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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": "Mortgage",
"Sub-product": "Other type of mortgage",
"Consumer complaint narrative": "We purchased our home in XXXX for {$100000.00}. In these 18 years we have absolutely nothing to show for it because we now owe {$160000.00} because of mortgage fees, etc. Our loan has a huge " Balloon '' payment due at maturity. In XXXX we were granted a modification with Ocwen. At that time, we were told that Ocwen would " forgive '' roughly {$40000.00} of our " underwater '' mortgage if we kept up with our payments for a specified time. This " forgiveness '' was part of a Settlement that Ocwen had to comply with. During this modification, we lapsed in payment and were told that the " forgiveness '' was null and void. A couple of months later, their lawyer contacted us & scheduled " meetings '' with the court over the phone with me. During those meetings, Ocwen decided to offer a new payment ( {$830.00} ) to us which was {$250.00} HIGHER than our previous payment. I brought this to their attention on the phone meeting with Ocwen, their lawyer and a court magistrate. I was told by the Ocwen representative that the higher payment was because they were purposely punishing me because of the default on our loan modification. I was dumbfounded. Their lawyer and the magistrate heard this as well and I'm sure this conversation was recorded. We tried making this higher payment but ran into problems when both of our vehicles needed repair. We ended up having to replace one of them and, since we have bad credit, are paying a very high car payment because no other options were available. We both work in different cities and having 2 cars is a must so we can work and provide for ourselves as well as our XXXX children. We're tired of being bullied by Ocwen. We now owe {$160000.00} on our {$100000.00} XXXX XXXX XXXX on XXXX of an acre which is in so much need of repair that we can't afford that either. We want to stay in our home. We want to be able to COMFORTABLY afford the payment. We don't feel we should be penalized by Ocwen any longer. We want the settlement that is owed to us. We want a lower payment. We want to be heard."
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "On XX/XX/XXXX my account was closed due to an error by unemployment and I had available remaining funds that was deposited into my account betweenXX/XX/XXXX from unemployment. I made few purchases and a withdrawal that day I had issues with chase prior with their online app banking and I filed varied complaints about not having access to my statements and how it goes blank that wasnt resolved at all. Finally on XX/XX/XXXX or XXXX I got a letter stating my account will be close if I dont show proper documentation of payments so I went to the nearest branch and spoke to a banking manager which they made a copy of both my statement and Id and still they closed my account do to error on unemployment who states the funds are mines if they dont release them either send it back to unemployment or give it back to me."
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Funds not received from closed account"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I have a XXXX MasterCard issued by US Bank. I have been making monthly payments on it regularly. Recently, as I continued using my credit card I did not realize that I had reached the credit card 's limit. But, instead of declining the card, US Bank allowed the transactions to go over the limit by more than a thousand dollars before stopping.
Now according the the monthly credit card statement, US Bank wants me to pay the amount over the limit IN FULL and AT ONCE.
I am able to make monthly minimum payments plus a little extra without a problem, but it seems rather fraudulent that a bank will let your credit card go over and then insist that you pay that whole amount in full. Isnt that the purpose of having a limit on a credit card so it stops when you've reached it?
There used to be Overdraft Fees by the banks, now it looks that the have brought the same trickery to their Credit Cards too.
I am a XXXX. I graduated from XXXX XXXX last month and successfully passed my XXXX. Last year or so, I have worked part-time as a XXXX XXXX and have been on the front line of XXXX XXXX along with my fellow brave XXXX. I have worked and studied very hard to get to where I am and having recently graduated, I am looking forward to start working as a XXXX XXXX myself.
Every one calls us Heroes and thanks us for our sacrifices, but blink or get too busy with your job of helping other people or fail to check your bank statements properly, and fall slightly behind in your payments and there is another financial institution ready to take advantage of you."
}
Output:
{
"Issue": "Struggling to pay your bill",
"Sub-issue": "Credit card company won't work with you while you're going through financial hardship"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Bank of the West has been double charging for my use of a debit card to withdraw from a NON BOW ATM during the month of XX/XX/XXXX. I understand that I may be charged by a fee for withdrawal of cash from the non bank atm by the owner of the atm and also I understand that Bank of the West will charge {$2.00} for using a NON BOW ATM. What I do not understand is that Bank of the West has charged for balance inquiries on top of the withdrawal service charge in effect doubling the cost per transaction to {$5.00}. I have no need to use a NON BOW ATM for balance inquiries as I have a BOW telephone app that provides access to my account for free. Also Bank of the West does not list any of the alleged balance inquiries on my statement but merely stated a Service charge fee that is double what it should have been. My XX/XX/XXXX statement shows 11 withdrawals from a Non bank Atm. Therefore the Service charge should have been a total of {$27.00} and not {$55.00}. The bank responded by stating that the addition fees where for balance inquiries providing me a list of dates that inquiries were made twice. They have not provided me more detailed information as to the times that such inquiries were made or if they were made at the same time as the withdrawals. Furthermore I see that balance inquires on four occasions were made twice on days that money was not withdrawn from the Non bank Atm. The bank has so far declined to credit bank to my account for the {$27.00} overcharge."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem using a debit or ATM 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 reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I called to apply to an emergency loan of {$1000.00} that the XXXX XXXX XXXX have advertised and I can't process it in their online webpage as other applicants. I then wrote y XXXX XXXX and sent the application submitted that in the first attempt gave me an error, I believed that they are making physiching with the people that visit their webpage but I applied again. Then I received calls of the institution and received an email of the manager requesting me application documents, I sent and never told me neither notified me that the emergency loan was denied. I checked my TransUnion credit report and saw their institution listed on my inquiries they denied the loan based on my credit report during a pandemic disaster. I am a natural declared disaster and declared disaster survivor and my credit reports were impacted because the banking institutions making unauthorized charges after closed accounts in the middle of the disasters and did not provide me any payment arrangements neither moratorium, I make several disputes and submitted statements but the institution used my credit report to denied my loan. They are not approved my loan in less favorable terms than other applicants, in discriminated actions against me."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Someone filed a change of address in my name to an address in XXXX Texas. Some of our utility and credit card bills along with our mortgage statement went to the new address. Using the information in them they applied for, and received, credit cards from US Bank, XXXX XXXX and XXXX XXXX XXXX. They also applied for a card from XXXX XXXX which I disputed before the card was issued. We have contacted each of the banks involved."
}
Output:
{
"Issue": "Getting a credit card",
"Sub-issue": "Card opened as result of identity theft or fraud"
} |
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": "STATUS Response disputed XXXX Credit Card said : Explanation of closure XXXX XXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX : Consumer Financial Protection Bureau - Case # XXXX XXXX account ending in XXXX Dear XXXX . XXXX : I am in receipt of your complaint filed with the Consumer Financial Protection Bureau and forwarded to XXXX Executive Office. As a liaison in this office, I was asked to review and respond to your concerns on behalf of Department XXXX XXXX XXXX ( XXXX ).
In researching your complaint, I found that on XXXX XXXX, XXXX, one of our representatives sent you the attached letter validating the debt on your XXXX account noted above. With this letter, we also mailed copies of the monthly billing statements for XXXX XXXX, XXXX, through XXXX XXXX, XXXX.
In summary, our records indicate that the account was opened on XXXX XXXX, XXXX, and closed on XXXX XXXX, XXXX, by the creditor, XXXX, because payments were not received as required by the terms of the XXXX Credit Card Agreement. The last purchase was made on XXXX XXXX, XXXX, and the last payment was received on XXXX XXXX, XXXX. If you have record of additional payments made on the account that did not appear on your statements, please send copies of the front and back of the cancelled checks or the bank transmittals for further investigation.
We show that all monthly statements were mailed to you at the address you provided as your billing address and there is no record of returned mail, which suggests that the monthly billing statements were delivered.
By using your account, you consented to the terms of the XXXX Credit Card Agreement, which governs the use and reporting of this credit account, and includes your promise to pay for all purchases as well as late fees and interest charges. Any delinquencies reported for this account were reported in accordance with that Agreement. It is the Bank 's policy to accurately report the account 's history and payment performance to the credit reporting agencies. Since the information we are reporting is correct, we are unable to delete the account from your credit report. On XXXX XXXX, XXXX, we sent a request to the credit reporting agencies to report your account as " customer disputes reporting. '' The account was charged off on XXXX XXXX, XXXX, with an outstanding balance of {$220.00}. It is the responsibility of the credit reporting agencies to remove information from credit reports. They will remove transaction activity or delinquent information from a credit report seven ( 7 ) years after the date of the first delinquency on the account that led to the account charging off. The balance will remain on our records.
XXXX . XXXX, if there are any further questions or concerns, your account is currently being managed by Northland Group , Inc. Should you need additional information on the account or wish to make payment arrangements, please Northland Group , Inc. directly at XXXX.
Sincerely, XXXX XXXX XXXX Executive Office XXXX Extension XXXX Enclosure cc : Consumer Financial Protection Bureau Response Additional time is needed to complete our investigation.
WHAT HAPPENED Describe what happened so we can understand the issue ...
CREDIT CARD BILL WAS PAID.
XXXX This is about a ( n ) Debt collection : Credit card Which of these best describes your issue? Cont 'd attempts collect debt not owed : Debt was paid DESIRED RESOLUTION What do you think would be a fair resolution to your issue?
THEY SHOULD DELETE CHARGE OFF FROM MY CREDIT REPORT.
CONSUMER INFORMATION Contact information Mailing address XXXX XXXX XXXX XXXX XXXX TX XXXX United States Phone XXXX XXXX XXXX Age XXXX Last XXXX digits of SSN XXXX PRODUCT INFORMAT"
}
Output:
{
"Issue": "Cont'd attempts 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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "XXXX XXXX XXXX and XXXX submitted a credit report adjustment to XXXX, XXXX and Equifax in order for them to remove the negative report. XXXX and XXXX did just that. However, Equifax did not."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Account 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": "Debt collection",
"Sub-product": "Medical",
"Consumer complaint narrative": "A collection agency illegally re aged one of my accounts.
XXXX XXXX.
