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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": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "American Express denied my credit application for their XXXX XXXX XXXX credit card. They mailed me a letter dated XX/XX/2021 stating they can not approve my application at this time due XXXX my XXXX score. According XXXX the EQUAL OPPORTUNITY CREDIT ACT 12 C.F.R 1002.2 ( c ) REGULATION B ADVERSE ACTION- American Express committed adverse action upon me by refusing XXXX grant me credit. American Express has severly discriminated against me according XXXX 12 CFR 1002.4 ( a ), XXXX has inflicted discouragement upon me according XXXX 12 CFR 1002.4 ( b ). American Express has mailed me numerous credit advertising letters with offer codes, XXXX still denied my application. These are also federal violations according XXXX the FCRA. According XXXX 15 USC 1602 ( p ) American Express has committed unauthorized use of my credit card, XXXX I did not receive any benefit.
REFERNCE NUMBER : XXXX"
}
Output:
{
"Issue": "Getting a credit card",
"Sub-issue": "Application denied"
} |
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": "I have a loan with Tower Loan Company, due to a recent accident I have fallen behind however I am making the minimum payments, I just have n't caught up the past due. The company calls me repeatedly throughout the day. Sometimes XXXX or XXXX times daily. They also contact my family and friends as well as my place of employment to the same degree even though I have asked them to stop and I am making every attempt to pay the debt. They have threatened legal action as well as gone as far as to say if I do n't want to be embarrassed by their constant calls then I should try harder to get caught up. I have tried contacting them back, I am making my payments again but the calls continue."
}
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": "Creditor Name XXXX XXXX Account number : XXXX I recently received a copy of my consumer report, and I noticed some late payments posted on my accounts. By law, pertaining to the CARES ACT, accommodations were made to my account and ive been current since, and late payments should have not been reported and is a clear violation to my rights as a CONSUMER. I have never missed a payment in the month of XX/XX/XXXX or any months AFTER. I have been affected by XXXX XXXX Please update the month of XX/XX/XXXX. PLEASE UPDATE to either ON TIME, NO DATA/DATA UNAVAILABLE, or DELETE ACCOUNT altogether : The following supporting documents are enclosed for your review : 5. CARES ACT 6. 15 USC 1666 ( BILLING ERROR ) 7. 15 USC 1681 C-1 8. 15 USC 1681 C-2"
}
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 pulled my credit to myself for the first time in 2 years and I have derogatory items that are not mine including two bankruptcies. These accounts are fraudulent and need to be deleted. Please remove these unknown accounts from my name XXXX XXXX XXXX XXXX XXXX {$2100.00} XXXX XXXX {$4000.00} US DEP ED XXXX {$390.00} XXXX XXXX {$1300.00} XXXX XXXX {$1100.00}"
}
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": "In accordance with the Fair Credit Reporting act inquiries done by XXXX XXXX XXXX XXXX XXXX, XXXX XXXX XXXX XXXX, XXXX XXXX XXXX XXXX XXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX have all violated my rights.
I do not recognize these inquiries. Never been in contact with any of these companies there forth these are fraudulent and are inaccurately reported to my credit profile.
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"
}
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": "This letter is regarding account # XXXX, which you claim I owe {$1200.00}. This is a formal notice that your claim is disputed.
I am requesting validation, made pursuant to the Fair Debt Collection Practices Act and the Fair Credit Reporting Act, along with the corresponding local state laws. Please note that I am requesting " validation '' ; that is competent evidence bearing my signature, showing that I have ( or ever had ) some contractual obligation to pay you.
Please also be aware that any negative mark found on my credit reports ( including XXXX, Transunion and XXXX ) from your company or any company that you represent, for a debt that I don't owe, is a violation of the FCRA & FDCPA ; therefore if you can not validate the debt, you must request that all credit reporting agencies delete the entry.
Pending the outcome of my investigation of any evidence that you submit, you are instructed to take no action that could be detrimental to any of my credit reports.
Failure to respond within 30 days of receipt of this certified letter will result in small claims legal action against your company at my local venue. I will be seeking a minimum of {$1000.00} in damages per violation for : 1. ) Defamation 2. ) Negligent Enablement of Identity Fraud 3. ) Violation of the Fair Debt Collection Practices Act ( including but not limited to Section 807-8 ) 4. ) Violation of the Fair Credit Reporting Act ( including but not limited to Section 623-b ) Please Note : This notice is an attempt to correct your records, and any information received from you will be collected as evidence should any further action be necessary. This is a request for information only, and is not a statement, election, or waiver of status.
P.S. Please be aware that dependent upon your response, I may be detailing any potential issues with your company via an online public press release, including documentation of any potential small claims action. I am also including a copy of my complaint to the organizations below : Cc : Consumer Financial Protection Bureau Cc : Attorney General 's Office Cc : XXXX 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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "On XX/XX/2019, I reached out to American Express for a second time do to payment issues and was forced a second time to call my bank on three-way with them. The representative from American Express placed me on hold to call my bank, and then proceeded to use my social security number and other personal identification to log in to my bank account through my banks automated system without my permission. The representative should not have used my information to access my account without my permission, instead they should of three-wayed me in to allow me to enter that information. I am frustrated and concerned because I have to go through this process every single time I make a payment indefinitely and I am concerned that American Express employees are going to continue to access my banking information with my personal information without my permission. I asked to speak to the manager, who said she agreed with my concern then proceeded to brush me off. I would like to know what American Express is going to do to protect my identifying information going forward, since they insist I call every time I make a payment."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Privacy issues"
} |
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 XXXX Account # XXXX, has violated my rights.
15 USC 1681 Section 602 States I have the right to privacy.
15 USC 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions.
15 USC 1666B : A creditor may not treat a payment on a credit card account under + open end consumer credit plan as late for any purpose.
XXXX"
}
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": "Auto debt",
"Consumer complaint narrative": "Good afternoon, Thank you for your time.
I leased a car from Mercedes Benz.
The release was paid in full at the beginning of the contract.
When I returned the vehicle there were no other funds due. MB claims to have identified damage and charged me an additional {$620.00}.
They sent numerous notices to an incorrect address.
When I became aware of the debt, it was in collections with Mercedes Benz Financial Services ( MBFS ).
I called them immediately and we settled on paying {$400.00}. They were to send me the info where payment was to be made. They did not.
I called them again after receiving another notice.
I made payment over the phone, with MBFS for the full amount on XX/XX/19.
MBFS has continued to send me threatening letters with language such as " final notice '' letters without a response that notify me that you " have attempted to resolve this matter amicably ''.
When I contacted them yet again, they had not record of the {$400.00} payment and wanted me to pay the full {$620.00}.
I stated that I would send proof of the payment and sent them a portion of my bank account statement showing {$400.00} being credited to MBFS.
Yet they continued to call and state that I owed them {$620.00}.
I asked what did I have to provide as proof that payment was made in full.
They stated that they wanted my complete bank account statement, specifically all pages. I found this odd since they could check back to their account to confirm payment. It seemed fraudulent.
I sent them the information anyway but redacted vital info but they still have not acknowledged that the debt was paid and verbally stated that they refuse to send me a letter or email stating in writing that my debt has been paid.
MBFS has behaved in a harassing manner regarding a debt that may have been fabricated but was paid nonetheless and will still not recognize that the debt was paid and will not provide written confirmation. It makes me very nervous that something fraudulent is transpiring with this company.
Thank you again for your time and attention"
}
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": "VA mortgage",
"Consumer complaint narrative": "The company that is servicing my mortgage, Franklin American Mortgage, notified me today through U.S. mail that they have sold my mortgage to another company, XXXX, XXXX. The effective date of this transfer isXX/XX/XXXX. The date on the notice FAM sent me is XX/XX/XXXX, but the law pertaining to this, 1024.33 ( b ) ( 2 ) ( ii ), states clearly : " ( T ) he transferor servicer shall provide the notice of transfer to the borrower not less than 15 days before the effective date of the transfer of the servicing of the mortgage loan. '' Having received the notice only today (XX/XX/XXXX ), the transferor servicer did not comply with the requirement of the applicable statute. The fact that the notice may have been written prior to the 15-day window is irrelevant. Franklin American is required that I have such notice in my hand at least 15 days prior to the change is the relevant factor, and they are not in compliance with the statute. The company had many alternatives to ensure that I received this notification in a timely manner. Sending the notice via overnight mail would have gotten it to me within time, as would them calling me or emailing me. My profile with their company includes all my current contact information, and there is also a message tab on my account where they could have posted this notice. They did none of these things, insuring that I would receive notification late. The timing of this transfer is problematic. I get paid once monthly, on the XXXX. I have set up a recurring payment to come out of my account automatically, but I must cancel these recurring payments several days in advance, and they rarely post to my account until the XXXX, which is the date the notice indicates this transfer will become effective."
}
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": "Student loan",
"Sub-product": "Non-federal student loan",
"Consumer complaint narrative": "Upon graduation from college in XXXX, Navient told me my payment was in the $ XXXX. I called multiple times explaining how this was too much money for one month, and asked how I can lower my payments based on my income. They denied this request for a whole year. In XXXX, I was given the option for an extended repayment plan, but it was only lowered to {$560.00} a month. I made this payment for one whole year, and in XXXX of XXXX, I called and asked to lower it again due to upcoming bills such as rent so I was able to move out of my parents house. They advised me to call XX/XX/XXXX. I called back the XX/XX/XXXX, and my payment was increased to {$590.00} due to interest increasing. They told me I would not be able to lower any payment, and the {$200.00} a month I could not afford, my consigner ( my grandmother ) would have to pay it for me. Every time I called Navient, I was given different information regarding my loan status and payment options. I was always told there was nothing else I can do, I would either need to make the payment or apply for forbearance. Today, XXXX, I logged on to view my account and the loan payment was increased to {$610.00}."
