down"
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have filed disputes and they regarding inquiries and accounts that should be removed and they keep coming back verified. I have submitted multiple documents and they still refuse to delete these inquiries and accounts. I called each bureau over 25 times and sometimes I got the same agent who was very rude and suggested I stopped calling as she was able to see I called multiple times. I was told I needed to file an FTC and police report if I wanted inquiries and accounts deleted and that was the law. I had multiple credit reports with the file numbers and was able to point out the inaccurate information. I was told that account could not be deleted and I could only dispute the information so the data furnisher could correct and update the account. I repeatedly gave them the FCRA law that proved that wasn't true. I stated multiple times the FTC doesn't allow you to dispute inquiries or accounts and that I had to file a CFPB complaint. The agents for the credit bureaus repeatedly stated I need a FTC and police report and inaccurate information doesn't mean an account should be deleted. These credit bureaus have destroyed my credit and it has dropped my score from as XXXX to over 120 points with all the inaccurate information. If I dispute the account they allow them to update and and false remarks and comments and at one dispute it dropped my score from the XXXX 's to the XXXX 's. These credit bureaus are getting away with allowing these agents to use false information which allows them to profit off of data furnishers and consumers like myself not buy homes, get loans or a credit card because of the refusal of a proper investigation. I'm asking that I receive monetary damages as I will upload credit reports and other documents that prove they didn't do a proper investigation or delete accounts with inaccurate information. I want {$10000.00} for the multiple violations per the FCRA for ruining my credit and the multiple denials of credit including a home loan. These credit agencies should not be allowed to say I have to file a FTC or police report when that is not true but all bureaus are stating this when you call. These credit agencies make it impossible to dispute inaccurate information and inquiries and to remove the information is impossible because they side with the data furnishers. Something needs to be done when complaints are filed and make these companies submit documents just as we have to when they are stating this information is verified when it's not. The E-Oscar system needs to be done away with.. I have submitted credit reports which show each company is reporting different to each bureau which is a {$1000.00} violation each and I have given the bureau on multiple occasions to delete the information. In the letter from XXXX they can't verify that is my account and that includes them staying I have a bankruptcy that is on my report and belongs to me.. That is another violation. The accounts are XXXX XXXX, XXXX, and XXXX XXXX. The inquiries are XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX and XXXX XXXX."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I submitted mutiple times to Equifax a letter asking them to provide method of verification for a bankruptcy they are reporting on my account. Back in XXXX I sent them a copy of the letter I received from the the bankruptcy courts letting them know that they do not provide any information to the any CRA regarding bankruptcy.
I again sent a letter to Equifax in XXXX asking them to provide the method used to verify that the bankruptcy they are reporting on my CRA is accurate. They sent me a dated XX/XX/21, which states that they verified the bankruptcy with XXXX XXXX as accurate and that if I have any furhter questions to contact the bankruptcy court in XXXX MO, which would be the incorrect court to contact in regards to a bankruptcy in my area.
Again they either did not investiage this or they are just lying in thier response. As you know from the complaint I filed agains XXXX XXXX they stated that this bankrupcty was deleted from my record with them on XX/XX/21. If that is the case, then there is no way the Equifax verfied this account with XXXX XXXX, or recieeved the information for me to contact a court in XXXX MO from XXXX XXXX."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "Ok so first off this account does not belong to me a few months ago it was with another company XXXX XXXX it was removed from all three credit bureau 's. So i wake up a few weeks ago and I get several alerts that I have a knew collection account from Credit Managment whom I have never heard of, and never received any communication from at all. I have been calling them everyday since they added this account to my credit profiles. Not only do I not owe XXXX XXXX because I have never had an account with them but isnt it illegal for Credit Management not to give me any notice and ruin my credit they I work very hard to correct and maintain. This is ridiculous I do not appreciate their lack of tact how after I successfully got this account removed while it was with another collection company they then buy it and saddle me with it yet again. This is incorrect and an over use of their power further more for me to not to be able to reach them is very unprofessional.."
}
Output:
{
"Issue": "Written notification about debt",
"Sub-issue": "Didn't receive notice of right to dispute"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I SUBMITTED MY REQUESTS TO XXXX AND EXPERIAN USING THE ANNUAL CREDIT REPORT REQUEST WEB SITE. BOTH SITES HAVE REJECTED MY ELECTRONIC REQUEST. EXPERIAN STATING A CONDITION EXISTS THAT PREVENTS THEM FROM ACCEPTING MY REQUEST AND TO MAKE MY REQUEST BY MAIL. XXXX HAS REFUSED MY ELECTRONIC REQUEST FOR YEARS. I COMPLAINED ( XXXX ) A YEAR AGO AND RECEIVED THE XXXX CREDIT REPORT DATED XXXX XXXX, 2015. I DO NOT UNDERSTAND WHY I CAN NOT DO SO ELECTRONICALLY. IF THERE IS SOME SORT OF ERROR IN THE FILE PREVENTING ME FROM OBTAINING THE REPORT, I HAVE NEVER BEEN GIVEN AN OPPORTUNITY TO CORRECT IT. I PREFER TO MAKE MY REQUEST ELECTRONICALLY USING AN ENCRYPTED WEB SITE RATHER THAN SEND MY PRIVATE INFORMATION THROUGH THE MAIL. MY REQUEST IS TO RESOLVE THE MATTER AND BE ABLE TO REQUEST MY REPORTS ELECTRONICALLY."