XXXX, NV XXXX"
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Frequent or repeated calls"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX should not be reporting to Equifax ; XXXX and XXXX that I am ( XXXX ) days late on the XXXX XXXX XXXX partial account number XXXX. ( Please see page attached from my credit report. ) I was granted payment arrangements on this account via the CARES Act. XXXX XXXX XXXX agreed to the terms of the payment arrangements and assured me that this account would not be in jeopardy under our agreement. I was also told that no late payments would report. XXXX XXXX XXXX did not honor our agreement and consequently they reported this account to the bureaus as being late. It is a violation of the Fair Credit Reporting Act to not report accurate information on a credit report. This false reporting of lates is seriously harming my credit score and these lates need to be removed. I have contacted XXXX XXXX XXXX as well as the bureaus to fix this however, I have not been successful."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I disputed an account ( XXXX XXXX XXXX listed on my credit report. I have been disputing it for a month. Its not my account. i have submitted my lease, my electric bill and various other evidence showing i never lived at the the address XXXX XXXX is trying to collect for. Experian has chosen to ignore all the evidence, plus the fraud alert and multiple notices saying my SSN has been compromised from their own company and kept this fake account on my record damaging my credit. I also have spoken with reps throughout this process and submitted information. I spoke with a rep on XX/XX/20 who stated that the creditor XXXX XXXX had not responded. It should have been deleted when I was on the phone. Experian is violating the FCRA by keeping this account on my credit and not investigating."
}
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/XXXX my PayPal business account was solicited by PayPal to apply for credit or Bill me Later I applied for credit under the business information. ie business Fed Tax Id, business address, business phone, and other business information. We received a credit response immediately and received credit of {$5000.00}. We have always used this credit for online business expense and business purchases made online. At no point have this ever been considered a personal liable credit.
In XX/XX/XXXX, PayPal had sent me an internal notification to verify the business account to comply with new federal regulations, by adding my social security number to the account. But this was simply for PayPal only and was supposedly at no point in connection with the paypals Bill Me Later line of credit. I reluctantly agreed and complied.
Almost 2 years had passed and approximately XX/XX/XXXX I had a major credit report hit from PayPal on my personal credit. This has come to a shock to me as i was 100 % this was a company credit card and had been applied and in no way requested or issued as a personal line of credit.
I contacted PayPal to advise them of the error and that I had never even applied for a personal credit line and had ONLY applied with our business credentials and in turn received a decision based on my business credentials. I demanded a copy of the original application to prove my case. they advised me that they no longer handle credit and referred me to Synchrony Bank, which took over all PayPal credit accounts in XXXX of XXXX.
I contacted synchrony bank and they advised me that this couldnt possibly be a business account or be switch back to a business account because synchrony bank does not provide business account. They advised me that they also couldnt provide me with the original credit application or credit agreement because they werent the original creditor and advised to reach out to PayPal.
After 2 weeks of back and forth phone calls and disputes filed with credit reporting agencies, I felt I have no recourse.
Today XX/XX/XXXX I received a letter from paypal that my original application that would prove that the credit was ascertained with business credentials and was in fact a business liable account, has conveniently disappeared.
The facts remain, the credit was applied through my business credentials, at no point have their ever been a missed payment or late payment. This account has always been in good standing. Once Synchrony Bank took over PayPals credit accounts, my business credit account automatically turned into a personal liable credit account. And neither company can or is willing to produce the original credit application or customer agreement to show that this was originally a business account. All they claim to say is only because my name only was associated with it I have become personally liable."
}
Output:
{
"Issue": "Getting a credit card",
"Sub-issue": "Card opened as result of identity theft or fraud"
} |
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 a victim of identity theft, this company has continued to report to credit and collect on a fraudulent debt. I have disputed with this company 3 times, they now deem my disputes as " frivolous '' and refuse to accept any documentation on my behalf. I have disputed these accounts over and over again with XXXX . I had my ID stolen at age XXXX , my social security cards, and my ids. They opened numerous insurance accounts, medical, apartments, auto loans, and credit cards, all defaulted on. These debts do not meet FCRA or FCDPA standards. Attaching police reports, and original as well as updated. This debt has already been removed by XXXX and TransUnion because it was deemed fraudulent. I never attended said " school ''. This is a fraudulent account that I did not authorize. Does not meet FCRA OR FCDPA requirements. This account must be removed from all credit reporting agencies."
}
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": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "Over the past several years, Wells Fargo Home mortgage has been difficult to deal with when it comes to my Mortgage Loan.
I did address my situation with Wells Fargo Management. I made it clear that am the Single owner after my XXXX died. Since then Wells Fargo lowered the interest rate to 3.350 % per annum.
In light of Covid-19, effective XX/XX/2020, Wells Fargo has raised the interest rate to 3.875 %. Subsequently, I contacted Wells Fargo by phone and e-mail. When I inquire about with new rate, the Customer Service agents only response is I am Sorry. This response is a false apology by Trained Agents. I followed my my complaint by e-mail and later Wells Fargo did block me from contacting them by e-mail.
Before the rate increase, my monthly payment was {$1600.00} and effective XX/XX/2020, the new payment is {$1700.00}. I also had to pay a large amount to pre-pay to my Escrow Account to lower my new monthly payment.
Since then, Wells Fargo has been suggested to me with two options.
1. Option 1. Forbearance. I will not accept this option. This tactic is done by Wells Fargo to put more Burden and pressure me to Accept this option. This method is good for Wells Fargo to eventually put me to Foreclosure Status.
2. Option, 2.. Refinance : I will not accept this option at all. I am not going through refinance process. I do not have funds to pay for closing costs and I hat e to put myself to Hard Inquiry credit process.
I will Accept the old 3.75 % and I request Wells Fargo Restore the old rate ASAP and refund me for the payment I made for the month of XX/XX/2020.
I am XXXX years old and an XXXX American I is the Pattern of Wells Fargo not to help Minorities with Lower Interest rate.
Wells Fargo wants to Drain me for longest period by Using Forbearance & Refinance Options. This Racial Tactic is designed to Minorities. It is to discourage us Minorities not to Won the American Dream.
A copy of the Complaint is being Filed with the State of XXXX Office of The Attorney General, Consumer Protection Division.
3."
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit repair services",
"Consumer complaint narrative": "Sensitive information was divulged without permission due to hack. Company ( Equifax ) internal security is flawed and inadequate leading to breach. I am currently in military active-duty stationed in XXXX XXXX and TrustedID credit monitoring service for 1 year does not seem adequate ; also terms of service limiting future legal rights/arbitration seems unethical and unfair."
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional fixed mortgage",
"Consumer complaint narrative": "Loan Officer directed Buyer and Selelr to schedule a closing for XXXX XXXX. Closing is scheduled. TCF bank has not answered phone calls or responded to emails regarding the status of borrower 's loan application for more than two weeks and closing is now 2 days away. Loan may possibly have been denied at underwriting level but since TCF is not corresponding / communication with buyer or buyer 's lawyer, we can not know for sure. Buyer is about to default on contract for purchase of property due to lender 's refusal to share this information"
}
Output:
{
"Issue": "Application, originator, mortgage broker",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Money transfer, virtual currency, or money service",
"Sub-product": "International money transfer",
"Consumer complaint narrative": "I have tried to send {$1900.00} to XXXX through Remitly on XXXX XXXX 2017 and they promised to deliver the funds by XXXX XXXX 2017, which did not happen. I called the customer support they said that are having technical issues and still need some time to deliver. I called on XX/XX/XXXX and inquired about this, they said still they are having issues. I wanted my money back, they are saying that it can not be refunded until 10 business days. This company seems to be fraud, I am worried about the money."
}
Output:
{
"Issue": "Money was not available when promised",
"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 Credit bureaus stated that on my account they did a proper investigation but how is that possible if the date opened is inaccurate, the date last active is inaccurate, and date last reported is not accurate and also date of last payment. This ground for removal.
they are continuing to violate my rights and this causing deformation of character because of the inaccuracies on my credit report.
They also violated my rights under : 15 U.S. Code 1681s section 8 ( 8 ) Expedited dispute resolution If a dispute regarding an item of information in a consumers file at a consumer reporting agency is resolved in accordance with paragraph ( 5 ) ( A ) by the deletion of the disputed information by not later than 3 business days after the date on which the agency receives notice of the dispute from the consumer in accordance with paragraph ( 1 ) ( A ), then the agency shall not be required to comply with paragraphs ( 2 ), ( 6 ), and ( 7 ) with respect to that dispute if the agency ( A ) provides prompt notice of the deletion to the consumer by telephone ; ( B ) includes in that notice, or in a written notice that accompanies a confirmation and consumer report provided in accordance with subparagraph ( C ), a statement of the consumers right to request under subsection ( d ) that the agency furnish notifications under that subsection ; and ( C ) provides written confirmation of the deletion and a copy of a consumer report on the consumer that is based on the consumers file after the deletion, not later than 5 business days after making the deletion.
They also violated my rights under : 15 U.S. Code 1681i section 5.
( 5 ) Treatment of inaccurate or unverifiable information ( A ) In general If, after any reinvestigation under paragraph ( 1 ) of any information disputed by a consumer, an item of the information is found to be inaccurate or incomplete or can not be verified, the consumer reporting agency shall ( i ) promptly delete that item of information from the file of the consumer, or modify that item of information, as appropriate, based on the results of the reinvestigation; and ( ii ) promptly notify the furnisher of that information that the information has been modified or deleted from the file of the consumer.
They also violated my rights under : 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.
And according to the FRCA 15 USC 1681 everything has to be 100 % Accurate. I have the credit report showing the inaccuracies.
These accounts are : XXXX Account Number : XXXXXXXX XXXX Account Number : XXXX XXXX XXXX XXXX Account Number : XXXX XXXX XXXX Account Number : 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": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "I have applied for refinance of my primary mortgage for 15 years on XX/XX/2020 with XXXX XXXX from US bank. They did a rate lock which was to expire in 3 months.
They took initial deposit of {$390.00}.
They collected some documents from me but did nothing, their loan office XXXX XXXX XXXX, just put me on hold for hours together while talking to someone else. After 4 months of doing nothing with my application the loan officer finally said that they have fired their Underwriter who was incompetent and now my loan application will move faster. I asked them to refund my {$390.00} so that I can get my refinance application somewhere else but they refused to refund the money. The lock period expired and the rates went down considerably but they refused to put another lock for lower rate. Now I am in this limbo where I do not know what I can do? The consumer should not be made to suffer if US bank choose to hire incompetent employees who can not do their work."
}
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": "Mortgage",
"Sub-product": "Conventional adjustable mortgage (ARM)",
"Consumer complaint narrative": "I purchased my house using a conventional loan in XXXX XXXX and decided to refinance it with XXXX XXXX bank. The broker at the time offered me an adjustable loan with the options of paying the minimal, interest only, or the full amount ; however, during our discussion he was less than transparent when I accepted the adjustable loan and did not inform me of the consequences of just paying the minimal, interest-only or the full amount when making my mortgage payment : to my dismay, {$1000.00} was attached to my principal balance each month when I paid anything accept the full amount with an interest rate of 8.75 percent.