}
Output:
{
"Issue": "Can't repay my loan",
"Sub-issue": "Can't decrease my monthly 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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hi I am submitting this XXXX XXXX this isn't any influence and this is not a third party. XXXX has low and unfair credit number for me in their report. I have complained more than 88 times nonstop this is exhausting im depressed they are not putting these fraudulent inquiries in dispute and accounts and they still haven't deleted them either I am depressed. The problem has not been resolved. XXXX XXXX has me at a credit score over 709 XXXX has me at a score around 590. That is a huge difference. XXXX paints me as a XXXX. my fico 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 90 days straight with XXXX i spoke to a representative agent name XXXX and XXXX and 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 one negative credit items outstanding debt now that is not mines. I have modest but ok income. Social Security. Something is wrong with XXXX. I do not understand why they are abusing consumers they don't help you at all This was a first 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 negative account its been 60days 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 88 times in the last 120 days. XXXX is an abusive company. They are supposed to be protecting consumers. They need to be reigned in. they are causing me severe XXXX and stopping me from getting this job offer n now im homeless n cant provide to my XXXX year old daughter PLEASE HELP ME PLEASE im depressed now.with no help they keep ignoring me after I keep telling them repeatedly they are hurting my character of my name they won't respond and it not even in investigation with and me and my son are bout to be homeless please help I cant live like this The credit bureaus never did a proper " investigation '' according to the FCRA. Section 1681a of the Fair Credit Reporting Act defines an investigative consumer report as a consumer report or portion thereof in which information on a consumer 's 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. They did not do this. Also they have violated my rights even more. The credit bureaus stated my account was properly investigated but how is that possible if I never gave these creditors WRITTEN CONSENT OR AUTHORIZATION TO USE MY PERSONAL INFORMATION TO OPEN OR CLOSE ANY ACCOUNT. THESE BALANCES IS IN ACCURATE, PAYMENT STATUS AND HISTORY IN ACCURATE, LAST ACTIVITY DATE IS IN ACCURATE, ALL INFORMATION REGARDING THESE NEGATIVE INSTALLMENT LOAN IS INACCURATE.Someone is using my personal information pretending to be me to obtain credit. I don't know anything about this fraudulent account. I don't have any closed OR OPEN accounts with THESE LISTED CREDITORS BELOW WITH MY DOCUMENTATION. Also they have violated my rights even more In accordance with the Fair Credit Reporting Everything has to be 100 % accurate on my credit report. The credit bureaus stated it was properly investigated but how is that even possible if THESE CREDITORS DID NOT GET ANY WRITTEN SIGNATURE, CONSENT OR AUTHORIZATION FROM ME. This account is fraud and does not belong to me, this is in accurate, I have never opened or closed any account with THESE CREDITORS I ask for documents with signature or authorization, they did not submit my request.This grounds for removal and deletion immediately. I never gave THESE CREDITORS ANY WRITTEN CONSENT OR AUTHORIZATION TO USE MY PERSONAL INFORMATION TO OPEN THIS ACCOUNT. THIS IS FRAUD. I'VE ASK FOR SIGNED DOCUMENTS WITH MY SIGNATURE GIVING WRITTEN CONSENT TO OPEN THIS FRAUDULENT CREDIT ACCOUNTS. SOMEONE USE MY PERSONAL INFORMATION PRETENDING TO BE ME WITHOUT MY AUTHORIZATION OR CONSENT TO OPEN OR CLOSE ANY CREDIT ACCOUNT WITH THESE CREDITORS BELOW They also violated the law because according to 15 U.S.C 1681 section 602 A. states i have the rights to privacy 15 U.S.C 1681 section 604 section 2 : it also states a consumer reporting agency can not furnish a account without my written consent. In accordance with the Fair Credit Reporting act WITH THESE CREDITORS BELOW EQUIFAX AND XXXX AND XXXX has violated my rights. 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 1666B : A creditor may not treat a payment on a credit card account under an open end consumer credit plan as late for any purpose SECTION 604 15 U.S CODE 1681 B THE LAW STATES THAT NO ONE CAN FURNISH INFORMATION ON MY CREDIT REPORT WITHOUT MY CONSENT. SECTION 605 A 15 U.S CODE 1681 C-1 WITH NO WRITTEN CONSENT OR INSTRUCTIONS THAT WOULD IN FACT BE IDENTITY THEFT SECTION 605 B 15 U.S CODE 1681C A copy of section 605B of the Fair Credit Reporting Act, whichrequires you to block the.fraudulent information on my credit report resulting from identity theft within four business days and to promptly notify the furnisher ( s ) of that information. Please block this information from my credit report, pursuant to section 605B of the Fair Credit Reporting Act, and send the required notifications to all furnishers of thisinformation."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Received offer # XXXX on XX/XX/2021 from Capital One to add an authorized user and spend {$1000.00} by XXXX to receive a {$100.00} statement credit. User was added and spent more than {$1000.00} before XXXX. Many agents confirmed this on phone. After speaking to 10+ people over a few hours in a couple of months, I still have not received the offer. I was told originally it would be done by the end of XXXX. It is now XXXX and I have spoken to 6+ people on my call today. I just want the {$100.00} credit I'm due."
}
Output:
{
"Issue": "Advertising and marketing, including promotional offers",
"Sub-issue": "Didn't receive advertised or promotional terms"
} |
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": "Home equity loan or line of credit (HELOC)",
"Consumer complaint narrative": "Client is trying to sell home that is in foreclosure but the equity loan was sold from XXXX to Trinity Financial they sold it to some third party but there is no assignment, power of attorney or servicing agreement so client can not sell because the title company can not locate mortgage person or paper work between Trinity and the party they allegedly sold it to."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "My issue is with the 3 major credit reporting agencies inwhom is reporting unverified information inwhich has not been certified and physically verified but instead the 3 major credit reporting agencies has relied on electronic Verification inwhich by law can not be used as vertification. In addition the credit reporting agencies has caused great harm to me as a consumer and others alike as well by continuing to use outdated technology inwhich is contributing to the spread of misinformation. In concert with the FCRA I'm demanding the following unauthorized accounts be blocked/ removed / deleted from my credit file immediately.
1 ) XXXX {$550.00} 2 ) XXXX XXXX {$690.00} 3 ) XXXX XXXX XXXX {$760.00} 4 ) XXXX XXXX {$270.00} 5 ) XXXX XXXX XXXX {$950.00}"
}
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": "The following accounts were a result of fraud. I am a victim of identity theft and I did not make this charge. Any accounts mentioned are not my responsibility..I am requesting that any finance and other charges related to the fraudulent account be removed from my credit profile asap XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX, Chapter XXXX Bankruptcy Dismissed XXXX, Chapter XXXX Bankruptcy Dismissed 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": "TRANSUNION HAS AN ACCOUNT XXXX XXXX ON MY CREDIT REPORT THATS NOT MINE."
}
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": "Last Year on X/XX/2016 I hired XXXX also known as XXXX to do marketing for me. In XXXX , Florida XXXX They said I had to have a different website they could not do marketing on my website. They were going to refund my money. Then they informed me that they could build my website. On X/XX/2016 I paid them {$1000.00} with XXXX and XXXX XXXX . They did not refund my {$230.00} they said they would build SEO into my website.
O n X/XX/2016 the y finished the website. I paid the balance {$1000.00}. At first I looked at the website and I thought it looked good. Then I started working with the website and it was broken, they also told me I could change my website to any of the options of the Wordpress template. I could not. I could only have revolving photos that looked like video, and the thumbnail photos on the gallery pages if you click on them they would enlarge, however they would not go back to small you would have to click out of the website. I tried to change not and then they said I could not change it. When they made the website they talked to me all of the time and then after it was finished there was no customer service. They would not answer me. One person sold me the site another person XXXX made the site with me and then there was another person I was supposed to communicate with who never answered me. After trying for 2 months with incompetent customer service and a broken website. I said I am out of this website. The man I was talking to said he was sorry for all of the issues. I went bak to my old website. I then disputed all of the charges with XXXX XXXX and XXXX ( they had XXXX ) So XXXX and XXXX XXXX had to agree to refund my money. They refunded all of my money.
Last week I received letters from a collection agency : Advantage Recovery Grou p, XXXX XXXX XXXX XXXX XXXX , XXXX , FL. XXXX ( XXXX ) XXXX . ( XXXX ) XXXX They said that it does not matter that my money was refunded from XXXX XXXX , that means nothing and they will not call XXXX XXXX . They said that is the same as calling the police. ( If something happens I call the police not in this instance ) . XXXX XXXX the man at Advantage Recovery Group said my dispute is the same as not paying. They will tell the credit bureaus and it will be on my credit report if I do not pay for the scam website. {$2000.00}. For that amount of money I should have great customer service and a website that worked. They are sending me the contract that I signed, plus other communication with XXXX the website and the customer service should be good and it was horrible."
}
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": "Payday loan",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I received a letter of Loan Forgiveness from EZCORP for a loan that was taken out in 2012. This loan was included in a Chapter XXXX Bankruptcy. I have received several calls from them and collectors trying to still collect on this loan. I have advised them on several occasions that this loan had been discharged on the ChXXXX Bankruptcy. They assured me that it was n't them that was trying to contact me on this loan. Now I receive a XXXX for this loan. The problem is that this information is supplied to the IRS as additional income. This would be correct if it was a loan that I defaulted on and did n't include in the Bankruptcy. Now the IRS is showing that I have an increase in my income by {$1900.00}. This is incorrect. What are my rights and options to get this taken care of? I have tried to contact EZCORP in regards to this, but I can not get through on their phone lines. I even asked them to contact my attorney but they never did."
}
Output:
{
"Issue": "Can't contact lender",
"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": "I have paid my mortgage on time. The servicer SLS has refused to accept proof of payment. I have mailed proof of payment through the postmaster and also spent hours on hold to get a fax number ( as they do not accept emailed copies of proof of payment ) I am in the middle of refinancing another loan into my name. They have now reported me 30 days late and this is affecting my credit score. It appears I have little recourse to the large banking institutions and I have no where to turn. I hope you can assist in resolving this matter. The lock rate fee is escalating on the refinance."
}
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": "Conventional home mortgage",
"Consumer complaint narrative": "In XX/XX/2018 I approached TD Bank for a refinance loan, the cost was {$7000.00}, upon closing, there was an additional {$20000.00} ask for on that day.
The manager collected the funds from my account.
In order to get this loan, I was told to get another address after they came to value the house twice.
Upon closing the documents could not be had for months."
}
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": "Consumer Loan",
"Sub-product": "Vehicle loan",
"Consumer complaint narrative": "consumer portfolio services told my insurance company when i got involved in a accident that i defaulted on my loan and the loan was charged off because of delinquent payment"
}
Output:
{
"Issue": "Problems when you are unable to pay",
"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 been trying too talk with Experian over and over again and I'm getting no where at all with them, They have never honored any of my disputes, and I'm paying {$19.00} and I'm paying for the three credit monitoring reports Experian can never get my XXXX and XXXX credit report correct and I'm paying them too get my credit report correct Iv'e sent them five copies of my XXXX XXXX and my XXXX and my XXXX XXXX payments and can't Boost Up My XXXX XXXX XXXX XXXX XXXX bills and There App is never working correctly ( Correctly..!!! ) Iv'e sent them a dispute letter on XX/XX/2020 by certified mail and they claim that they have not received it yet and won't be doing a investigation on my dispute and I'm faithfully paying them, I want all of my 11 closed accounts deleted erased with-prejudiced, I want my Boosts Boosted up or my money back. I want my inquiries also deleted erased with-prejudiced.."
}
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 my NUMEROUS request that I have been a victim of identity theft and that no one seems to care, that I want to dispute specific records in my credit file that do not belong to me, or that I have signed any agreement. The items I'm challenging have nothing to do with any transactions I've done or authorized to gain products, services, or money. Please remove the following ITEMS.
XXXX XXXX XXXXXXXX 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 XXXX XX/XX/2023."
}
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": "My personal cellphone is listed on the Do Not Call registry but this debt collector ( XXXX XXXX XXXX ) persists in contacting me in relation to a debt allegedly owed by my ex-wife. They have no right to do so."