}
Output:
{
"Issue": "Unable to get credit report/credit score",
"Sub-issue": "Problem getting my free annual report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "VA mortgage",
"Consumer complaint narrative": "In XX/XX/XXXX, my Fiancee applied for a mortgage to buy a house. He was NEVER asked if he was a veteran ( he is ). He was given a pre-approval letter for an FHA loan. He made an offer on a FOR SALE BY OWNER house. He gave the seller {$5000.00} cash and got a receipt. The loan officer then informed us that XXXX needed to have XXXX in his account. I gave him cash, so then we had to wait for 3 months so the deposit was off his statements. That was XX/XX/XXXX. Then we submitted all the requested forms and jumped through all hoops. Then the appraisal came in. There were many things that needed to be fixed, pipes had burst, new wiring, new panel, new furnace, new water heater. I spent XXXX fixing it up. It also took about 2 months to have everything done. That was XXXX, we were supposed to close XX/XX/XXXX. Then the loan officer kept telling us different amounts for the closing costs, first it was XXXX, then it was XXXX then it was XXXX. I then spoke to her boss, within five minutes he asked if fiancee was a Veteran. I said yes, so after seven months we changed to a VA loan, jumped through hoops had to have ANOTHER appraisal, fixed a couple of things, then passed appraisal. He stated closing costs would not be more than XXXX. Then loan officer tells me one day the closing costs will be XXXX, then the next day she stated we need XXXX. Loan officer aid after it passes the appraisal we can close. She got the appraisal results. Now after 6 weeks of waiting she tells me yesterday XXXX needs his XXXX. She should have told us that 6 weeks ago. Then I ask about a closing date. She won't give me one. We are VERY FRUSTRATED with this bank ( BANK of the WEST ). I asked for a new loan officer, she said her boss will call me SOMETIME today."
}
Output:
{
"Issue": "Applying for a mortgage or refinancing an existing mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "XXXX XXXX my bank account from Huntington was taken out for withdrawal under miscellaneous for {$1500.00} and I call the bank and ask them what was that transaction? The bank told me they dont know and will investigate. I also went to their branch in XXXX because I felt whoever I talked to was not much of a help. The teller I talked to also said this was a very strange transaction and she never seen anything like this. She couldnt figure out what this miscellaneous transaction was and told me she will put in a dispute for me. Two weeks have gone by and I didnt hear anything from them so I gave them a call back to check on the status of my dispute. The customer service told me that I withdrew {$1500.00} in XXXX and it was their fault for taking money out 2 months later, they kept apologizing saying it shouldnt have never happened to me and shouldnt have taken that long for the transaction to appeared. I told them I didnt withdraw anything for that amount and asked for proof of transaction in a documentation. They said they will send a letter explaining to me about what happened. XXXX week went by and I waited but no letter from the bank so I decided to give them another call, they told me there is no documentation or letter to show what happened and kept apologizing again. Most withdraw transaction say money withdraw but the {$1500.00} they took out it said miscellaneous, I found that transaction to be very strange and this bank is just trying to cover up someones mistake by blaming me. And I XX/XX/XXXX they took {$700.00} from my sons checking account and add that amount to my checking because they said my name was also on his account. Even though my name is on his account, they shouldnt have taken money out of his account because he is a XXXX XXXX student and work only part time and now he have no money to spend. He worked very hard just to save up to {$700.00}. I called the bank to ask them why did they take the money from my son and they said to cover the negative account in my bank. I told them its not right for them to do that to me as a single mother and to my son because they are ruining my bills I have to pay. They apologize again and nothing was done. I then asked again if they have proof for my {$1500.00} withdraw and they told me they dont have any letter to show it and cant provide anything. I am very angry with this bank and the poor customer service they have, I feel like they arent trying to do anything to help me."
}
Output:
{
"Issue": "Problem with a lender or other company charging your account",
"Sub-issue": "Transaction was not authorized"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "On XXXX XX/XX/2022, I submitted a concern about a refund of any unearned portion of interest charge to XXXX XXXX XXXX the CFPB. The initial letter is below. The company responded that the account didn't belong to them ; however, they have serviced my profile and the information was taken from my consumer report. I received confirmation from XXXX that NelNet has the proceeds concerning my complaint. I am attaching the reply from XXXX and is demanding Nelnet to release ALL unearned interest to XXXX XXXX XXXX. NelNet lied and is very unacceptable.
NELNET XXXX XXXX XXXX XXXX, XXXX XXXX Re : Refund of unearned interest on Account XXXX XXXX XXXX XXXX To Whom It May Concern, It has been brought to my attention that refinancing, consolidation or restructuring of a consumer credit transaction the creditor shall promptly refund any unearned portion of the interest charge to the consumer. The creditor shall compute the refund based on the method which is at least as favorable to the consumer and by law it is the actuarial method. The actuarial method allocating payments made on a debt between the amount financed and the finance charge pursuant to which a payment is applied first to the accumulated finance charge and any remainder is subtracted from, or any deficiency is added to, the unpaid balance of the amount financed. Upon receiving this written request, before the end of the 5 day period beginning on the date you received from the consumer this written request, the creditor or assignee shall provide me the disclosure of the amount due on any precomputed consumer credit account. Please provide me the statement of the amount necessary to prepay the account in full. I am aware that the amount you will disclosed includes an amount which is required to be refunded with respect to such prepayment, the amount of such refund. I am aware that the written statement is required considering that I am have made this request in writing. It is noted that I, the consumer, shall be entitled to 1 free annual statement each year without charge. XXXX XXXX XXXX, XXXX Without Recourse"
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Problem with customer service"
} |
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