To my shock and disbelief, I was devastated to find this out! My mortgage loan went from {$240.00} and accumulated to {$260.00}. That was an increase of {$20000.00} dollars attached to my principal balance, better known as negative amortization. In XXXX at the beginning of the recession XXXX XXXX sold my loan to IndyMac. I applied for a loan modification and worked with IndyMac for more than a year to modify my loan to a more affordable rate. However, IndyMac consistently delayed the process over and over again stating that I did not file the necessary documents in spite of me sending them the same documents they requested over and over again. Eventually, I just gave up trying.
Subsequently, I tried to short-sale my home and IndyMac also denied a short-sale. While losing my job IndyMac still refuse to work with me in granting a solution to enable me to keep my home. I also tried XXXX XXXX, and all other advocates to help me keep my home to no avail.
Because my home is not under XXXX or XXXX XXXX, no financial institution was willing to work with me to refinance my mortgage. Therefore, the only option that is left will eventually lead to foreclosure of my home that I lived in for over 11 years. As a veteran, who served his country for XXXX years, I am simply appalled how these financial institutions " thank me for my service '' by using unethical and predatory lending practices to cheat and fill their pockets of veterans ( and citizens ) with hope that the borrower will foreclose on his/her property to collect the insurance benefits and resell the home at market value to increase their profits XXXX folds."
}
Output:
{
"Issue": "Loan modification,collection,foreclosure",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX, Experian and XXXX XXXX Why do the below inquires appear on my credit report. Aren't you aware that you must enforce the permissible purpose provisions of the Fair Credit Reporting Act?
Prove to me that the inclusion of this inquiry complies with this federal law, or remove it now.
Experian : XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX and XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXXXX/XX/XXXXXX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXXXX/XX/XXXXXXXX XX/XX/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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have been disputing the lates on my mortgage account with Nations Direct since XX/XX/XXXX which was when I noticed the negative credit reporting.
I was marked 30 days late on XXXX, XXXX and XX/XX/XXXX when I was in an active, approved Forbearance plan which they have clear records of. I have notified them of this and they stated XX/XX/XXXX that they would be requesting the removal of these lates and nothing has been done.
On XXXX and XX/XX/XXXX I was also marked 30 days late for both months and both payments were NOT 30 days.
XXXX was received on XX/XX/XXXX and XXXX was received XX/XX/XXXX. My due date is the XXXX of the month and I have up to the XXXX of the month to make the payment without negative credit reporting.
Because of the numerous FCRA and FDCPA violations I would like this account completely removed from my credit or for these lates to be removed before I pursue legal actions."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I submitted several disputes for a bankruptcy that's reporting on my credit report i contacted the courts and submitted the letter that was provided to me by the courts the company keep saying the bankruptcy is verified i asked for documentation showing this and was not provided with any, two other credit bureaus deleted the bankruptcy with no problem once i sent them the letter i received from the courts saying that the court did not have this information on file. I have spoke to supervisors that claim it would be deleted in 15 days but this has not happened."
}
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": "Theres several errors on my credit report that need to be removed immediately. I've contacted Equifax a few times but still dealing with the same issue."
}
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": "To whom it may concern : I am writing to update/correct my personal information on file with your company.
Please update my address to : XXXX XXXX XXXX XXXX XXXX XXXX XXXX Please update my name to XXXX XXXX My only social security number is : XXXX My only and correct date of birth is : XXXX I do not wish to have any telephone numbers on my report.
Below are the negative account/accounts impacting my credit XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 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": "Medical",
"Consumer complaint narrative": "On XXXX/XXXX/XXXX I was treated at the XXXX. in XXXX XXXX. I was treated and released. I am a XXXX Veteran insured by the XXXX. Due to a glitch in the system, I did not have my ID card. Medicare paid part of the bill. I submitted the bill to the XXXX for payment. Thought it had been paid. When we went to inquire about refinancing we we told that our credit score had dropped due a collection agency named Commonwealth Financial CFSI. I was never contacted by this agency or asked to defend the charge of {$960.00}. I have been trying to get this resolved since XXXX XXXX. They have been sent numberous pieces of documentation stating that I was insured by the XXXX. My Doctor gave me a statement on a XXXX form which stated that I have been a XXXX patient since XXXX/XXXX/XXXX to present day. I sent this along with a letter and my ID card to the agency. I was told that if I gave proof of my elegibility as a Veteran that the debt would be cleared. My wife spoke with many reps there and General Manager XXXX XXXX, XXXX, XXXX XXXX, XXXX. She calls on a weekly basis, each time they say it is with client relations and call back later. In early XXXX they said it was sent back to XXXX XXXX XXXX. Call again for status. They had not heard back. Today XXXX/XXXX/XXXX over XXXX later, she is informed that the account is on hold for 60 days and that XXXX XXXX XXXX is rebilling the XXXX XXXX. This is a clear violation of my right as a Veteran who has served his country and always enjoyed excellent credit. This bill is not my responsibility. Please help get this issue resolved so that we can get our credit score where it should be. This is not way to treat a Veteran Thank you"
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I opened a Chase credit card in XX/XX/XXXX, for the benefit of a 6 month XXXX interest period for repayment. I used the electronic card for online shopping at Amazon, and never used the card for any other transactions outside of those Amazon purchases. I never received the card, and did not realize I did not receive it, until i was contacted by Chase on XX/XX/XXXX.
I was contacted by Chase on XX/XX/XXXX when they noticed suspicious activity on my card. I received the following text message : " Chase Fraud : We declined {$93.00} with card ending XXXX at XXXX XXXX XXXX Was this you? Reply YES or NO. If yes, you will not be charged unless you try again. If no, we will close your current card and send you a new one. '' I replied NO and received a call shortly after. The representative told me they sent the text due to suspecting fraud. She told me the liquor store charge was declined, and a woman called Chase, pretending to be me, to try to get the transaction authorized. The representative told me the woman was able to answer some identifying questions but that it was all information that was available online. She stated she remained suspicious when the woman reported that my cell number on file was a landline. I checked my wallet and unopened mail and realized I never received the card. I informed the rep of this, and assured that I had never swiped the card, had only ever used it in my electronic Amazon XXXX, and had never authorized anyone to use the card.
On around XX/XX/XXXX I received a letter with the results of my dispute. I misread the letter, thought it was resolved in my favor, and threw out the letter.
On around XX/XX/XXXX I received my statement and realized the charges were not reversed. I contacted Chase and reopened my fraud investigation. When asked, the representative I spoke with informed me the claim was denied because the card was swiped. The representative acknowledged that this was not a good reason to deny a claim since I never received the card. The fraudulent charges total more than {$2000.00}. Charges were made in cities that I have not been at all over the past several years. They were made every day of the week, an hour or more from my home and two hours or more from my workplace. I informed the representative that it was impossible for me to be at the places the charges were made.
On around this same date I filed a police report with my local PD to document the theft. On the same date, I froze my Chase card.
On XX/XX/XXXX I logged in to my account to unfreeze my card and saw the full balance remained, and I had a letter in my inbox that the claim was again denied."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
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": "In 2017 we canceled our card due to fraud and requested a new one. We continued to make payments on the account but they were only applied to new charges on the new account, resulting in the previous account being charged off as bad debt. This was all due to an internal glitch at Citi which was eventually resolved on their end, but negative credit reporting still exists. Please see attached correspondence.."
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "You never received your bill or did not know a payment was due"
} |
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": "Other type of mortgage",
"Consumer complaint narrative": "On XX/XX/XXXX, in the process of applying for a mortgage refinance with XXXX XXXX XXXX XXXX, the mortgage processor asked Suntrust for a document called a " Subordination Letter. '' As of today, XX/XX/XXXX, Suntrust has not complied.
Mortgage processor from XXXX XXXX calls Suntrust weekly to find out the status of the document and is told " we are working on it. '' I went to local Suntrust branch where I do my banking ( since XXXX ) to ask them to find out what was happening. I was told this is just " how we do business. '' This delay hurts me enormously. And I might add, when I applied for a Home Equity Line of credit last year, it took over 5 months to close. My credit score is well over 800.
When I complained to Suntrust, I received a call from a lady at their corporate offices who admitted the HELOC should only have taken 30 days to close.
This is so wrong. I am a longtime customer of Suntrust but this doesn't seem to matter."
}
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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I take issue with the handling of my student loans since i graduated from college back in XXXX. I write to request that you urge the XXXX XXXX to modify the XXXX XXXX XXXX XXXX ( XXXX ) program such that periods of full-time public service combined with periods of in-school deferment would count toward XXXX, regardless of whether payments were made during those periods.
Please consider the grounds upon which I issue this request : I have worked for public institutions in higher education since XX/XX/XXXX. When I began my employment at a university I called FedLoan Servicing regarding beginning my payments and was told that since I was a student it would be pointless to begin paying my loans since it would not count toward forgiveness. During this time I was only making around XXXX XXXXXXXX annually and would have qualified for XXXX XXXX monthly payment. Since that time I have consistently worked and gone to school full-time and have been told over the years that it would be pointless to go into repayment since I was a student. At XXXX point I was informed that since I so many loans if I began making payments some of the loans would not be eligible for repayment so I needed to wait until my degree was confirmed to begun payments on them so that they would have the same payment counts. Upon graduating in XXXX my payments began and my count began."
}
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": "Auto debt",
"Consumer complaint narrative": "This is a warning notice to the alleged violator of such debt. After an extensive review of my consumer report I noticed I have violated pursuant to the FDCPA ( Fair Debt Collection Practices Act ). There is evidence of the use of subside, deceptive and unfair debt collection practices. Not only did you furnish this information to the reporting agencies without my consent, nor did you take the proper steps to verify that this information is accurate. Which is it NOT! 15 USC 1681s-2 You also listed this alleged debt as a charge off. How can you collect on a cancelled debt that is also considered income? Also, income should not go on a consumer report at all. This also means that you filed this as a loss with the IRS and received compensation for it. This also means that I should have received sa 1099 to file, BUT I DIDN NOT! This is a violation of 26 USC 61-64. I also never received a notice that this information would be furnished on my report. You are supposed to contact me within 30 days, no later before furnishing the negative remarks. This is not all, but just s few of the violations that I am bringing to you US AUTO FINANCE XXXX XXXX and the CFPBs attention.
I am requesting to be provided with the written contract between me and you US AUTO FINANCE XXXX XXXX giving you the right to access my personal information and to also furnish this information. I would also like the original written contract with myself and US AUTO SALES. I will also need a full audit trail. I am needing the actual physical documentations to support the information that is being reported on my consumer report. Screenshots and statements will not suffice.
I expect to receive a response within 15 days of receipt of this letter, or I will expect that the item in question will be permanently expunged from my record : Best Regards, Done in good faith. Without prejudice. All natural inalienable rights reserved.