}
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": "Money transfer, virtual currency, or money service",
"Sub-product": "Domestic (US) money transfer",
"Consumer complaint narrative": "On XX/XX/XXXX, I contacted Lincoln Automotive Financial where my car loan is processed and advised them that I would need to send the payment through XXXX XXXX as my payment was due on the XXXX.. I was given my account number and told to send it to XXXX XXXX. I did so at the XXXX XXXX in XXXX Colorado on XXXX and XXXX in the amount of {$640.00}, which included the payment of XXXX and {$7.00} for the XXXX XXXX fee. Tracking # XXXX A few days after the I began receiving calls from Lincoln Automotive Financial and was told that they did not receive the payment. I was told to call XXXX XXXX to make sure it was sent. I did so and was told that it had been sent to XXXX XXXX. I called Lincoln Automotive Financial again and was told they would review their records and call me back.
On XX/XX/XXXX, I received a call from XXXX at Lincoln Automotive Financial that they still had not received the payment and that I needed to contact XXXX XXXX again and call her back. She asked me to fax her a copy of my receipts. I did so. I made approximately 10 calls this day back and forth from Lincoln Automotive Financial and XXXX XXXX and the results were that the money should have been sent to Lincoln Automotive Financial but XXXX XXXX sent the money to XXXX XXXX ( the wrong company ). XXXX XXXX stated they would begin the process to get this corrected and I should call back on Monday of the next week. The case # is XXXX.
I called back to XXXX XXXX and was told that they needed the city in which the payment was to be sent. I called Lincoln Automotive Financial and was told XXXX, MI. I then called XXXX XXXX back and was told that they would get back to me in a few days after they received the money back.
I called XXXX XXXX the following Monday, ( Which is now XX/XX/XXXX. ) and was told that they are not receiving a response from XXXX XXXX to get the refund. That they would escalate the claim and get back to me in a few days.
I called on XX/XX/XXXX and I still have not received a response to where my money is. I was dinged on my credit with a late payment by Lincoln Automotive Financial due to the fact the money was not sent to Lincoln Automotive Financial because I told to send it to XXXX XXXX by the Lincoln Automotive Financial representative.
I want the money returned to me."
}
Output:
{
"Issue": "Other transaction problem",
"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": "The XXXX XXXX XXXX XXXXXXXX XXXX credit card company made an agreement and accommodation on my account to waive two late payments for XX/XX/XXXX and XX/XX/XXXX and retract two associated late payment fee amounts of {$40.00} each relating to the two late payments in prior months during the time in which I was in good standing with a XXXX balance on XX/XX/XXXX. They did agree to waive these missed payments in good faith due to the circumstances related to covid that caused me to get behind these two times, also considering my record of on time payments for the last 3 years with them.
After reaching out to them and making them aware of The Coronavirus Aid, Relief, and Economic Security Act ( CARES Act ), Pub. L. 116-136 ( CARES Act ) and how it addresses accommodations to consumers impacted by COVID-19. I disputed my credit report because they continued to report both months that they accommodated under the status " 1 : 30-59 Days Past Due ''. However section 4021 of the CARES Act, Pub. L. 116-136, states that such an accommodation includes, for example, agreements to defer one or more payments, make a partial payment, forbear any delinquent amounts, or modify a loan or contract should be reported as current according to the following : Section 4021 of the CARES Act amends the FCRA to address how furnishers report accounts subject to an accommodation if : ( 1 ) a furnisher makes an accommodation with respect to one or more payments on a credit obligation or account of a consumer, and ( 2 ) the consumer makes the payments or is not required to make one or more payments pursuant to the accommodation. If the credit obligation or account was current before the accommodation, during the accommodation the furnisher must continue to report the credit obligation or account as current. If during the accommodation the consumer brings the credit obligation or account current, the furnisher must report the credit obligation or account as current.
The dispute on my credit report for the reporting status was not handled correctly with XXXX, XXXX, or Experian and they each continue to report the accommodated months on my credit report for the months of XX/XX/XXXX and XX/XX/XXXX as 30 - 59 days Past due instead of current despite the CARE Act consumer reporting requirements."
}
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": "In accordance with the Fair Credit Reporting act XXXX XXXX XXXX XXXX XXXX, has violated my rights.
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"
}
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": "I am XXXX XXXX and I am submitting this complaint myself and there is no third party involved. Despite the multiple previous written requests, the unverified information listed below still remains on my credit report in violation of Federal Law. The TransUnion Credit Bureau failed to comply with Fair Credit Reporting Act, 15 USC sections 1681i within the time set forth by law and continued reporting of unverified personal pieces of information. PLEASE REMOVE THE FOLLOWING INCORRECT PERSONAL PIECES OF INFORMATION COMPLETELY FROM MY CREDIT REPORT.
Incorrect Addresses : XXXX XXXX XXXX XXXX XXXX XXXXXXXX, FL XXXX XXXXXXXX XXXX XXXX XXXXXXXX XXXX XXXX XXXX, FL XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information 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": "XXXX of 2019 I realized that I was a victim of identity theft and fraud. I started writing dispute letters, calling the creditors and also the credit bureaus. Majority of the items were removed. XXXX has XXXX XXXX on my credit after being removed by XXXX and Equifax. I have submitted affidavits and police report, and also a FTC report, I called and spoke with someone and he said he wont remove it and when I stated I was going to file a complaint he stated he didnt care.
Equifax is still hold XXXXXXXX XXXX XXXX XXXX and that too is fraud. Other than these items I have great credit thats how they know they arent mine."
}
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": "I had been having unexpected expenses between XXXX and XX/XX/2019 due to an illness and medical bills.I had overdrafted about 6 times during that period. Since I receive a monthly pension automatic deposit of {$5800.00} on the XXXX of every month and an automatic deposit of {$940.00} on the XXXX and another {$940.00} on the XXXX of each month from a part time job I usually have enough funds to cover any overdrafts during an emergency or period of extra monthly expenses. So I was billed {$35.00} in overdraft fees for about 6 times during this period. That's about {$210.00} in overdraft fees which I expected to incur and which I could make up with the funds coming in the following month. Well 's Fargo has a policy of permitting up to 3 overdrafts transactions per day with a {$35.00} overdraft charge per each transaction. They also have a policy of not charging an overdraft fee if the transactions occur within 24 hours of an automatic deposit and a policy of approving the transaction if an automatic deposit is scheduled to be deposited within 24 hours. However on XX/XX/2019 a {$240.00} transaction for XXXX XXXX was declined at XXXX mst even though the next day was XX/XX/XXXX and an automatic deposit of {$5800.00} was scheduled within 24 hours, It is suspicious that when it was not advantageous for Wells Fargo to collect their {$35.00} overdraft fee they simply denied the transaction in direct violation of what they advertised their policy is. This is false advertising. When I called and complained they simply said it is at the discretion of a computer and not guaranteed. It appears that it is only guaranteed they will approve an overdraft transaction if they can charge a {$35.00} overdraft fee but if the overdraft transaction occurs with 24 hours of a direct deposit and they can not charge a {$35.00} overdraft fee they decline the transaction in direct violation of their advertised policy."
}
Output:
{
"Issue": "Problem caused by your funds being low",
"Sub-issue": "Overdrafts and overdraft 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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I qualify for and am enrolled in the Public Services Loan Forgiveness ( PSLF ) Program. In XX/XX/XXXX, I submitted my application through the federal government to have my account changed from an Income Based Repayment ( IBR ) plan to the Pay as you Earn ( PAYE ) plan. My loan servicer, FedLoan Servicing, based in XXXX, PA did not begin processing this request until XX/XX/XXXX at which point it placed my account in forbearance to make the changes to my account. They did not complete the update until XX/XX/XXXX. Because forbearance is not a qualifying status for PSLF, the 3 months that my account was in forbearance do n't count toward the 120 payments, pushing my eligibility date from XX/XX/XXXX to XX/XX/XXXX.
Under standard repayment, my monthly payment is > {$1200.00} per month, resulting in approximately {$3700.00} additional dollars that I am liable for because of their failure to update my account in a timely manner. Additionally, placing an account in forbearance causes interest to be capitalized, which in my case resulted in {$1700.00} in interest being added to my principle balance."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have been disputing inaccurate information on my credit report with Equifax for months. The Equifax disputes has come back verified every time. XXXX XXXX I have pointed blatant inaccuracies for Date of Last Active, Date of Last Payment, Late payments after account is charged off. past due on a charge off account. XXXX XXXX I have pointed that XXXX does not own the account. The XXXX XXXX is owned by XX/XX/XXXX XXXX XXXX XXXX and XX/XX/XXXX XXXX XXXX XXXX is not registered in the State of Georgia XXXX I also pointed out blatant inaccuracies on the XXXX XXXX Date of Last Active, Date of Last Payment, Late Payments after account is charged off. XXXX XXXX has blatant inaccuracies, Late Payments saying I'm 90 days late and stating that I am {$1400.00} behind on my account. If Equifax had done their investigation like they are required to according FCRA Section 611, Equifax would have known my account is current and never late. Also in XX/XX/XXXX, XXXX, XXXX, XXXX I faxed Equifax my dispute for XXXX XXXX and Equifax stated that the account was verified. How can that be according FRCA Section 611 the credit reporting agency suppose to do an investigation and that did not happen. That is a violation!!!"
}
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": "I was in process to refi my house and discovered a collection account from MRS BPO in the amount of {$210.00}. This was a debt i informed them had been paid direct to XXXX in XXXX of XXXX, for an account closed from XX/XX/XXXX. At advice of my loan officer was to pay and request a deletion for better rates. I called XXXX XX/XX/XXXX requested that if I pay would they remove from my credit report. She informed me that once XXXX notified them balance is paid it would be deleted. Now they are saying they never stated that, and that they cant delete collection items reported to Credit Agency, but yet they can put it on my report. Please be careful and know rights as I should have used a pay for deletion agreement. Unprofessional practices"
}
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": "In XX/XX/XXXX I submitted a complaint against Freedom Mortgage and it appears they do not understand not only contract law but the simple fact that a Federal Law supersedes any of their " rules ''. Since XX/XX/XXXX I have been dealing with these guys so lets talk legal terms. 1 : In your last response you spoke of a " Contract '' that is between Freedom and Myself. In basic contact law one must have a contract, review the contract and approve the contract to be held accountable for said contract. Freedom purchased my home loan and I have never seen, or agreed to any terms with a contract that was never delivered to me. IAW If an agreement is not signed, the terms of it are generally not binding. Binding means that the parties are legally obliged to carry out their obligations/duties in the contract. Not only did I never agree to your terms ; I have never seen your so called contract. Most contracts must contain two elements to be legally valid : All parties must be in agreement. Something of value must be exchanged -- such as cash, services, or goods -- for something else of value. 2 : In XX/XX/XXXX Freedom was found guilty and paid a {$2000.00} dollar fine to the State of Mass and was also found guilty of Federal laws which is exactly what Freedom is doing currently. Freedom is currently in violation of the Mortgage Relief Act of XX/XX/XXXX which states if a borrower submitted a payment that covers the Escrow, Principle, and Interest the mortgage MUST process this as a full payment and later will receive the late payment from the borrower. Federal law trumps any rules that Freedom might have in a contract I have never seen or agreed too. 3 : So going forward in may I owed only XXXX and moving forward I am only owe Freedom a back payment of XXXX in late fees. Even in your own response you admit to not following Federal Law. So I am no longer playing this has gone too long. You will apply those payments as required by law or I will be forced to take this further."