XXXX : 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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "All three major credit bureaus Equifax, XXXX and XXXX have illegally hoarded consumers personal information without this Consumers expressed written consent. Pursuant to the 1974 Privacy Act all 3 credit bureaus violated all privacy rights and allowed personal information to be leaked due to their false and misleading ways. According to the Fair Debt Collection Practices Act all three credit bureaus maliciously and collectively shared this Consumers personal information to make trillions of dollars by selling it to various debt collectors, employers, financial institutions, and a host of others.
My wish is for the Federal Trade Commission and Consumer Financial Protection Bureau to demand on my behalf that all 3 credit bureaus remove all of this Consumers social security numbers, date of births, financial accounts or credit/debit card numbers, driver license numbers, detailed health or medical history or similar sensitive information. All relations with these 3 credit bureaus are now in question for reporting fraudulent information to disgrace this Consumer. Consumer wishes for damages of XXXX XXXX dollars from all 3 credit bureaus for their Treasonous deeds and all will be forgiven. Consumer would also like to thank the Federal Trade Commission and Consumer Financial Protection Bureau for protecting XXXX Personal, Family, and Household.
This consumer is putting Equifax, XXXX and XXXX on notice of the value of this consumers personal data and signature and is warranted damages in the amount of XXXX XXXX dollars.
Take Note : All XXXX XXXX XXXX ( XXXX XXXX XXXX ) Personal Information and Signatures hold a required value of XXXX XXXX dollars ( XXXX ), per incident ( request ) 15 USC 1692 ( A ) ( 4 ). Violations found may be cause for indictment/ penalties for Violation of Rights, Trademark/Copyright Infringement, Fraud, and Estate XXXX , etc for all parties involved.
ACCORDING TO 15 USC1692 ( A ) 4 I XXXX XXXX I AM THE ORIGINAL CREDITOR-IN-FACT/ALL PERSONAL DATA AND SIGNATURES HOLD AN UNLIMITED VALUE/SO CALLED CREDITORS LISTED ARE DEBTORS IN FACT/EQUIFAX/TRANSUNION/EXPERIAN DOES NOT HAVE MY EXPRESSED CONSENT TO MY EXPRESS WRITTEN CONSENT TO FURNISH MY DATA."
}
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": "OnXX/XX/XXXX,three(3)fraudulentchargesweremadetomyCITIBankAAdvantageExecutiveMasterCardatthefollowinglocations:{$5400.00}--XXXXXXXXIL{$840.00}--XXXXXXXXXXXXXXXXIL{$780.00}--XXXXXXXXXXXXILTOTAL:{$7000.00}AtXXXXonXX/XX/XXXX,Ireceivedatextnotification(detailsincludedinattacheddocumentation)askingmeifIattempted{$1900.00}atXXXXXXXXXXXXXXXXwhichIimmediatelyresponded"2''(NO).Ithenreceivedanauto-responsetoopenafrauddispute,whichIdidinlessthan1minute(detailsincludedintheattached)afterreceivingthemessage.
IspokewithafraudspecialistatCITIfor7minuteswherewewereabletoidentifytheaforementionedthreechargesthatwereNOTauthorizednormadebyme.Thefraudpreventionspecialistthenshutthecarddown,mailedmeanewcardandassuredmetheclaimswouldbetendedtoimmediately.
OnXX/XX/XXXX,IreceivedaletterfromCITI(detailsintheattached)informingmethattheydidnotconsidermydisputetobefraudulentactivityandtherefore,myaccountwouldberebilledforthethecharges.Sincereceivingtheletter,IhavespentcountlesshoursonthephonewithCITItryingtoresolvethedispute--which,includedthereopeningandquicklyreclosingoftheinvestigation3times--allresultinginthesamedetermination.
CITIiscurrentlyinviolationoftheirowntermsandguarantees"AtCiti,youwillnotberesponsibleforunauthorizedcharges(chargeswhichwerenotmadebyyou,oranyoneallowedtousetheaccount)'':https:XXXXAsaloyalCITIAAdvantagePlatinumcardholderformanyyears--acardthatcarriesa{$500.00}annualfee--thishasbeenacompletelydishearteningandunprofessionalexperienceandhavelostallfaithinthebank."
}
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 reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have disputed the late payments on the stated accounts for months. The credit bureaus refused to reverse the late payments. I don't understand how they have verified this..Not to mention they will not give me an explanation as to why they wont overturn or fix this erroneous inaccurate claim that has affected my credit score tremendously. CFPB please step up and fix this ASAP .. XXXX XXXX XXXX/XXXX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "On XXXX XXXX, XXXX in The United States Bankruptcy Court of XXXX Massachusetts case # XXXX and under Chapter XXXX debt owed to " XXXX XXXX '' in the sum of {$820.00} was discharged. XXXX XXXX received a XXXX of Notice of this discharge on XXXX XXXX, XXXX and that is the only account I have ever had under my name and social security there after.
On or about XXXX of XXXX I noticed a " Pinnacle Credit Services '' reporting on my credit reports that I owed them {$820.00}. When I called regarding this debt I was told it was for a XXXX XXXX account I had recently opened. After explaining to them that this debt was discharged and providing them the above information nothing was done to correct the issue.
Instead I received harassing phone calls saying that they were taking me to court ( which never happened XXXX. They told me they were not responsible for removing or reporting information to the credit bureau 's and gave me numbers to Vision Financial at XXXX and XXXX XXXX XXXX at XXXX which I spoke with both who simply told me that XXXX was responsible for this.
I have gone as far as to call XXXX XXXX 's corporate offices and they verified with me that the account I had with them was discharged through my bankruptcy in XXXX and that because of that they would not re-open an account for me under my social security without a large security deposit for 10 years!
The way Pinnacle is reporting on all XXXX of my credit reports is that this debt is in collections and current and it has been damaging my credit for almost TWO YEARS.
I am reporting them to the Attorney General as well and my next step will be to hire a personal attorney if you or they can not assist which I will expect Pinnacle to pay for.
I would greatly appreciate your help as I have been working very hard at improving my credit.
This company is awful and destroys people.
Thank you"
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt was discharged in bankruptcy"
} |
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 'm writing in pursuit of my owed money from my private loans. I have consiladated twice and my payments exceed well over XXXX $ month. I have joined XXXX and XXXX for 5 years and have received no discount or lower payments because of my civil service. I was told I was ineligible."
}
Output:
{
"Issue": "Dealing with my lender or servicer",
"Sub-issue": "Received bad information about my 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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "On XX/XX/21, at approximately XXXX pm I ( XXXX XXXX ) received a call from a woman named " XXXX, '' who claimed to be from Citibank. She claimed there was an attempt to charge {$450.00} at a XXXX in Georgia.
She asked were we visiting Georgia. I told her no! I was a bit distracted while on a XXXX business call and multitasking. She then texted me a Citibank code and asked me to repeat it to her. I gave her the code and she claimed she declinded the attempted fraud on our checking account.
Approximately three hours later around XXXX XXXX, Citibank called and indicated that there was a {$2000.00} wire that was just sent out from our checking account. I stated that we did not send out a {$2000.00} wire on XX/XX/21.
The Citibank representative shared with me that the wire was sent to another Citibank Account, to a woman named XXXX XXXX. I was told the fraudulent wire was caught quickly and flagged as a fraud and it would be easy to recover the funds and I would be reimbursed. I have attached police report and letters from Citibank declining my compliant and my demand of the {$2000.00}."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem using a debit or ATM 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 reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have sent the police report, filed disputes on XXXX XX/XX/2021 {$840.00} ACIMA XX/XX/2021 {$3400.00} XXXX XXXX XX/XX/2021 {$800.00}"
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "On XX/XX/XXXX, I received an alert via my credit monitoring company that VANCE & HUFFMAN LLC, placed an alleged collection debt on my credit report. Per the FDCPA, the company failed to provide a written request of Validation and/or contact me prior to placing the alleged debt on my credit report. The company has been sent a letter dated XX/XX/XXXX via Certified mail in reference to this FDCPA violation."
}
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": "On my XXXX XXXX phone all 3 credit reporting company 's login and passwords all taken off screen. Login will not work i have all 3 with a security freeze. Sense a year ago there should be no activity on these accounts."
}
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": "Debt collection",
"Sub-product": "Medical",
"Consumer complaint narrative": "I have called and tried to contact the hospital and debt collector to get a mailed bill or an account number with balance from them and no one I have talked to at hospital or debt office can find the information or says I do not have an open account or balance due."
}
Output:
{
"Issue": "Disclosure verification of debt",
"Sub-issue": "Not given enough info 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": "Money transfers",
"Sub-product": "Domestic (US) money transfer",
"Consumer complaint narrative": "I believe paypal is scamming me. A few months ago I made some purchases on ebay and used PayPal to purchase items. After quite a few purchases I started receiving bills in the mail from PayPal for paypal credit. I did not sign up for this service and expressed that to them but all they would do is cancel the service. My bills from them are stacking up. Please help me."
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In XX/XX/XXXX I disputed a account that's showing inaccurate information on my credit report. The account showing inaccurate information is XXXX XXXX # XXXX The original creditor is XXXX XXXX {$4800.00}. In XX/XX/XXXX EXPERIAN sent in the mail that the account was verified and accurate. In XXXX I sent in a complaint to the CFPB about the account to the original creditor which is XXXX XXXX XXXX XXXX XXXX XXXX, VA XXXX. XXXX XXXX Responded to the complaint and sent in letter stating that the account was charged off with a balance of {$720.00} as of XX/XX/XXXX. ( see attachments ) The account was sold to XXXX XXXX. Looking at my recent credit report provided by you EXPERIAN the charged off/Amount Past due off amount is showing {$520.00} ( see attachments ) on the report under XXXX XXXX. I have enclosed a copy of the letter from XXXX XXXX the original Creditor showing proof that the amount is reporting inaccurate. EXPERIAN is showing and reporting {$520.00} which is inaccurate. As per the FCRA all credit reporting agency are to report 100 % accuracy information on consumer reports. Well as of XX/XX/XXXX I have enclosed also a copy of your inaccurate reporting of my report. I will be filing a suit with the court if this account isn't removed from my credit file.. EXPERIAN is supposed to be conducting a reasonable investigation and not relying on a third party system. Please remove this account from my file or I will gather all my documents and go to my local court and file a suit as this is not right and not reporting 100 % accurate information on my report, I have copies of the balance that's reporting wrong and a copy of the letter from the original creditor so as of today this account needs to be removed from my file and not updated. if I find that the amount is updated a suit will be file,,, delete the inaccurate info"
}
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": "Prepaid card",
"Sub-product": "Government benefit payment card",
"Consumer complaint narrative": "I have requested my replacement rushcard XXXX paychecks ago and have yet to receive it. I have paid expedited fees and still have yet to receive my card. I have request it XXXX times starting on XXXX XXXX. I have XXXX small children XXXX XXXX XXXX and I can not pay my bills and can barely make it to work. All my money is on that rushcard and I am beyond frustrated hurt upset annoyed and irritated."
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "On the XXXX on XXXX I told them to stop calling me every day and just to send whatever information to my house.
They did not.
And have continued to call me most days XXXX XXXX XXXX XXXX XXXX XXXX pm XXXX XXXX XXXX XXXX XXXX XXXX XXXX pm XXXX XXXX XXXX pm XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX am"
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Dates and amounts are included with the uploaded documents.
I have made multiple attempts to resolve this with XXXX XXXX and the property. XXXX XXXX corporate will do nothing, saying it must be adjusted by the property.
All attempts to deal with the property have been unsuccessful. When I call them, they say they will have the property owner call me back to resolve but I never receive a call."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX. The following account is not mine XXXX XXXX XXXX Account Number : XXXX Please remove it from my credit report.
XXXX. The following account is not mine XXXXXXXX XXXX XXXX Account Number : XXXX Please remove it from my credit report.
XXXX. The following account is not mine Account Number : XXXX Please remove it from my credit report.
XXXX. The following account is not mine XXXX XXXX XXXX XXXX Account Number : XXXX Please remove it from my credit report.
XXXX. The following account is not mine XXXX XXXX XXXX XXXX Account Number : XXXX Please remove it from my credit report, 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 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than seven years.
15 U.S.C. 1681s-2 ( A ) ( 1 ) A 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."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "I have written to AR Resources to have them validate a debt they are negatively reporting on my credit files with all credit reporting agencies. I have not received said validation. I was simply mailed a hospital bill that was not itemized as to what the charges were for. I believe my HIPPA rights have been violated as I never authorized any entity to disclose my personal medical information to a third party. AR Resources has been reporting negative information to my credit files and I have asked them to delete this information as they have not provided validation of the debt they claim I owe."
}
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": "Payday loan, title loan, or personal loan",
"Sub-product": "Title loan",
"Consumer complaint narrative": "One Main Financial ( formerly known as Springleaf Financial ) has conveniently terminated a loan contract ( XX/XX/XXXX) that was supposed to be covered by XXXX payments through XXXX XXXX XXXX for account # XXXX.
All required paperwork was completed and approved by my doctor, myself and XXXX from XX/XX/XXXXthrough XX/XX/XXXX. However, I just discovered that One Main Financial has recently decided to forfeit the contract and claimed that it expired although theyre at fault and have added frivolous and erroneous fees to the account. Furthermore, they have erroneously turned over the account to a collection agency. This will result in additional fees from the collection agency, ruin my credibility and credit report.
I attempted to discuss and resolve this issue with XXXX, One Main Financial Branch Manager, however, he refused and threatened to call the police because I asked for an explanation and informed him that I intend to sue for bad business practices."
}
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": "Auto",
"Consumer complaint narrative": "I voluntarily gave my vehicle back to the company that was financing it after year you 're not really responsible for the debt is really been three years and I do n't know how do you can take XXXX points when you volunteer to give the car back from my credit report how can you XXXX come up with this"
}
Output:
{
"Issue": "False statements or representation",
"Sub-issue": "Attempted to collect wrong amount"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Approximately XXXX of 2019 I requested that XXXX XXXX XXXX would correct the late payments showing on my credit bureau for both of my credit cards with them one XXXX and one XXXX because XXXX autopay. The autopay worked for a couple of payments then stopped. There was an account number change that could have been the reason for the autopay being removed but it is XXXX XXXX XXXX 's responsibility to reinstate the same services I had on the accounts.
When I noticed there was no Autopay a couple of months later, I called and requested to reinstate autopay and correct the late payments. The representative reinstated the autopay and refunded the fees but failed to correct the late occurrences on the accounts.
I requested a loan to pay off my debts today and the bank gave me a copy of my credit bureau. Both of the XXXX XXXXXXXX XXXX cards XXXX and XXXX are showing late payments and furthermore ; are not showing as a current payments and in good standing. These two account do not appear in my current accounts. They both show up under derogatory accounts and not showing under current accounts. These two accounts I have had with XXXX XXXX XXXX for over 20 years.
There has been previous account number changes but never closed and reopened. The late payments and the way it is reporting on my credit bureaus is negatively impacting my credit score and credit history. I have autopay on all of my credit cards to avoid this.
I just spent over an hour attempting to contact them through their customer service phone number. Only to speak with a representative named XXXX from New York to tell me I have the wrong department and put me back into the general Queue which doesn't give an option for disputing erroneous information reported on the credit bureau. This make is appear as though there is no accountability on the part of XXXX XXXX XXXXXXXX to make this right. They collect fees but have no contact available through their automated system to contact this department."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "A couple time I asked to prove which method they used to verify the information and they never send me any documentation proving how they find out or method used. by law they need to provide. but the never did. On XX/XX/2019 Report # XXXX. I asked and they respond accurate information. XX/XX/2019 Report # XXXX. Same respond. XX/XX/2019 Report # XXXX. Same respond. The account need to be deleted by law. Because the process was incorrect. I was able to fix the situation with XXXX without problem. They can not verified and they deleted the account.
Account XXXX # XXXX ( XXXX and TransUnion )"
}
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": "According to the Fair Credit Reporting Act, Section 609 ( a ) ( 1 ) ( A ), you are required by federal law to verify -through the physical verification of the original signed consumer contract -any and all accounts you post on a credit report. Otherwise, anyone paying for your reporting services could fax, mail or email in a fraudulent account.
I demand to see Verifiable Proof ( an original Consumer Contract with my Signature on it ) you have on file of the accounts listed below. Your failure to positively verify these accounts has hurt my ability to obtain credit. Under the FCRA, unverified accounts must be removed and if you are unable to provide me a copy of verifiable proof, you must remove the accounts listed below.
XXXX FI XXXX ; XXXX XXXX ; XXXX XXXX XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX/XXXX XXXX ; XXXX XXXX XXXX ; Chapter XXXX Bankruptcy 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": "In accordance with the Fair Credit Reporting act XXXX XXXX Account # XXXX XXXXXX, 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 an 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.
In accordance with the Fair Credit Reporting act XXXX XXXX XXXX Account # 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 an 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.
Please note there is not actual account number listed for this account."
}
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 had XXXX Chase XXXX accounts that are being incorrectly reported to the credit bureaus. After having a difficult time with paying my debt and ultimately nearly going into default I contacted the company who agreed to accept a settlement on the accounts. They agreed to remove the information from my credit report upon payment of the account. I settled the account with Chase XXXX, however they did not follow through with the removal from my account as promised. Please remove these accounts from my report. Regards"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "TransUnion did not properly investigate this account. They are breaking the law by ignoring me. In accordance with the Fair Credit Reporting Act, the below account, 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 an account without my written instructions. I have never had a collection issue with XXXX and they admitted it. Account in Violation of the Act and Law : XXXX XXXX ( ACCOUNT NUMBER : XXXX ) This account must be DELETED."
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "XXXX HI guys.
You did not answer my problem.The my mortgage monthly payment was counted for XXXX XXXX city Grand List on XX/XX/.This value on Grand List can not be change and is obligatory for one year.The Grand List assessment XXXX take effect on XXXX XXXX to the next XXXX in XXXX.While XXXX to XXXX XXXX the value of this assessment is mandatory.I bought my home on XXXX XXXX when I was under assessment from XXXX XX/XX/XXXX to XXXX XXXX.The assessment value of property tax was {$4900.00} this means that semiannual property tax to the city should be {$2400.00} to the XXXX XX/XX/XXXX, but you change it in the XX/XX/XXXX to {$2700.00} semiannual payment for property tax..Why?
Sincerely XXXX XXXX"
}
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": "Other debt",
"Consumer complaint narrative": "Debt company, Viking Client Services, is stating that I owe an alleged debt in the amount of {$350.00}. This is completely false and they have reported this debt to credit bureaus. They have defamed my name and I am taking legal action.
I sent Viking Client Services a Debt Validation Letter, Cease and Desist letter, an invoice for all violations, a copy of their mailed document with all of the violations listed, and a notarized Affidavit of Truth. They have yet to respond to my requests."
}
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": "send XX/XX/2022 Hello to whom it may concern, I have contacted the credit bureaus to do a verification of my credit report by mail. They responded within a couple weeks.
They said they conducted their investigation and updated all information and that everything was accurate. After their investigation I went ahead and researched myself and discovered improper inaccuracies even after they did their investigation.
In accordance to the Fair Credit Reporting act XXXX Account # 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 The following accounts have huge discrepancies.
XXXX. XXXX XXXX XXXX XXXX XXXX XXXX is inconsistent Last Reported is inconsistent Date Last Active is inconsistent Date of Last Payment is inconsistent XXXX. XXXX XXXX XXXX -Date Opened is inconsistent -High Credit is inconsistent -Credit Limit is inconsistent -Last Reported : is inconsistent Date Last Active is inconsistent Date of Last Payment is inconsistent XXXX. XXXX XXXX -Account Type - Detail : inconsistent -Date Opened is inconsistent -High Credit is is inconsistent Payment Status is inconsistent Last Reported : is inconsistent Date Last Active is inconsistent Date of Last Payment is inconsistent XXXX. XXXX XXXX XXXX Account Status : is inconsistent Date Opened is inconsistent High Credit is inconsistent Payment Status : is inconsistent Last Reported : is inconsistent Date Last Active : is inconsistent Date of Last Payment : is inconsistent XXXX. XXXX XXXX XXXX XXXX, XXXX -Account # is inconsistent -Account Type is inconsistent -Account Status is inconsistent - Date Opened is inconsistent -No. of Months ( terms ) is inconsistent -Last Reported is inconsistent -Date Last Active is inconsistent XXXX. XXXX XXXX Account Type - Detail is inconsistent Account Status : is inconsistent Date Opened is inconsistent No. of Months ( terms ) is inconsistent Last Reported : is inconsistent Date Last Active : is inconsistent Date of Last Payment : is inconsistent XXXXXXXX XXXX XXXX Account Status : is inconsistent Date Opened : is inconsistent High Credit : is inconsistent Last Reported : is inconsistent Date Last Active : is inconsistent Date of Last Payment : is inconsistent XXXX. XXXX XXXX Account Type - Detail : is inconsistent Account Status : is inconsistent Date Opened : is inconsistent High Credit : is inconsistent Last Reported : is inconsistent Date Last Active : is inconsistent Date of Last Payment : is inconsistent XXXX. XXXX XXXX Account Type - Detail : is inconsistent Date Opened : is inconsistent High Credit : is inconsistent Last Reported is inconsistent Date Last Active : is inconsistent Date of Last Payment : is inconsistent XXXX INQUIRY is inconsistent XXXX - INQUIRY is inconsistent XXXX XXXX -INQUIRY is inconsistent XXXX INQUIRY - INQUIRY is inconsistent"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Non-federal student loan",
"Consumer complaint narrative": "My private loans have been sent to an outside collector named Allied Interstate. They call multiple times a day, they were verbally abusive, and threatening when I had spoken with them. They call all hours of the day, and interrupt me while I am working."