}
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": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "PORTFOLIO RECOVERY bought the Debt from XXXX XXXX as of last year for two cards in the total amount of {$3200.00} one card is for {$1200.00} and the other capital one card is for {$2000.00} although the debt is nearly 3 1/2 years old. I didn't receive any documentation of my right to dispute or any paperwork for that matter. The next thing I know they're trying to take me for court and they didn't have an amount and no information specifically underlying my " case ''. I do not like the fact that this company is trying to bully me into paying a debt that I do not mind paying but using hostile tactics from a loan that they just bought from XXXX XXXX in XXXX and the debt pre-dates XXXX. ( Default of loans in XXXX ). This new loan has affected my credit score because it makes it looks as though my debt is brand new and it's not. I would like all reports from Portfolio Recovery to be removed since it's not from the original company ( XXXX XXXX ). I understand that Portfolio Recovery Soon after they first contact you, the debt collector is required to notify you of your right to dispute the validity of the debt. Then, you have 30 days to send a debt validation letter requesting proof that the debt is yours. After receiving your letter, the collector must stop collection activity until theyve sent the proof. Portfolio Recovery did no such thing. I never received that nor did I know that I have a window of time to dispute it. I understand that each state and rules may vary but my concern is that these loans will continue to be rebought over and over, as they continue to not only break the rules and keep me in the dark but hurt my credit and lively hood as well. I would like to come to some kind of peacful resolution."
}
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": "Debt collection",
"Sub-product": "Credit card",
"Consumer complaint narrative": "I received a message indicating that a complaint had been filed against me. The caller did not state the company name and only gave her name as XXXX XXXX. When I called, it took XXXX attempts to get her to identify the company and give me the mailing address. XXXX XXXX said that it was a complaint from XXXX Bank, a bank have not done business with in over 15 years. I explained that I settled my accounts at XXXX over 15 years ago and have no debts with them. XXXX XXXX said that if my social security is XXXX XXXX XXXX then the complaint is against me and they will take action. I would not verify my social security number, and I asked for their Maryland XXXX number. When she asked for clarification, I explained that I am a resident of Maryland that Maryland licenses collectors, and I needed ARA 's license number to confirm that they can legally do business in Maryland. She asked me to hold, and left me on hold until the call disconnected."
}
Output:
{
"Issue": "Taking/threatening an illegal action",
"Sub-issue": "Threatened to sue on too 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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "XX/XX/XXXX 2017 open account for XXXX to this date already pay XXXX and still have 6 payments to make which will make the total over 1000.00. can not afford to keep paying on this account and the interest is killer. need to have account closed and taking care of. feel it is pay in full. no more monies coming out. this is loan shark company and when you need help or in desperate need they are there to help but in the long run interest alone will get you on trying to pay back."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "It was deemed by Equifax there was no proof validating ownership with a signed contract owing XXXX XXXX XXXX account # XXXX in regards to this account on my profile. Therefore, it was removed.
Then 5 days ago on the XX/XX/23 it was added back on to my credit profile.
However, I WAS NOT notified in writing within 5 business days per FCRA guidelines of the reinsertion of this account."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On or about XX/XX/XXXX, I requested my credit report from Experian : I noticed that this company documented the same company that I was listed on my credit report four times, on two sheets of my credit report : ( 1 ) XXXX XXXX XXXX, collection, reported 2 times on my credit report, opened XX/XX/XXXX, {$28000.00}, ID # XXXX, times on my ( 2 ) XXXX XXXXXXXX, ID # XXXX, {$870.00}, date opened XX/XX/XXXX, reported 2 times on my credit report, ( 3 ) XXXX XXXX, {$2000.00}, ID # XXXX, opened XX/XX/XXXX, collection. Reported 2 times on my credit report.
This is error, I would like this removed from my credit report, I have disputed these accounts several times, however they still remain two times on my official Experian credit report."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Old information reappears or never goes away"
} |
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": "This is in regards to my previous complaint XXXX I guess I am more disappointed by the response than the original issue. Here is my response to CFPB about your so called " resolution ''.
The response just shows how tone deaf these guys are. They have not addressed any of the issues I mentioned.
The incident I mentioned was just the beginning and not an isolated incident. I have tried a few other charges and nothing goes through. So basically they have given me a credit card which is not usable at all.
And I asked a very specific question about how can I meet the {$500.00} spend criteria if they wo n't allow me to use the card. And this is how they respond.
" Your correspondence also expressed concerns regarding the promotional offer that was provided to you at the time you applied for this account. Our records indicate that your account was established on XXXX XXXX, 2016, under a promotional offer which extended a {$150.00} rewards bonus after you spent {$500.00} in net purchases on your card within the first 90 days of the account opening. We understand that you were expecting to meet the spend requirement for this offer with the above referenced transaction at XXXX which ultimately was not Cardmember Service, XXXX. XXXX XXXX, XXXX, ND XXXX processed. Please be aware that you have until XXXX XXXX to fulfill the required spend amount of {$500.00} '' She has basically copy pasted the terms and conditions of the spend criteria. In what world and how does that answer the very specific question that how can I meet the spend criteria if you wo n't let me use the card at all?? This is just insulting that you get these kind of answers from their so called " Office of the President ''.
To summarize, My card is still not usable at all, so that issue is not addressed I have no way to meet my spend criteria because of the above, so that issue is not addressed either.
I guess I will keep filing more complaints till I get someone to actually respond. I have never encountered this kind of shameless behavior. i guess it was not just the customer service but the whole chain to the top is like that."
}
Output:
{
"Issue": "Transaction issue",
"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": "I do not know",
"Consumer complaint narrative": "From XX/XX/2018 - XX/XX/2018 they have contacted me anywhere from 40-50 times.
They have accused my co-workers, my self and other associate with me of all sorts of allegations.
They have contact my wife and family members about this allege debt.
This is not my debt and I want the harassment to stop."
}
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 reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I am a natural person ( as such as the God of nature formed me ). My name in ALL CAPS is not me. I am the natural person XXXX, XXXX XXXX XXXX XXXX XXXX XXXX who is the student as well as the consumer on these accounts who applied for credit for personal purposes and who has a right to privacy afforded by the Family Educational Rights and Privacy Act 34 CFR Part 99 which protects the privacy of student educational records. These records are directly related to me, in which I did not give prior written express consent before they violated my rights to privacy.
Furthermore, In accordance with the Fair Credit Reporting act, THE LIST OF XXXX ACCOUNTS BELOW HAS VIOLATED MY FEDERALLY PROTECTED CONSUMER RIGHTS TO PRIVACY AND CONFIDENTIALITY under 15 USC 1681, The Family Educational Rights and Privacy Act of 1974, and The Privacy Act of 1974 : XXXX XXXX XXXX XXXX - XXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX, XXXX Closed accounts of XXXX XXXX XXXX closed XX/XX/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 XXXXXXXX, XXXX XXXX 15 U.S. Code 1602 ( b ) BUREAU -The term Bureau means the Bureau of Consumer Financial Protection.
15 USC 1681 ( a ) ( XXXX ) states : There is a need to insure that consumer reporting agencies exercise their grave responsibilities with fairness, impartiality, and a respect for the consumers right to privacy.
15 USC 1681b ( a ) ( XXXX ) I never gave any consumer reporting agency written instructions to furnish a consumer report/account as this section states.
15 USC 1681c ( a ) ( XXXX ) states : No consumer reporting agency XXXX make any consumer report containing any of the following items of information ( XXXX ) Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than XXXX years.
15 U.S. Code 1681c Requirements relating to information contained in consumer reports ( a ) WHICH STATES NO consumer reporting agency may make any consumer report containing any of the following items of information : ( XXXX ) Accounts placed for collection or charged to profit and loss which antedate the report by more than XXXX YEARS.
THESE ACCOUNTS LISTED ALSO APPEARS TO HAVE BEEN CLOSED ACCOUNTS AND ARE XXXX XXXX XXXX from XXXX and have been delinquent/closed/charged for XXXX years.
15 USC 1681s-2 ( a ) ( XXXX ) 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.
Furthermore, The Privacy Act of 1974 establishes a code of fair information practices that governs the collection, maintenance, use, and dissemination of information about individuals that is maintained in systems of records by federal agencies, as well as PROHIBITS THE DISCLOSURE OF A RECORD ABOUT AN INDIVIDUAL FROM A SYSTEM OF RECORDS ABSENT THE WRITTEN CONSENT OF THE INDIVIDUAL. The Act also provides individuals with a means by which to seek access to and amendment of their records, and sets forth various agency record-keeping requirements.
16 CFR Part 313 Privacy of consumer financial information governs the treatment of nonpublic information about consumers by the financial institutions. The term nonpublic personal information means personally identifiable financial information.
16 CFR 313.10 ( A ) ( 1 ) I have NOT been given as the consumer a reasonable opportunity, before the disclosure of my information to nonaffiliated third parties, to opt out of any disclosures.
Lastly, I also invoke my right to erasure ( Articles 17 & 19 of the GDPR ) in which I have the right to have my data erased, without undue delay, by the data controller based on the grounds in these sections."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Based on their random credit score check, XXXX XXXX reviewed my account and received a letter dated XX/XX/2018, pulled my credit score and determined that my account is closed immediately."
}
Output:
{
"Issue": "Closing your account",
"Sub-issue": "Company closed your 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": "Something happened to my Equifax credit file, and Equifax will not tell me what the issue is. They needs tons of documentation for a situation that I did not create. This is unfair to me."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
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": "XX/XX/XXXX Response to TransUnion Response to Credit Reporting Complaint TRANSUNION RESPONSE TO CREDIT MISREPRESENTATION COMPLAINT ID XXXX. SUBMITTED ON XX/XX/XXXX PRODUCT Credit reporting, credit repair services, or other personal consumer reports ISSUE Incorrect information on my report My complaint asked the credit agencies to remove misinformation 1 ) that there was a conflict between me and a person with an XXXX XXXX address claiming to have my Social Security number, and 2 ) that no verification of credit history could be done unless I was able to identify the address of the XXXX XXXX claimant as being my recent address. It was never my address.
In response to my complaint, TransUnion sent me access to a questionnaire to fill out with detailed information about my credit history, and my personal information, including Social Security information.
When I first learned of the credit companies misrepresenting my credit history in XXXX, I was asked to fill out such a questionnaire. At that time, I did give the credit companies access to all my personal information. I provided proof that my Social Security number belongs to me, but none of the credit agencies changed their misrepresentation in my credit report. XXXX claimed that the change could not be made because the information was a part of my credit history. It certainly was NOT a part of my credit history. My Social Security number has never belonged to anyone but me. The credit agencies would not change their fraudulent verification data in their computer system. The large number of credit agency information breaches justifies the Social Security Agency refusal to provide consumer data ( SSNs ) to credit agencies.
The questionnaire was a distraction. If I trust them again with all my private information, they do nothing to repair their fraudulent credit report. This was proven in XXXX.
CRAs do not collect information from consumers, but rather collect it from businesses, including credit card companies, banks, employers, landlords, and others. When an individual applies for credit, the lender will pull their credit report from XXXX or one of the other CRAs to see if they have a history of repaying their debts.
When consumers make a dispute, they often fail to get an adequate answer, or any answer at all, from the credit reporting company.
Since XXXX, I found other irregularities. I was unable to open credit union accounts or get credit. For example, XXXX would have allowed me to put money into an account, but not to take money out ). My current landlord forces me to beg for a rent voucher to take with CASH to a distant cash-transfer location ( XXXX XXXX XXXX XXXX ). They refuse to accept a check from a debit account, or to take cash to the rental company office in person. XXXX XXXX recently ( XX/XX/XXXX ) refused to open a XXXX XXXX deposit account for me because TransUnion failed to verify me, claiming a Social Security number conflict. To some companies, TransUnion reported that I had XXXX credit.
To avoid further fraudulent reporting, I cancelled all my credit cards. I never had a problem with my credit history before. I never wanted to deal with fraudulent credit card companies again.
TransUnion should have reported that I froze my credit reporting due to the companies misrepresenting my credit history.
I do not accept TransUnions response as constructive. They already know that my Social Security number is mine. They purposely set up their verification process to use fraudulent information. This is their policy. Many consumer complaints describe this process. Prosecute the credit agencies for propagating fraudulent information in pursuit of profit."
}
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 subscribed with Experian and after 1 month I ccancelled my subscription. They keep charging against the credit card I initially provided them. I have tried repeatedly to contact them but I am kept on hold over an hour and a half with no response. No ability to leave a message or callback. I have tried online unsuccessfully and the same thing of unheard of no response phone wait. I want my subscription with EXPERIAN cancelled immediately and my credit card refunded each monthly fee other than the 1 month."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
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": "Hello, i have been a long time american express customer with XXXX cardholders on my american express account. A couple of years ago american express began automatically placing chargers over a specific amount ( I believe it was any transaction over {$150.00} ) into a pay over time category which is not payable in full at the end of the month and accrues interest of approximately 16 %. Initially we did not know transactions were not automatically moved to this category so there were transactions for both card holders included in this pay over time balance which accrued interest and grew over time. on several occaisions i have contact american express to provide the pay over time balance and accrued interest balance disaggregetaed for each card holder. american express has been unable to provide this information to me. Having this information is imperative for card holders especially since the pay over time election is made my american express ( i asked american express to remove this function however pay over time chargers recur on my account from time to time ) and the fact that interest accrues on this balance. i will say that the reporting on the pay over time balance has improved over the years ( a couple of years ago it was almost impossible to determine the entire pay over time balance, now this information is available in summary form ) however the data available to cardholders is insufficient to properly track charges and manage monthly payments."
}
Output:
{
"Issue": "Billing statement",
"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": "Unauthorized activity on my Barclays card. There 's still XXXX transaction that has n't been corrected. The fraudulent charge is from XXXX XXXX XXXX in the amount of {$4900.00}. I did NOT make this purchase. This is an unauthorized/fraudulent charge."
}
Output:
{
"Issue": "Identity theft / Fraud / Embezzlement",
"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": "4 months later, refinance still not finished.
XX/XX/XXXX : Applied for and was approved for refinance from Amerisave ( NMLS # XXXX ) ; paid {$500.00} application fee.
XX/XX/XXXX : All required documentation provided.
XX/XX/XXXX - XX/XX/XXXX : Didn't hear anything from lender, no returns of calls/emails.
XX/XX/XXXX : A processor reached out to ( ultimately ) set up a closing date of XXXX XXXX.
XX/XX/XXXX : Closing appointment canceled with an hour 's notice given to me. XX/XX/XXXX : Closing appointment canceled without any notice given -- I had to call signing agent because I thought he was late and he told me the documents weren't ready.
XX/XX/XXXX : Wired {$3400.00} to XXXX XXXX for closing costs.
XX/XX/XXXX : Closing appointment was canceled with an hour 's notice given to me.
XX/XX/XXXX : Lender requested a second hard pull of credit, more documents, The entire time I've had to intermittently provide updated pay stubs, bank statements, and other documentation, which has been extremely inconvenient and wouldn't have to happen if the lender had their affairs in order. Between the application fee and the closing costs, the lender has {$3900.00} of my money.
I've done two other refinances and they were both simple and fast. This has been completely unacceptable."
}
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": "Money transfer, virtual currency, or money service",
"Sub-product": "Virtual currency",
"Consumer complaint narrative": "I HAVE UNAUTHORISED USE OF MY ACCOUNT WITH PAYPAL THEIR DEFENCE IS THAT IT WAS ME THEY WONT PROVIDE ANY PROOF MORE THAN THAT THEY RETAIN FUNDS 180 DAYS MORE THAN THAT I WAS KICKED OUT FROM THEIR SERVICE AFTER BIENG SCAMED"
}
Output:
{
"Issue": "Other transaction problem",
"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": "I do not know",
"Consumer complaint narrative": "JC Pennys reported to the credit reporting company 's my account was 3 month late cause 50 point drop in my score my jc pennys account is paid in full XXXX,, nor did I do anything to stop payment at XXXX XXXX is not my account why jc pennys paid for it for and account that's minds not sure why, I requested a payment history to date nothing the account don't apply to me nor minds JC Pennys are XXXX nor was I notified of no payment for three months its not my account either one"
}
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": "Back on XX/XX/XXXX I and co-signer took out a XXXX XXXX XXXX XXXX XXXX XXXX via XXXX XXXX for {$15000.00}. I recall the variable interest rate was 3.5 %. The loan has been in a XXXX deferment the entire time. I now not the rate to be 14.5 % and the loan is at {$31000.00}. Now loan is with Navient. I have contacted Navient multiple times to see what happened. I requested to know what the original rate was and informed they ca n't find the original disclosure statement for the loan. I and same cosigner took a XXXX XXXX XXXX XXXX XXXX XXXX XXXX on XXXX/XXXX/12 for {$5000.00} with a 3.5 % rate- It 's still at 3.5 %. I have never missed a payment with the loan and the accout is in good standing. There is no reason the rate to have increased to 14.5 % and Navient ca n't attest. I feel this has been a grave error on either XXXX XXXX 's or Navients part."
}
Output:
{
"Issue": "Dealing with my lender or servicer",
"Sub-issue": "Don't agree 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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In XXXX 2017 I filed a dispute of an item on my credit report with Transunion. I provided documentation which proved that a collection did not belong to me and Transunion removed the disputed item from my credit report. A week later I discovered through the TransUnion website that they had lowered my credit score. The reasons for the scoring were contradictory. On the positive side they claimed I had no collections, that my debt ratio was perfect and my payment history was perfect. On the negative side they claimed I had too many collections and past due payments my debt ratio was too high and the age of my accounts was too recent.
When I called to find out the reasons for the discrepatencies they told me that the score on line is not my real score and that I would have to pay to get my true credit score. When I told them that they had raised my score last month and I owed more and had the incorrect collection yet this month they lowered my score even though I owe less and the collection was no longer listed they told me that the scores they give for free and the scores they give when I pay them and the scores they give to potential creditors are not the same. They also refused to transfer me to a manager for clarification. I feel that TransUnion is intentionally providing fake score on their website in order to force people to pay for an accurate credit score. In addition, the measure they use to determine credit scoring is arbitrary and biased. I feel TransUnion uses bribery in order to sell scoring products and also practices discriminatory scoring practices."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
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": "Back in XXXX of this year, Seterus XXXX opted me into a coupon program and stopped providing me with online statements. I had been paying on their website at the time and was retrieving my statements online. I did not ask to be opted into the " coupon '' program. I called in XXXX to have this resolved and was told that they were sorry. Then they opted me out of the coupon program and said I should be able to view my statements online. Subsequently I stopped receiving statements of any kind. I no longer received the paper statements by mail and was still unable to view statements online. On XXXX XXXX I called again and was told they found the problem and my XXXX statement would be available. I called on XXXX XXXX and was told that the statement had n't been issued yet and to call back in a few days. I called on XXXX XXXX and was promised a call back. I never received a call back. I called in again on XXXX XXXX and was told they had reached out and to check back in XXXX. I have asked to speak with a manager on multiple occasions and they have refused. To make a long story short, I 'm extremely frustrated. Calls to Seterus are getting me nowhere. I have spoken with XXXX at ext. XXXX and XXXX at ext. XXXX among others. I believe if I 'm paying them they have to provide me with a statement but they are unable or unwilling to provide this statement."
}
Output:
{
"Issue": "Loan servicing, payments, escrow 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 reviewed my credit report and checked that I had an inaccurate account that needed a positive and quick response. There is a discrepancy on my credit report and the balance on the account is inaccurate and incomplete. I am consumer by law and my information was misused. I listed below the item with discrepancy that indicates the company name, balance and date of last reported : XXXX/ {$8700.00} / XX/XX/2022"
}
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": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Transunion has an incorrect address noted for me. I believe that there is an individual with my name who lived there, and the information was simply added to my report instead of hers."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Personal information"
} |
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": "Were charged monthly fees when they shouldnt have been. Wells Fargo advertised no fees if a customer made 10 or more debit card purchases and/or payments in a month, but the bank limited the types of eligible payments and didnt count debit transactions that posted days later."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Fee problem"
} |
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": "RE : Letters Submitted : XX/XX/2021 XX/XX/2021 & CFPB COMPLAINT XXXX FEEDBACK SUBMITTED XX/XX/2021 I called and spoke with a TRANSUNION representative about my report on XX/XX/2021. I was only provided a partial report with no score. When I spoke with the rep I was told that I would receive a new report and updated score once the full disputes had been compete. On XX/XX/2021 I received the new credit report but again it was partial information not a new full report with newly updated score. I do not have pay for a new score as the partial credit report sent to me states. I am entitled to accurate and fair reporting as the consumer.
On XX/XX/2021 your organization responded to my complaint to the CFPB saying : Within 30 days, we will close out your dispute and advise you of the results in writing. You have not done so and you are in violation. Because you have not provided me with an updated credit report and score and have failed to respond to my written disputes within 30 days which makes you in violation of FCRA Section 611 Part ( A ) ( 1 ).
It is obvious that TRANSUNION has attempted to circumvent the system and I will report this if they do not comply. Since XX/XX/XXXXI have provided documentation and asked for them to investigate then delete this inaccurate information from my report and provide me with a newly updated report and score. It is now XXXX and TRANSUNION has failed to comply with the law.
Also you are attempting to Re-age my account by updating the date of last activity on your credit report in the hopes of keeping negative information on your account longer. I am certain you are aware of the FCRA Section 605 ( c ) Running of the reporting period of which you are also in violation of. Its the original date of when the debt was incurred that determines when it is removed from your credit report."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
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": "NOTICE of Fraudulent access to my non public personal information. No express written consent was provided to D2 Management to use, store or maintain my indentifying information for monetary gains. REMOVE or DELETE this entire account from ALL consumer reporting agencies and your internal data base. Failure to comply with law shall result in criminal liability."
}
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": "I never applied for a Bank XXXX XXXX credit card from XXXX to XXXX The account # on my my credit report is XXXX. Someone else did this by using my information which this is fraud and theft. This needs to be remove from my credit report from all XXXX credit bureaus."