}
Output:
{
"Issue": "Can't repay my loan",
"Sub-issue": "Can't get flexible payment options"
} |
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": "To Whom it May Concern, This letter is a formal compliant that you are reporting and incomplete credit information.
According to the Fair Credit Reporting Act section 609 ( a ) ( 1 ) ( A ), you are required by federal law to verify, through the physical verification of the original signed consumer contract, any and all accounts that you post on a credit report. Otherwise anyone paying for your reporting services could fax, mail or email in a fraudulent account.
I demand to see verifiable proof ( I.e. : an original consumer contract with my signature on it ) that you have on file for the account listed below.
Your failure to positively verify these accounts has hurt my ability to obtain credit. Under the FCRA, unverifiable accounts must be removed and if you are unable to provide me a copy of verifiable proof."
}
Output:
{
"Issue": "Threatened to contact someone or share information improperly",
"Sub-issue": "Contacted you after you asked them to stop"
} |
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 am currently disputing debts that are allegedly owed on my credit reports. I have sent numerous letters to the collector to validate other alleged debts - which they have not been able to validate and now they have added another older debt onto my report for {$620.00} opened on XX/XX/2018. I have also looked up this original creditor and I have never lived or been to XXXX XXXX, Nevada. Ever since I have asked for validation from this collector they are arbitrarily and viciously adding more debts without my knowledge that the debt even existed without sending me proper notices for validation. I have been sending these letters of validation and they have failed to validate that any of these alleged debts are mine without proper proof. Furthermore, these debts are outside of the statute of limitations. I will also be filing complaints with the XXXX and the Texas Attorney General 's Office today."
}
Output:
{
"Issue": "Written notification about debt",
"Sub-issue": "Didn't receive notice of right to dispute"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I was trying to buy my first vehicle and the salesman pulled my credit and told me that my credit was pretty bad for the amount I was trying to get approved for I asked him what was the problem with credit and he showed me my report. These are many derogatory items that do not belong to me. Please remove these items from my account. I have contacted the FTC and my local police and I am doing this on my own behalf. Please remove these from my report : XXXX XXXX XXXX, XXXXXXXX XXXX Company Inquiries : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX"
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "Loan Depot has been giving me the run around on an application for a mortgage and the seller is about out of time. I can't go to another lender due to the excessive amounts of credit pulls they have done. This has left my credit damaged with no choices to go elsewhere. I have talked to managers and none care or arr willing to do anything. Im currently homeless because of this and have included a copy of a form the lender sent that lists the excessive amounts of pulls."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "XX/XX/2018 To CPBP, SUBJECT : Bank of America 's deliberate attempt to delay process of Automatic Full Payment inspite of reaching out several times.
Hello CPBP, I have been approved for XXXX XXXX XXXX XXXX from Bank of America. I have contacted them several times to make sure that the card is paid in full ( automatic full paym
ent ). However, Bank of America has enforced a policy that unless and until " XXXX '' is not generated, customers can not set up their account to be paid in full. The " XXXX XXXX' generation process has taken several months and upon several requests Bank of America was not willing to set up auto payment over the phone.
They have done so for charging interest My balance was just in range of {$20.00} and they have marked my account with credit reporting agencies as delinquent and in debt collection of for debt of {$1.00} ( for 4 months, total of {$6.00} ).
This has led to cascading effect of high inconvenient and many of my applications being deined resulting in hundreds of dollars of loss..
I have made full payment to Bank of America ( including the interest charge of {$6.00}, today XX/XX/XXXX). They have still NOT obliged my request for auto payment of my card, in case i use the card.
I sincerely, reqest CFPB to investigate this loophole which " Bank of America '' has created to not approve the " XXXX '' process and hence NOT honor any request for auto- payment in full.
thanks XXXX XXXX XXXX XXXX XXXX XXXX XXXX Email : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX TX XXXX"
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "Problem during payment process"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have repeatedly asked for evidence to support XXXX 's reporting, but yet again, the company has failed to provide me with a copy of any viable evidence submitted by DeptEdXXXX, XXXX XXXX, and XXXX XXXX substantiating their claims.
Furthermore, I have requested your method of verification, and XXXX have not complied, which is an additional violation of the Fair Credit Reporting Act 611 ( a ) ( 7 ).
Given that I believe you are acting in bad faith, and have not complied with the Fair Credit Reporting Act, I am fililing a complaint. I sent them several certified letters dated, XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX. I have not received any reponse from the credit bureau."
}
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": "Mortgage",
"Sub-product": "Home equity loan or line of credit (HELOC)",
"Consumer complaint narrative": "Figure ( https : //www.figure.com/ ) advertises a home equity line of credit ( HELOC ) product. On XX/XX/2022, I submitted an application to receive a rate quote. However, once I submitted my application, it became apparent that their HELOC product more closely resembled a home equity loan product.
If lent to, I must take 100 % of the HELOC proceeds at closing. For instance, if my total line was {$100000.00}, I would have been forced to take {$100000.00} at closing. They claim it's an " open line '' because if I make principal payments, I may then borrow up to the initial $ 100k during the " draw '' period.
I believe this is intentionally deceptive advertising and does not meet the criteria of a HELOC product, where an initial draw is optional. Please investigate and potentially make them reclassify their existing " Figure Line of Credit '' product."
}
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": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "SLS refuse to assist me they want me to give them the house they wo n't tell me why and how I got denied no explanation Cfpb do ur job put this ediots out of business I will not give them my home if SLS want my home they should kill us first then they could have our home SLS not a good servicer they do n't do anything they want me to give them my home without assisting me"
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "The debt does not belong to me. I called them back in XXXX and explained that this debt is fraudulent and does not belong to me. It was removed from my credit report, but it appeared again in XXXX of XXXX."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit 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 Account # 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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I am a co-signer on a loan # XXXX for my daughter with Navient. Her name is XXXX XXXX. It is a college loan. For some reason, she was 60 days past due.
I was not aware of this until Navient reported it to XXXX. My score ( have excellent credit ) dropped from XXXX to XXXX. I feel that this action is unwarranted and unfair to me.
I am being penalized because she as an adult failed to make the payments.She is a XXXX and this is her loan. I have contacted Navient ... they will not correct my credit score.This has been extremely upsetting to me and my husband died in XX/XX/2017.
I am going to need this higher score to purchase a car.
What can be done about this ... .I really need your help to clarify this situation.
Thank You, XXXX XXXX XXXX XXXX XXXX.
XXXX XXXX, OH XXXX email : XXXX XXXX"
}
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": "On XX/XX/2019, I asked my bank ( XXXX XXXX ) to obtain a credit report as part of an application for a mortgage loan. Three companies reported : XXXX, TransUnion, and XXXX. Both XXXX and TransUnion referenced several Creditor accounts that were incorrect, that is, the Creditor information was not mine but actually belonged to another person. XXXX information was mostly correct. I contacted TransUnion to dispute the incorrect information. After two calls, a TransUnion representative took my complaint and initiated the steps to remove the incorrect information. I was not successful in contacting XXXX either by internet or phone call to rectify the report. I have three fundamental concerns : ( 1 ) how and why was incorrect information added to my identity, ( 2 ) why are credit-rating companies like XXXX and TransUnion permitted access to my personal financial information and have such incompetence in ensuring that the information is correct, ( 3 ) why do financial institutions like XXXX XXXX rely on credit-rating reports when the reports may have inaccuracies? It seems that persons like me are the ones who have to rectify credit-rating problems created by the incompetence of companies like XXXX and TransUnion. My suspicion regarding concern ( 1 ) is that the credit-rating companies are not using a complete set of personal information when they obtain financial information : namely, complete 9-digit social security numbers, current addresses, and birth dates, all correlated to an account held by a person. The incompetence of companies like XXXX and TransUnion places a terrible burden of proof on the financial status of US citizens. Why are such companies not appropriately regulated by the Federal Government?"
}
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": "Payday loan",
"Consumer complaint narrative": "Was notified by a collection agency that they were attempting to collect on an online loan made by someone using my name and social security number. The said loan was made in 2014 but the company is just now attempting to collect. Collection agency has not been forthcoming about any information and will not verify any details about the fraudulent loan ; all they want is money."
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt resulted from identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I am a victim of identity theft.
on or about XX/XX/2020, Capital One sent me a letter telling me that they had put a hold on my Capital One account because they wanted to clear up concerns they had around potential FRAUD. They wanted me to call them so that they could work to resolve this issue with me. I called Capital One on XX/XX/2020. They stated that an individual was claiming to be me, so they wanted me to send them copies of my driver 's license, social security card and proof of residence. I send them what they requested.
On XX/XX/2020, Capital One sent me a letter telling me that they had put another hold on my credit card account, because they suspected FRAUD. I sent them copies as they requested. Capital One sent me various new cards to clear up to eliminate the potential FRAUD. This potential FRAUD was never eliminated by Capital One. The Thief has stolen my social security number, driver 's license, birth date and was able to deceive Capital One employees into allowing him into my Capital One online account. Where he was able to change my password and user ID. To this date Capital One has refused to comply for the various records/documents requests."
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "Let me start by stating that I DO NOT OWE THIS COMPANY A DIME. THEY ARE CONTACTING ME TO FIND MY XXXX year old Married Son.
I was contacted by a man who identified himself as XXXX XXXX. He stated that my son was in a lot of trouble. He then went on to say that there was a Summary Judgement filed against my son. I interrupted him saying that my son was an adult and I do n't owe his company or the company he is representing any money. He got angry and said he did n't know that the company my son works for was in the business of " hiring snakes '' and if I was n't such a " bad mother '' then I would know where my son is and I was a " crazy person. '' I hung up and he called back. I handed my husband the phone. When asked to identify himself he ( XXXX ) stated, " This sounds like an episode of XXXX XXXX '' ( A blatant and obvious derogatory statement regarding our racial identity. ) My husband asked him what company he represents and he just began yelling out my social security number ( the phone was on speaker. ) We hung up at this point.
Several minutes later our XXXX daughter called from school stating that she received a call from XXXX XXXX and he stated, '' Are you going to talk to me or are you a mean person like your mom. '' She was confused so she hung up and called us immediately.