}
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 card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Applied and was approved on XXXX XXXX for a Citi Simplicity Credit Card with 0 % interest for 21 months. This is what they advertised. Today is XXXX XXXX and still have not received the card. I have called 3 times the first time on XXXX XXXX to report I still did not have the card even though I received numerous emails welcoming me and stating that I needed to activate my card. I was told it was mailed on XX/XX/XXXX (? -Holiday? do n't think so-1 hour on hold ) and I would need to wait another week. So I called back on Friday the XX/XX/XXXX but hung up after XXXX minutes on hold. So I called back again on XX/XX/XXXX ( approx 45 min on hold ) to advise I still did not have the card and he told me he could block it but there would be a {$6.00} charge to reissue it. I told him I was n't paying the charge because I never received the first card and not even sure they mailed it. He told me he would waive the fee since I am a valued customer and would transfer me to the reissue department ( another 20 minutes on hold ). The girl told me that she would overnight me a card via XXXX and I should have it no later than Tuesday, XXXX XXXX. That is today and it is XXXX and I still have nothing. I got a welcome letter in the mail today ( it was mailed to the same address that I verified with them each time I have called ) for the card they supposedly blocked. It is amazing how that arrived but still no card. I am calling once again to get a tracking number ( if they even sent another card ). So far I have been on hold for 30 minutes. They just answered he asked me my first name and I provided it told him that I need to get a tracking number and he stated one moment m'am and now I am back on hold ... ... I said H-E-L-L-O and he said yes m'am I am checking on it. But I do n't see how since he only has my first name. Put me back on hold .... now it has been 35 minutes and someone is dialing on the phone. I have a feeling they are going to disconnect me. Still no answer, great I am back on the hold music! Meanwhile my 21 months @ 0 % interest is clicking away. This starts on the date you are approved. I think they need to honestly advertise that they have no plans on mailing the card immediately so really it is more like 18-19 months not 21 months no interest."
}
Output:
{
"Issue": "Advertising and marketing",
"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": "Fraudulent account identity theft XXXX XXXX XXXX XXXX Open XX/XX/XXXX Amount XXXX Original creditor XXXX XXXX XXXX and XXXX XXXX XXXX Unknow person opeond Original account in XXXX account was deleted from all 3 credit reports and founded as fraud Somehow after 2 years the account found its way to collection company after previously being deleted from Original creditor on all 3 reports I provided as much evidence as I could in the pictures I sent as you can see in the photos the original account was deleted and as you can see in the photos the original account number and the current account number are the same as the account that was deleted 2 years ago Could you please stop allowing the original creditor to send this account to collection agencies when the original account has been deleted and I was not found responsible for the fraudulent account"
}
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": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "Customer Service : I am writing this letter to request help with obtaining an overpayment from Acura Financial Services. I recently was blessed with a financial gift to pay-off my car. The financial gift was more than the final pay-off ( See enclosures ) ; it was an additional {$2000.00}. I contacted Acura Financial Services before sending the pay-off and was told, it would take a week and a half before I would receive my overpayment. After that time passed, I contacted customer service. I was told that the check would be held to make sure everything was Okay and the overpayment would be sent out on XX/XX/XXXX. I told the lady it was a cashiers check, which is cash, so why would it need to be held. She indicated she would send it over to another department for review. I asked for a supervisor and was told it would take 24 to 48 hours before I could speak to a supervisor. I called back after 48 hours and spoke with XXXX and she pretty much gave me the same run-around, but she did say that the check had been mailed from Texas. I asked her if I would still receive a call from a supervisor and she said the supervisor would tell me the same thing.
Shortly thereafter, I received a phone call from XXXX ( Supervisor ) and he told me the check was mail on XXXX the XXXX and it was coming from California, but he could not determine the mail time frame. Now understand, I received my title a week and a half after the car was fully paid off on XX/XX/2020. The title came around XX/XX/XXXX.
I am now requesting help to obtain my overpayment of {$2000.00}. I feel as though Acura Financial Services hold individuals overpayments for their purpose ; to increase their profits.
This should be investigated for all consumers if this is an ongoing practice. A cashiers check is guaranteed."
}
Output:
{
"Issue": "Problems at the end of the loan or lease",
"Sub-issue": "Unable to receive car title or other problem after the loan is paid off"
} |
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": "For approximately one year, I have been trying to certify employment with XXXX XXXX to track payments for the public service loan forgiveness program. Every time I sent in forms, their customer service would tell me either the form was never received or one of their employees entered the information incorrectly, or there was a miscalculation with the number of qualifying payments. Each time they told me they would fix it and contact me and each time I am never contacted. Every time I call in, there is a new problem. They are intentionally stringing me along to prevent me from qualifying for loan forgiveness. I have all the documentation to prove my efforts for approximately one year and they still have not accurately tracked my eligible payments. I have only worked in public service, so all payments should qualify. They are giving me approximately two years of payments yet I have been working in public service for almost 7 years."
}
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": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "I received a notification from XXXX XXXX about a collection that was placed on my XXXX report which dropped my score 31 points. It was from Debt Recovery Solutions for the amount of {$1400.00}."
}
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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "During the past year as a Lending Point customer I have encountered many problems. The due dates, payment amounts, and late payment information has been inaccurate and still is. Lending Point has failed to update it's systems and that that resulted in an erroneous negative late payments being reported to my credit report, harassing phone calls, and extended wait times when calling to report and correct the issues.
Recently Lending Point has changed their web site to include a 2 % transaction fee on all payments made via their web site. In an attempt to avoid this exorbitant new charge I recently called to make a payment and now they charge on all phone payments as well. With the payment amounts the 2 % fee can be very expensive, My average payment to Lending Point is {$1000.00}. This would mean an additional {$20.00} charge just to make the payment.
Working with Lending Point has proven to be very difficult and they do not have the customers financial success or future in mind. Lending Point continues to create obstacles that obstruct payment ease, accurate payment amount and due date information, and consumer friendly access to accounts."
}
Output:
{
"Issue": "Problem when making payments",
"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": "I went on XXXX XXXX XX/XX/XXXX and ended up in the hospital till mid XXXX. Went on vacation in Florida XX/XX/XXXX thru the XXXX, after the management meeting i had with the time share resort I'm apart trying to get rid of the property since i'm no longer in good health or finance to keep it, my cell phone and wallet were stolen. i tried calling on a land line to claim it had been stolen but was told that i had to call from a number registered to my card. i don't have a land line only the cell phone that was stolen. I didn't have the things i needed to get a new phone with me. With my health and life getting in the way i was not able to get a new phone till XX/XX/XXXX of XXXX, by then the thief had used my phone to charge over XXXX to my bank of America card and various other credit cards. i called all of the companies and told them what had happened they took care of it right away. Bank of America on the other hand keeps saying I'm on the hook and it wasn't fraud. I am very sick and with covid 19 i have not been able to work i am a single a mother I can not pay XXXX for someone else using my card."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Card was charged for something you did not purchase with the 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": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I paid off my credit card in full and they charged me interest after the fact. Not only that they withdrew money from my account for the minimum payment and then deducted money for interest after I paid off my credit card And would not refund my money."
}
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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "My second mortgage loan with XXXX XXXX XXXX past due amount was paid in full in XX/XX/XXXX. And, I continually pay the monthly payment due each month until the full amount, showing on my credit report to date, is fully paid. XXXX XXXX XXXX must change the status of my loan to " pays as agreed ''. Charged off account, as committed by XXXX XXXX XXXX is false, as I am current on my monthly dues as noted on my credit report.
On my personal information, the XXXX addresses showing and reported that I have lived in : former addresses XXXX and XXXX need to be deleted. I do n't recall living in these XXXX addresses.
Lastly, there 's a query listed from a XXXX XXXX on XX/XX/XXXX. Please remove that on my profile, I have never dealt with XXXX XXXX before.
Lastly, how do I go about clearing the 'negative accounts ' on my credit report?
I appreciate your help resolving the issue as noted here.
Thank you"
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Account status"
} |
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": "Account has several addresses with inaccurate spelling.
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 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information 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'm member of XXXX who report my rent being paid each and ever month to all credit bureaus. XXXX, Equifax and XXXX to earn my credit scores from all three of rent being paid. My credit scores hasn't been increasing of my rent being paid each every month. My credit scores should at least be XXXX for me to qualify for home loan. I feel i am being held back from qualify for home loans. Maybe because of my XXXX, XXXX, age, XXXX, martial status. To qualify for home loans."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
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": "An XXXX report indicated that Equifax provided my credit information to " XXXX, XXXX XXXX XXXX, XXXX '' without my permission on XXXX/XXXX/15. " XXXX '' is actually " XXXX '', which apparently has a very long record of this behavior. Thanks for your assistance!"
}
Output:
{
"Issue": "Improper use of my credit report",
"Sub-issue": "Report improperly shared by CRC"
} |
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": "I was enrolled at wyotech in XXXX Florida for school. I was told that I had no choice but toGet this loan from genesis lending for my XXXX academic year, I had to get this personal loan for {$5000.00} to be able to finish my school. The XXXX advisor never explained this when I started my first academic year, anyway I was also told that hey would find job placement as soon as I got out of school, and they did not do anything to help me, I have had financial hardship since I have been out of a school and could only find a job that pays me XXXX an hour. My loans are to high and genesis will not work with me and the interest is 12.9 % and each Month they charge me an additional XXXX late fee. Everything I was told has been false regarding the loans and interest, as well as the job market, there are no jobs that are paying XXXX to XXXX an hour! I have had extreme hardship trying to pay this debt to my fed loans as well as genesis. My genesis loan is still at XXXX 3 years and it only shows XXXX paid!"
}
Output:
{
"Issue": "Can't repay my loan",
"Sub-issue": "Can't decrease my monthly 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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "It has been drawn out into the open that you are not hindering records when I sent a copy of the FTC report. Since it is a necessity by regulation 605b of the FCRA I'm sending this Complaint to assist with laying out a lawful response. In the event that you decide to not obstruct the accompanying things recorded beneath which are answering erroneously because of fraud, I will hand my archives over to my lawyer to begin a legitimate activity. It's my goal to not seek after any legitimate activity so if it's not too much trouble, comply with the law.
XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXXL XXXX XX/XX/XXXX XXXX XX/XX/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 card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "My husband has had our Discover card for 21 years and has an exceptional XXXX of XXXX years is being charged a 17.24 % APR."
}
Output:
{
"Issue": "APR or interest rate",
"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 sent in a request with all the required information to experian over a month ago and it was completely ignored. im submitting this request again via cfpb"
}
Output:
{
"Issue": "Problem with fraud alerts or security freezes",
"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": "I had a payment card processing account with Square Inc.. On XX/XX/XXXX they sent me an email stating my account was deactivated and I would receive my funds within two business days of XX/XX/XXXX. On XX/XX/XXXX they sent me another email stating " Due to the recent chargebacks on your account, any funds currently in your Square account will be held for an additional 10 days. '' However I have not had a single chargeback on my account. I waited the 10 days anyway and still couldn't withdraw my money so after contacting support multiple times which their customer service is geared so they can steal people 's money. Everytime you contact them they tell you that the team that can release your funds only communicates by email so sit and wait for an email which never comes. It's literally impossible to speak to anyone who has the power to release your funds. So, Finally on XX/XX/XXXX I filed a complaint with the XXXX XXXX XXXX In retaliation to me filing a complaint a " manager '' from Square named XXXX stating that my funds would be held indefiinitely. Which to me means this is basically a criminal organization that steals money from small business owners. The payments weren't paid to Square customers paid with credit and debit cards with the expecation that I was getting paid for services rendered. Square has no right or reason to just keep my money and I want my money because its not THEIR money its MY money! I'm attaching pdf copies of all the emails I've mentioned as well as a screenshot showing that I have no disputes and my balance."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Other problem"
} |
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 has failed to remove all of the false reports about that I owe any more money to this student loan. I have forwarded them documents signed by the Federal Student loan representative stating that the loan is paid in full on XX/XX/XXXX. These documents are certified and sworn to in federal court. It is crystal clear.
My attorney and myself have reminded and resent this documentation several times to the student loan people. They refuse to acknowledge it and are breaking the law by continuing to pursue me. XXXX is now an accomplice in harrassing me now that they have seen the clear evidence that I do not owe this money. I will attatch my documents that clearly state this loan is paid in full.
They are just now sending me to colletions despite the fact that this has been going on since XX/XX/XXXX. I have always disputed this debt vigorously, and they will leave me alone for a while, but they just keep coming back. It is now XX/XX/XXXX and I am still disputing it."
}
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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "On XX/XX/XXXX I submitted a Payment for the final amount of {$42.00} it was processed on the following day, On XX/XX/XXXX they took another full payment of {$68.00}. I called them on XX/XX/XXXX to get the problem resolved. I was told that the payment would not stop until I called them, so they would have continued to take monthly payments every month had I not called on the XX/XX/XXXX but it was to late and they could not refund the amount taken on XX/XX/XXXX even though they were paid in full. I again tried to contact them by email and again on XX/XX/XXXX with no avail they keep insisting that They do not owe me anything but the {$19.00} which would have been the only over payment if I had not paid the full amount on XX/XX/XXXX. So now I am going to have to go to my bank and have them refund my money to me that they will not an go through the whole aggravation of fixing their mistake. It is not fair nor is it right that this is how they conduct their business. Thank you for your payment. Below you will see the email sent to me. Due date of XX/XX/XXXX2018 paid on the XX/XX/XXXX. I removed account information for privacy reasons. Their should not have been any other payment due on the account or any more money take from my account.
This email is to confirm your payment submitted on XX/XX/2018 for your Preferred Credit account.
Confirmation Number : XXXX Payment Amount : {$42.00} Scheduled Payment Date : XX/XX/2018 Amount Due : {$42.00} Payment Due Date : XXXX-2018 Account Nickname : XXXXXXXX Routing Transit Number : XXXX Account Number : XXXX Account Type : Checking Account Category : Consumer If you have questions about this payment or need assistance, please view the payment online at http : //www.preferredcredit.com, or call Customer Service at ( XXXX ) XXXX.
Thank you for using the Preferred Credit electronic payment system.
( A friendly voice when you need it - easy, self-help options when you don't. )"
}
Output:
{
"Issue": "Problem with the payoff process at the end of the loan",
"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": "Medical debt",
"Consumer complaint narrative": "Several weeks ago, I received a call from Affiliate Asset Solutions. They claimed to be collecting on a medical bill from a XXXX emergency room. They told me the emergency room sold my debt to their client, Pendrick Capital Partners. I asked for something in writing providing info about the debt and documentation showing that Pendrick actually purchased the debt from the hospital. I received an account statement in the mail, but I still was not provided with documentation proving that Pendrick owned the debt. I can't be expected to pay someone if they can't prove the validity if what they are claiming. Today, I received a call from a very hostile woman at Affiliate. When I explained that I still didn't have anything showing that Pendrick was the rightful owner of the debt, she yelled at me and told me if I needed to know who Pendrick was I should call them. I told her I was disputing the debt because I didn't know who the alleged owner of the account is and hadn't been notified that an account ( which I still don't know what it's for ) of mine had been sold. I told her that I knew she has a obligation to help resolve disputes on debts they are trying to collect. She yelled that I had an obligation to pay my bill and then hung up on me. I called back and asked for a manager to try to resolve the matter. The manager yelled at me as well and was not willing to provide proof of who the current creditor is for the alleged account. I ended the call because the manager wouldn't stop yelling and wouldn't help resolve the problem.
I immediately sent an email to Affiliate Asset Solutions ( at the only email address I could find on their website : [email protected] ) requesting that they cease and desist all communication with me. I should not have to deal with degrading hostility when I was just trying to get proof of the validity of the debt."
}
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": "You broke the law Pursuant to 15 USC 1681 ( a ) ( 1 ) The banking system is dependent upon my consumer report being fair and accurate. After taking a look my consumer report I realized that my report was inaccurate and this inaccurate report is unfair to me because I was never notified when this inaccurate information was furnished and I was never notified when during your companies initial investigation that you were going to report this information.
Pursuant to 15 USC 1681 ( a ) ( 1 ) This grave error has undermined my confidence in your companies procedures and as a result of this I will no longer be extending credit and if this information is not removed I no longer want my information reported in your companies database.
Pursuant to 15 USC 1681 ( a ) ( 2 ) - Your company is a powerless elaborate mechanism. This mishap that your company has caused has ruined my reputation and it has adversely affected my credit worthiness.
Pursuant to 15 USC 1681 ( a ) ( 3 ).
Your company assumed this role. You were not given express permission by me, the consumer, or a competent court of jurisdiction. So, not only did you fail in 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 that this information to accurate and you did not exercise your grave responsibility with fairness and impartiality. You took one side of the story and never consulted with me to find out if this transaction even took place. As a result of this my privacy was breached and this violated my right to privacy.
Congress makes it clear that a consumer report should NOT include transaction between the consumer and the person making the report. This alledged transaction that you are reporting, and failed to investigate, whether or not it factually took place, can be categorized as a transaction that took place between the consumer, and the person making the report. Which means it should not included i nmy consumer report and in fact is a violation of my consumer rights In your initial investigation I was never given the opportunity dispute."
}
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": "Medical debt",
"Consumer complaint narrative": "I have informed the Credit Reporting Agencies on XX/XX/XXXX of the inaccuracies and have asked them to have the Collection companies remove them from my report. I explained how I had medical insurance at the time and the insurance company paid 90 % of the bills. However, they refused to pay these smaller bills claiming I had secondary insurance ( which was inaccurate ) Now, I still have not heard back from XXXX regarding my request.
Plus, my other complaint is by filing a complaint/request with XXXX they have lowered my score by over 20 points. I do not understand ...
XXXX XXXX XXXX XXXX ( Collections ) XX/XX/XXXX - {$1100.00} XX/XX/XXXX - {$1000.00}"
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "I went to pay my mortgage thru XXXX XXXX like i usually do and was told that XXXX XXXX could not accept my payment and that i needed to call my mortgage company to find out why.So i call NationStar and they told me my mortgage was raised because my city taxes went up.I informed them that i had not received anything saying my monthly mortgage payment had went up and that my taxes had not went up. But was told i had to pay almost 300.00 more than my usual payment. I then asked if i could pay the {$1000.00} right then and get the rest and pay that later and was told i could make my full {$1400.00} payment on line if i got it before midnight.but when i tried to make it online I wasnt allowed and the next morning when i called they told me it was a new month which put me 3 payments behind and i had to pay 3 payments to catch up or id go into foreclosure"
}
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": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "This situation is puzzling to me because I have never been to any hospital in Florida or anywhere close to Fl on the date of XX/XX/2015. Although I have lived in Florida to take care of my mothers when she had XXXX XXXX but never to any hospital.
XX/XX/2015 was not in hospital The amount of {$850.00} is not mine because I was not there.
Also I have reason that the social security you have on file isn't correct with mine, because on Step 4 where it asks for account number or last 4 digits of social. As I enter my last 4 it does not let me proceed which gives me reason to believe that the social you have on file is also incorrect which would make sense because it was never me there at hospital. " Identity Theft ''"
}
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 reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "From XX/XX/2021 thru to XX/XX/2021 Transunion has manipulated my credit report. Now, it appears that Transunion have inserted an unknown erroneous COLLECTION account by XXXX XXXX XXXX XXXX with an original creditor as " XXXX XXXX '' to my credit profile and refuses to remove this damaging information. As previously stated this account is not mine and does not belong to me. It seems as XXXX XXXX XXXX XXXX have not reported on this account since XX/XX/2021, more than five ( 5 ) months ago XXXX XXXX third party credit report included. Transunion hurts and damage consumers daily. These treacherous, malicious acts must stop."
}
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": "Medical debt",
"Consumer complaint narrative": "I had to attend XXXX XXXX in Florida when I only had XXXX XXXX insurance so I had to go to the XXXX XXXX to be covered. The visit should have been covered by insurance and wasnt XXXX XXXX still billed me. I ended up paying it and I have a letter with proof of payment. This collection company will not remove it and then Insists I still havent paid the bill. This should be covered by the surprise bill act. But it is paid in full."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "These are the addresses listed that I do not live at, have never lived at, and does not belong to me. Look below to see address in question.
( 1 ) XXXX XXXXXXXX XXXX XXXX, TX XXXX ( 2 ) XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX ( 3 ) XXXX XXXX XXXX XXXX XXXX, TX 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": "Bank account or service",
"Sub-product": "Other bank product/service",
"Consumer complaint narrative": "A terrible decision caused my account to be gone forever from TDbank.A fraudster got my debit card information.There were unauthorized deposits and fake checks deposited into my account.The bank fraud department took too long in investigating.I did pay off what was owed but the bank showed hatred against me.I think they stopped the investigation.They may be also slandering me .I did not know what to do.I have been told that I am to get refunds from what was stolen even if it was done in an irresponsible way.The bank has to realize that I am no criminal.They also need to know that in this digitalized time banking information can be gotten by anyone.I want to know why they feel they do not have to follow the law and why they think I should pay for what happened.They also need to be questioned as to why they allow these fraudsters to keep stealing money from people 's accounts.The bank also insisted on saying that they do not care what happened and that I should have known better.I also want them to know how this made me feel.I had been with them since even before they became TD.Back then they were more responsible.They did work with customers on these problems.They advised on how to be careful with these problems.They are possibly trying to say that I conspired with this person who seriously damaged my account in order to steal from them.I am no criminal.I do not need theirs or anyone elses money to resort to doing such a fraudulent thing.They really do need to be called out on how they pretend to investigate bank fraud but really do not.It is due to this stress that I was not able to pay what was owed until my tax refund came through.I also want them to know that I am not the only one that this has happened to.It will continue as long as they allow the fraudster who they are aware of continues to do this as long as they are not caught.I have to say that they may be working with this fraudster.I had not received any help with payments from TDbank until a few days before they made some charge off and then claimed they gave me a credit.They allowed me to open a new account but then they closed it.They will tell me the results of what happened.I have the right to know that."