I got a message five minutes later from XXXX XXXX in which he stated that he intended to take me and my minor daughter to court regarding my son 's alleged debt.
Let me reiterate that this man/company is attempting to reach my XXXX year old adult married son."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Used obscene/profane/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": "Debt collection",
"Sub-product": "Mortgage",
"Consumer complaint narrative": "false statement and representation XXXX XXXX SENT A GENERIC LETTER, XXXX XXXX MAY NOT BE A HUMAN, NOT A LIVING PERSON, MAY NOT EXIST. HIS SIGNATURE WAS ROBO SIGN AND PLACED ON THE LETTER ANOTHER EVIDENCE OF FRAUD ON CAPITAL ONE, Na no evidence of XXXX '' '' deceptive debt collection lawsuits based on flimsy or nonexistent evidence. by capiatl one, NA illegal practices that can deceive or intimidate consumers, such as filing lawsuits without determining if debts in question are valid. " '' see cfpb as CFPB Takes Action to Halt Illegal Debt Collection Practices By Lawsuit Mill and Debt Buyer,,, " were not verified and might not exist, '' did not respond to QWR and assignment fraud violated the Fair Debt Collection Practices Act and the Dodd-Frank Wall Street Reform and Consumer Protection Act, which prohibits unfair and deceptive acts or practices in the consumer financial marketplace.
Lawsuits against consumers without sufficient basis.
Filed lawsuits based on unreliable or false information.
challenged the validity or accuracy of the ASSIGNMENTS FRAUD. capital ONE, NA did not obtain or review qwr,, the fraud assignments information. FRAUD FALSE FACT IN court filings. AFFIDAVIT"
}
Output:
{
"Issue": "Disclosure verification of debt",
"Sub-issue": "Not given enough info 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 HAVE BEEN A VICTIM OF THEFT THESE ACCOUNT WAS OPEN WITHOUT MY CONSENT"
}
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": "Mortgage",
"Sub-product": "Conventional fixed mortgage",
"Consumer complaint narrative": "Whilst getting a replacement debit card, due to a stolen account number, the personal banker suggested a refinance on my existing FHA mortgage. She said she could beat the rate I had, but I need to submit some info to " LOCK '' in the rate, which could be done over the phone. However once everything was completed & we showed up to sign the paperwork, there was a higher interest rate than what we had " locked ''. Then the manager to tell us they do n't lock rates. And we had to wait for everything to go through before we would know what it is. In other words, w/o telling us in advance, they claim no one knows there rate till signing. Which does n't make any sense, why would anyone waste their time then? So someone is lying, or covering up for someone not doing their job & locking in my rate. Either way, I feel that this was a " Bait & Switch '', and this bank should be ashamed of treating a customer of over 10 years this way. Due to this, we had to go with another loan agency. During this transaction, we found out that we had lost thousands of dollars in interest savings by wasting our time with US Bank."
}
Output:
{
"Issue": "Settlement process and costs",
"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": "Wells Fargo home mortgage has made things worse. I have tried for two weeks to simply recast my mortgage. I was told someone from that department would call me within " 24 hours '' and no one has. Then I was told to go to a local branch and apply funds for payment, instructing the teller to apply all funds to principal, as ordered by Wells Fargo. Teller incorrectly applied some funds to income. I alerted Wells to this and now they have reversed the entire payment, over {$210000.00}. Sp, my check has cleared and Wells is saying they have never received it due to a clerical error.
I have been serially abused by this incompetent institution. How the government is allowing this institution to survive is beyond me."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hi. I have sent many different verification letters to the credit bureaus in regards to the inaccuracies across my credit report. I told the credit bureaus that there are inaccuracies in names, addresses and companies that I did not recognize as well as inaccuracies in the dates across the board. All of the credit bureaus, for the last 3 months have verified this information. Until this day none of the dates are aligned so how can you say you verified the information?
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 ( reporting duplicate as well ), have violated my rights 15 U.S.C 1681 section 602 A. States I have a right to privacy.
15 U.S.C. 1681 Section 604 A Section 2 states a consumer reporting agency can not funrish 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": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "I am the recipient of a large number of contacts lacking mandatory legal documentation or purpose, on such a continuing basis that the filer at US Bank is clearly engaged in stalking me and targeting my business as the only agenda US Bank has in this matter, as having made no attempt to collect and instead retaliating much further in response to any attempt to get even XXXX MN or CFPB to mediate in the dispute, by date.
As far as can be determined, this employee has engaged in accounting fraud, XXXX the overdraft protection on the account at the same time she adjusted out the funding from a single check for {$330.00} that their software had screwed up the deposit of unlawfully and which cleared the maker 's bank XXXX in the normal 3-5 days after it was finally possible to deposit it which documentation is took place XXXX at my end despite the screwup in the US Bank XXXX for the same transactions, and only to US Bank.
The responses continue to change and consist of inflammatory libel which keeps escalating.
All this person has ever done is target my business credit, public reputation, relationships with customers, and me personally financially and otherwise, hence the use of the term stalking as this has no relationship to lawful debt collecton.
I have been through the attached request in other forms with the bank since XXXX/XXXX/15 when she did all of this at the same time through having customer service shout at me when I phoned and make highly litigious offensive false accusations where the record indicates there is an employee who engaged in accounting fraud and has stolen the funding of the check at this point.
I however am being harassed so severly that I have and have just had to pay for credit reports for my business due to having been sent notice they were reporting a chargeoff as their collections efforts when I attempted mediation through the XXXX MN as their other response of the same date as that XXXX falsifying claims of repeating bouncing checks etc and that this client also had checks deposited at another bank.
Which is the work of someone reading comic books that make no sense particularly given the customer is a long term repeat customer and has provided both sides of the check involved as deposited at their company 's bank. By email. Upon request as decent honest business people who have no role in any of this.
However, it appears I am being dragged through court to force US Bank to permit me to clear my name, my business credit, and force US Bank to stop the harassment of me personally and otherwise like a vindictive slapstick cartoon.
I am seeking documentation of US Bank that I have requested before, and provided the final as expected emotionally angry request attached, but they have refused to document or provide any evidence of any claim they make which is against the law in creating and collecting debt.
As is collecting fraudulently created debt or debt created in error which this kind of malicious hostility indicates is not the case.
They do owe me the {$57.00} balance in the account from when she did this to my account, she being XXXX XXXX XXXX the US Bank Branch Manager in XXXX XXXX IL in a branch I went into XXXX at least a year ago whose ATM generally did not work and so I rarely even drove to the ATM of after hours.
I have had forces scattered so widey that I am also not sure what anybody can do other than address the various components of the consistent across the board gross misconduct with debt collections of a debt I do n't owe that was created with accounting fraud.
About a single check that is not this difficult to cooperate with dispute and a normal bank creates and provides notices of with any notice of the alleged bouncing checks the other bank stated did not take place.
They have no record of multiple presentations of that check at all.
They have no funds returned from US Bank either."
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "I already complained on Bank Of America, XXXX XXXX XXXX XXXX Its been 2 weeks still I didn't get any response from them. because of this I am not able to take Mortgage loan for a house even I can afford, Please try to fix this as soon as possible, Thanks in advance!!
Complaint ID : XXXX"
}
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": "I recently submitted a request for investigation of a XXXX XXXX XXXX XXXX, which you refused. I submitted enough information for you to carry out a reasonable investigation of my dispute, since you obviously did not investigate this account thoroughly enough the last time I submitted one. They have already verified that I have never had a late payment. They have stated when I have called them, that they have contacted your agency numerous times to have the other account removed to no avail, often noting unreasonable hold times to get through to the fraud department.
If you had investigated properly, rather than use your XXXX system, you would have known, that not only have ever been late on this account, but that I am not the owner of this account, though your company claims to have verified this.
It is at this time that I will point out that in XXXX v XXXX, XXXX v. XXXX ( XXXX ), and XXXX v. XXXX, Equifax, et al, the courts ruled each and every time that the CRA couldn't merely " parrot '' information from the creditors and collection agencies ... that they have to conduct an independent REASONABLE investigation to ensure the validity of the debt and the honesty/integrity of the creditor/CA in question. Sending out a generic form through the XXXX system that does not even contain my reasons for the dispute is not reasonable."
}
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": "On Tuesday XX/XX/2019, I learned that XXXX experienced a breach of my accounts and that those who breached the account ( s ) have my contact information including the phone number attached to my credit reports and all 'business ' accounts.
I have filed a Police report and as soon as it is ready to pick up I will be filing with the FTC for Identity Theft. Forthcoming will be complaint against XXXX that I have initiated with NY and MD Attorneys General 's offices.
In the meantime, I have gone online to Equifax, XXXX, and XXXX to request copies of my credit reports and it appears each have changed my address or contact information to something I didn't authorize because I am unable to get copies of any of the reports. When I enter what I have provided to each of them, I get error messages that my reports can not be accessed.
So then ... here we go again ... not even a year later, and after many, many, many of these complaints against Equifax, XXXX and XXXX they have once again changed my information to some unknown information even after they have been informed over and over and over again! that my address on my credits and phone number exist to protect my identity ... and yet they've changed my information to I don't know what ..."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Problem getting your free annual credit 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": "Bank account or service",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Discover bank is not treating me fairly and equitably. The bank keeps giving me a hard time with managing my accounts. I had to call this bank several times and the customer service is very poor. I was put on hold for such a long time and a resolution could not be reached!"
}
Output:
{
"Issue": "Account opening, closing, or management",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The list of accounts below have violated my federally protected consumer rights and confidentiality under 15 USC 1681.
Account name : XXXX XXXX XXXX XXXX Account number : # XXXX Account name : XXXX, XXXX.
Account number : # XXXX Account name : XXXX XXXX XXXX Account number : # XXXX Account name : XXXX XXXX XXXX Account number : # XXXX Account name : XXXX XXXX XXXX XXXX Account number : # XXXX Account name : XXXX XXXX Account number : # XXXX Account name : XXXX XXXX XXXX Account number : # XXXX Account name : XXXX XXXX XXXX Account number : # XXXX Account name : XXXX XXXX XXXX Account number : # XXXX Account name : XXXX Account number : # XXXX Account name : XXXXXXXX XXXX XXXX XXXX Account number : # XXXX Account name : XXXXXXXX XXXX XXXX XXXX Account number : # XXXX 15 U.S.C 1681 Section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions.
15 U.S.C 1681c. ( a ) ( 5 ) Section states : No consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than 7 years.
15 U.S.C. 1681s-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or has reasonable cause to believe that the information is inaccurate.