}
Output:
{
"Issue": "Using a debit or ATM card",
"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": "I purchased and closed on my home XX/XX/2014. To my surprise i just received a letter from my servicer that there is a {$4000.00} shortgage to cover the taxes and insurance. I contacted the title co who prepared Hud 1 and I contacted the Wells Fargo who preparedthe closing documents. Each are blaming one another. My mortgage payment is {$1000.00} and now going to increase to over {$1600.00} to cover the shortage. I would like to know how in the world was i even approved for this loan? My husband is XXXX years old and im XXXX. This home was a flip home and i never met the seller. I believe they purposely kept the correct annual taxes amount hidden from me because when i would ask them during the process " what are my taxes '' they would just say " oh about {$350.00} a month ''. I believe they did this cause i didnt qualify for the actual amount. I come to find out after 1 year my taxes are {$7100.00} a year as opposed to {$4200.00}. I am now in a position that i may have to sell my house. This is a huge burden for us!!"
}
Output:
{
"Issue": "Loan servicing, payments, escrow 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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I went in branch and closed my account in XXXX. Got my ledger too! And the check for my funds.
The account is not closed! They just gave me a fee so I'm at negative balance somehow!
I even have all calls with them recorded about closing the account."
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Company closed your 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": "I have paid down all my credit cards on XX/XX/XXXX and my credit card companies have sent an update to XXXX, Experian, and XXXX but they have still not posted the update to their reports - I have called each one and have asked them to update but they make me go through the process of doing a dispute rather than just publishing the new information. I need them to update it as I have to raise my score as much as possible to get a mortgage. I have even had the credit companies send letter to them - attached are copies of the letters"
}
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": "Medical debt",
"Consumer complaint narrative": "They call all hours before XXXX after XXXX. Insurance did not pay as they should so paid debt in full both {$740.00} plus {$72.00}. It was paid on XX/XX/XXXX cleared on XX/XX/XXXX. Paid by credit card. I have proof. Called company and disputed with XXXX and XXXX"
}
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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "Throughout the time my account has been with Tower Loans, my account information has been shared several times with other employees at my job and residents in my home. The representatives do not verify any account information before sharing the details of the account. I have had 2 co workers approach me about my Tower loan account.
Tower loan reps are calling 3/4 times several days within a week. The calls have come in durations of 10 minutes to an few hours apart. There has been moments I call for account information or resolution, but still about receive 2 calls throughout the same day. This account has never been completely abandoned account, if I couldnt make full payments I paid partial, ls, than extra to bring it currents after hardships arised.
In efforts to bring the account current after, I made an automatic payment arrangements for {$250.00} and wrote several post dated checks up to XX/XX/XXXX for an additional {$150.00}. The {$250.00} covered regular {$210.00} payment and extra {$150.00} cover any past due amounts. It was setup this way because the system wouldn't allow them to setup 2 separate automatic payments within a single month. During this time I was still receiving at least 2 calls per day stating my account was past due and needed to setup arrangements. Each time I explained, that it had already been done and the checks were physically in the office. Each time the representative stated that my account had not been noted of those arrangements and repeatedly demanded payment. There were a few incidents where the representative told me, " if you would pay my bills, they ( tower loans ) wouldnt have to keep calling you, next step is we will sue you ''. Everytime an incident occurred I spoke with a current General Manager that promised the account would be updated to prevent further " human error '', needless to say those situations repeated itself nearly every month. Despite making on and off payments to bring the account current. it had been sent to collections and they are still continuing their aggressive phone and home visits.
Today XX/XX/XXXX, I missed their call, I called the office back within 7 minutes, spoke with a representative, then within 3 hours another representative arrived at my door.
The company has to be held accountable for harassing customerd and sending representatives to your home,, especially during the pandemic."
}
Output:
{
"Issue": "Problem when making payments",
"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": "Medical debt",
"Consumer complaint narrative": "In my opinion, this company is sending collection letters with threatening language claiming a negative credit reporting 'will ' remain on credit records for several years. The letter also generated a false sense of urgency like the sentence : Warning! This matter is very important to you and to your credit report. This is the text for a medical debt collection, the language could have been more emphatic.
Full disclosure : The concerned debt didn't exist at first place ( false reporting to original creditor ) so no debt resolution or contact with collector was required.
There is also a potential debt trap as they are asking for credit card details to repay the debt. Its potential because it is not clear if in the letter they are referring to VISA, Master Card etc for debit card or credit card is also accepted In my case this was an erroneous reporting but it hurts to learn that many other less fortunate who are already suffering are getting similar harsh and potentially misleading communication."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "It appears that my credit/my file/my credit file has been compromised. Again, I was going through my records & noticed many information, accounts or items which do not belong to me. Since Im a stickler for research, I found that under section 605b of the FCRA you are required by law to remove & block any accounts or information which is found to be opened due to identity theft. The dispute items do not belong to me. Im attaching the required FTC Report for you and the bank 's records ( learned through more research both parties require ). Please block/remove these files. I found that under section 605b ( a ) Equifax and XXXX 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 4 business days after the date of receipt and it requires the following documents and statement to remove the disputed accounts on my file : ( 1 ) Appropriate proof of the identity of the consumer ; ( 2 ) A copy of an identity theft report ; ( 3 ) The identification of such information by the consumer ; and ( 4 ) A statement by the consumer that the information is not information relating to any transaction by the consumer.
The dispute items do not belong to me. Im attaching the required FTC Report for you ( which complies with FCRA Section 603 ( q ) ( 4 ) which states ( 4 ) The term " identity theft report '' has the meaning given that term by rule of the Bureau, and means, at
a minimum, a report ( A ) That alleges an identity theft ; ) ( B ) That is a copy of an official, valid report filed by a consumer with an appropriate Federal, State, or local law enforcement agency, including the United States Postal Inspection Service, or such other government agency deemed appropriate by the Bureau ; and ( C ) The filing of which subjects the person filing the report to criminal penalties relating to the filing of false information if, in fact, the information in the report is false. ) 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 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX
If you feel there is a possibility these accounts belong to me I will require all documentation that bears my signature ( another research item I found that requires you to verify with 100 % accuracy that each account is 100 % true, accurate, correct, complete & VERIFIABLE ). If you do not provide any records & claim the item is verified please understand I will seek legal action. I do not take kindly to games nor does my attorney. SO please comply with the law so this matter can be handled swiftly."
}
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": "In Accordance with the Fair Credit Reporting Act ( 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 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX XXXX XXXX XXXX XXXXXXXX has violated my rights. 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 also states a consumer reporting agency can not furnish an account without my written instructions."
}
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": "A collection agency named Bull City Financial is currently reporting 43 individual and separate medical collections on my credit profile. I recently contacting Bull City to start paying these debts one at a time, however Bull City Financial would not let me choose which accounts I can pay off, and there is no way that I can pay 43 accounts at one time. They told me that anything that I paid them would go towards the grand total of the 43 accounts, and that none of them would be updated as paid until all of them are paid. I told them that this was illegal, but they refused to accept payment on the individual bills. Since Bull City will not accept payment on individual accounts, I want them to combine the total amount owed to their organization in one collection account on my credit report, as opposed to 43 separate and individual listings. I had XXXX, so all of these bills should have been paid already anyway."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account information 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": "XXXX has failed to address my concerns about being deceived into accepting a repayment plan that resulted in a large past due balance as well as negative marks reported to my credit.
I incurred a large past due amount because I was misled deceived and tricked by XXXX into accepting a repayment plan they proposed. After I accepted their plan, payments were sent equal to the new lower monthly amount proposed by XXXX. My Credit Counseling Agency sent these payments on my behalf.
To reiterate, I am the one who spoke directly with XXXX, over the phone, verbally accepting their repayment plan. During this conversation that I verbally accepted the plan, XXXX agent proposed I pay a SUBSTANTIALLY lower monthly payment, a payment I was told will become my new monthly payment. This was the ONLY thing communicated to me in this call about the repayment plan and then the Agent asked me, " Do you accept this new monthly payment amount ''.
I later learned that I was not told the full terms of the plan. In XX/XX/XXXX I called XXXX because I saw a series of negative marks on my credit report.
During this XX/XX/XXXX call, after a long series of prodding questions to their Agent, I finally learned the exact terms of XXXX repayment plan. I was told by an agent that the new lower monthly payments I had begun to make did not satisfy my monthly payment because they were not the full amount due under the initial terms of the loan and I would be reported as past due every single month as long as I was under this plan. Once the agent told me this, I told her that XXXX DID NOT tell me this would happen to me. I told her only thing XXXX did was propose a new low amount and then verbally asked if I accepted this new lower monthly payment with a Yes or No.
I then immediately terminated the plan. I also was told during this call that the amount due in XX/XX/XXXX was not included in the plan and it was also past due. Again, I was never told it was not part of the plan when I initially accepted the plan.
For clarity, XXXX was contacted in late XX/XX/XXXX, by my Credit Counseling Company on my behalf seeking repayment options. This occurred in late XX/XX/XXXX at my direction, only a few days past my due date to set up some type of plan because I could not afford to pay my XX/XX/XXXX payment. XXXX provided a response to my Credit Counseling Company yet failed to implement the plan for some reason.
I subsequently called directly to XXXX because they had failed to fax over everything needed to my credit counseling company and/or was not responding to their calls. Again, at this time XXXX failed to mention that XX/XX/XXXX amount wouldn't be part of the plan. It was also at this time XXXX proposed to me a lower monthly payment and asked ME directly if I would accept this lower monthly payment as discussed above.
XXXX failed to tell me when they proposed to me a new lower monthly payment that I would be reported past due every single month even if I paid this new lower amount every month. XXXX failed to tell me they would not include the XX/XX/XXXX amount due in the repayment plan. Net credit ONLY told me about the new lower amount, an amount which is substantially lower than the regular monthly amount, which enticed me and mislead me into accepting the plan Had I known the full details of the plan proposed by XXXX, I would have opted for a plan that would not result in negative notations and I would not have accepted the plan they proposed. No reasonable person would put themselves what I have been through by XXXX and NO reasonable person would pay a monthly payment that would result them to be reported negatively. There are still years remaining till full repayment. Meaning under their plan I would be reported negatively to my credit for years. No one would accept derogatory marks EVERY MONTH for YEARS even though they were paying exactly what was proposed by the creditor.
The negative marks are inaccurate because they are based upon misleading and deceiving statements made by XXXX agents. XXXX XXXX failed to fulfill their duty to inform their customer of the full terms of the plan, even though I was on the phone with them and they could have. I was deceived by XXXX and I should be compensated by XXXX with the removal of these marks.
When I complained i XX/XX/XXXX about this, the agent said " I should have asked '' and they didn't have to tell me the full impact of their plan. That is absolutely crazy!
I was right there, speaking directly to them on the phone and all they had to do was let me know what exactly I was agreeing to and that I would still be past due for XX/XX/XXXX under their plan. But for some reason the company believes they didnt have to share any of that.
I struggled to pay this past due amount and now I am hoping the damage done to my credit by XXXX inaccurate reporting can be resolved by XXXX.
The entire purpose of a repayment plan is to pay what is due on the amount and so that moving forward, payments will be more affordable."
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Suntrust close my account after I told them not too. And on top of that I still have not received my new check card.. Im XXXX and this bank should be fined. As a customer I reserve the right to say not to shut down my card I was going to call back. But of course they did it anyway.."
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Company closed your account"
} |
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