15 U.S.C 1681 b- permissible purpose."
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "Called today XX/XX/24 to tell them the covid 19 recovery modification documents did not explain why the partial claim is 44, 000 when our past due currently is 33,000.00 from covid 19 forbearance and the agent tried to say that that was with not including addtl taxes and interest which is wrong, my mortgage statement show the 33000.00 includes taxes and interest and principle. They want me to sign a loan modification with no explanation on why my partial claim is more than my past due, please help ( this is 2nd complaint here, first one no one responded, XXXX XXXX XXXX XXXX, XXXX, Mi XXXX XXXX"
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Payday loan",
"Consumer complaint narrative": "We used payday loan service XXXX years ago and paid off our remaining debt. Halsted financial services has called us XXXX yr later saying that we owe them money. We do n't owe them money or have we used payday loans for XXXX yr"
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I have a checking account with BBVA which is now part of PNC Financial Services Group , Inc. I only have one task/objective - closing my checking account with them.
On or about XX/XX/21, I called BBVA 's customer service number and was informed that I need a XXXX balance in order to close my account. Accordingly, I initiated an electronic transfer for {$4400.00} ( my remaining balance with BBVA ) to an external bank account later that day. The BBVA website indicated that they will charge me a {$3.00} fee ( which I am okay with ) and the maximum amount for a transfer is {$5000.00}, which was not a problem as my transfer was less than that threshold amount.
The status of the foregoing transfer was " UNDER REVIEW. '' On or about XX/XX/21, I submitted an online inquiry regarding this status because I wanted to remedy any issues so my transfer wouldn't get denied. I attached the response BBVA provided to me indicating that the status may be due to its large or unusual amount. They also informed me to contact its Enterprise Fraud Department ( EFD ) by telephone.
On XX/XX/21, I received a text from BBVA informing me that the transfer " for {$4400.00} previously under review has been blocked. '' Attached is a true and correct copy of that text. BBVA did not provide me any reasons as to why my transfer was blocked.
Since XX/XX/21, I have been calling BBVA who has been playing ping pong with me by transferring me to different departments, including the EFD. No one has been able to give me any answers and I have been on the phone for approximately two hours. On XX/XX/21, I received a message from BBVA ( attached ) which can be accessed when I login onto their website and informed me that the transfer was denied because it was not verified by the EFD. However, I have already called the EFD who has told me they are unable to help me. Also, shouldn't EFD contact me to verify before outright denying the transfer?
On XX/XX/21, after many calls, I made one last call to ask if they have an email address and XXXX, the representative, said BBVA does not have any. Instead he suggested I go to a local BBVA branch. I informed XXXX that I live in Hawaii and there are none here. He said that calling appeared to be only way I could address my complaints as I told him my frustration of having no one help me over the phone. I also asked if there was an address I can write a letter to and he said there was none.
XXXX suggested I fax a request to BBVA to close my account which needed a notarized signature. I asked how long would it take to process and he said approximately a month.
I also googled PNC as I could not find any support from BBVA and discovered an email address, XXXX. Unfortunately, I sent them an email regarding my foregoing concerns and the email bounced back which I have attached.
I feel that BBVA/PNC have been misleading and deceptive in making it difficult to close my bank account. I believe they are unlawfully holding my money and withholding important information as to why my transfer was blocked. It is unfair that I have to get a written request notarized which may take approximately a month to process when I could do an electronic transfer which can be done in a couple of days. Also, I XXXX searched how to close my BBVA account, and BBVA provided only one form. I interpret their form as how to close an account with another bank account so you can transfer your funds to BBVA. It seems odd they they do not provide a form where you can close a BBVA account and transfer it to another account. I am frustrated that their online responses and phone call representatives are unable help with this easy and common task and those are the only two ways I can request for help. I would not be surprised if I submit my notarized request and it gets blocked/denied for unknown reasons."
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Can't close 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": "XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX, Wisconsin XXXX Date of Birth : XX/XX/XXXX SS # : XXXX Dear, Experian Re : This is not a regular dispute. Take action immediately you are in violation.
I am a victim of identity theft and I write to dispute certain information in my file resulting from the crime that i recently became aware of. The items I am disputing do not relate to any transactions that I have made or authorized. Please REMOVE this information at the earliest possible time.
As required by section FCRA 605B ( 15 U.S.C. 1681c-2 ) a copy of which is enclosed which states that this information must be removed within four business days of receipt, I am requesting that the adverse information on the items be removed to correct my credit report.
Please investigate these matters and remove the disputed items as soon as possible.
XXXX. The inquiry was not authorized XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXXXXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. The inquiry was not authorized XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
XXXX. XXXX XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX Account XXXX : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXXXXXX XXXX XXXX XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXXXXXX XXXX XXXX XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX Account XXXX : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX Account XXXX : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXXXXXX XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX Account XXXX : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX Account Number : XXXX This accounts name and number are questionable. Either clarify this information to me or delete this account immediately from my credit report.
XXXX. XXXX XXXX XXXX Account Number : XXXX Kindly investigate and make sure this account belongs to me, because I can not recognize this business. Please delete this account from my credit report.
XXXX. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
Enclosures : Proof of identity : Proof of Address, ID, FCRA XXXX Enclosure Identity Theft report Please note that you have 30 days to complete this investigation, as per the Fair Credit Reporting Act Section XXXX ( a ) ( XXXX ) ( A ). Be advised that the description of the procedure used to determine the accuracy and completeness of the information is hereby requested as well, to be provided within ( XXXX ) days of the completion of your re-investigation and I am keeping a careful record of your actions, including your XXXX of XXXX. I do not consent to e-Oscar or any means of automated verification.
Failure to respond satisfactorily within 30 days of receipt of this certified letter XXXX result in a small claims action against your company, seeking {$5000.00} per violation for : XXXX. ) Defamation XXXX. ) Negligent Enablement of XXXX XXXX XXXX. ) Violations of the Fair Credit Reporting Act XXXX. ) Financial Injury My contact information is as follows : XXXX XXXX XXXX XXXX XXXX XXXX, Wisconsin XXXX SSN : SS # : XXXX P.S. Please be aware that dependent upon your response, I XXXX 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 : XXXX XXXX XXXX XXXX of XXXX Consumer Financial Protection Bureau XXXX XXXX XXXX XXXX Washington, XXXX XXXX CC : Law Firm XXXX XXXX XXXX XXXX XXXX XXXX XXXX : Consumer Financial Protection Bureau CC : Attorney Generals Office CC XXXX XXXX XXXX XXXX XXXX : XXXX XXXX XXXX : XXXX XXXX XXXX XXXX XXXX : Comptroller Of The Currency CC : Federal Reserve XXXX XXXX : Credit and insurance CC : Federal Trade Commission XXXX : XXXX XXXX XXXX XXXX : California Regulatory agency Division of consumer complaints Thank You, Sincerely, XXXX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Investigation took more than 30 days"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XXXX XXXX i sent letters to Equifax, XXXX, XXXX stating pursuant to 15 usc 1681a ( 2 ) ( B ) any authorization or approval of a specific extension of credit directly or indirectly by the issuer of a credit card or similar device should be excluded from a consumer report."
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "My Mortgage company sold my loan to another Mortgage Servicer. The property was given to me fully by my divorce judge and I have faxed the new Mortgage company the Decree several times asking them to recognize me so I can continue to pay the loan, as I have for over 20 yrs. My Maiden name is on the deed as well. Now they will not let me speak to billing or send me a statement, I just keep paying the payment, but have little authority. I have asked to speak with a supervisor and have been waiting on a call that is probably not happening. When I can explain the situation to someone they have me to explain it but then say, they can't do anything, hours wasted. He lived here for about a year ; I for 22 years."
}
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": "Demanding that a borrow complete a recast form and pay a {$300.00} fee so that extra principal payments are properly reflected in the recording of monthly payments, i.e. a lower proportion of the payment goes to interest expense and more to principal, is fraud and theft and a violation of federal and state law. Credit card companies can only charge interest on the monthly outstanding balance, but NewRez continues to charge interest on principal balances that no longer exist because they have been paid off with extra principal payments. And NewRez makes every effort to avoid doing a recast with their customer service reps consistently stating on recorded lines that they do not know anything about recasts or even stating NewRez does not do them. Also, once a recast is requested, they take 90 days to complete it.
I had a mortgage both on my home and raw land in the 1980 's and whenever I made an extra principal payment, the proportion of the monthly payment going to principal each month was immediately recomputed by computer and increased with no need to request a recast. Yes, even in the 1980 's, computers could easily do that. SO NEWREZ NOT DOING THIS IS A VERY BLATANT RIPOFF!!!!"
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX XXXX, XXXXXXXX XXXX XXXX and XXXX XXXX violated my 609 rights by reporting incorrect information to Transunion regarding my credit file. Transunion further violated my rights by adding this incorrect information to my credit profile for others to see.
XXXX XXXX, XXXX XXXX, XXXX XXXX, XXXX XXXX XXXX XXXX and XXXX XXXX XXXX reported to Transunion that I applied for credit in which I never applied for. They have violated my rights to have fair and accurate information on my credit report.
My personal information on my credit report is inaccurate as well. I have requested to have my addresses, phone numbers, and names corrected on my report this is a violation on my consumer protection rights."
}
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": "XX/XX/XXXX I was injured on the job at my employer. I was off on XXXX During this time, Hospital mistakenly billed or services.
Contact was made with hospital, as an employee, and the incorrect billing was removed.
XX/XX/XXXX XXXX XXXX notified me that XXXX XXXX has filed 4 derogatory remarks on my credit file. all on the same day.
They are showing dates that I was not even an employee of the hospital ... XX/XX/XXXX and XX/XX/XXXX I tried to dispute but the only response back was that the marks were in FRMC compliance. No other information They are also violating my HIPPA Rights by posting that I had an injury when I did not .. they disclose my name, birthday, social security number. date of birth, hospital, and what they think were dates of service. Although the dates were in XX/XX/XXXX and no bills were owed.
XXXX XXXX is a debt purchaser.
CAPIO PARNTERS Last reported : XX/XX/XXXX {$240.00} Needs Attention CAPIO PARNTERS Last reported : XX/XX/XXXX {$150.00} Needs Attention CAPIO PARNTERS Last reported : XX/XX/XXXX {$75.00} Needs Attention CAPIO PARNTERS Last reported : XX/XX/XXXX {$50.00} CAPIO PARNTERS XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I RECENTLY HAD MY CREDIT PROFILE PULLED. UPON LOOKING AT THIS REPORT I HAVE NOTICED THERE WERE ACCOUNTS THAT WERE NOT MINE.
I HAVE SENT DISPUTE LETTERS ON XX/XX/XXXX BUT TO DATE I HAVE NOT RECEIVED ANY RESPONSE FROM YOUR COMPANY. WE HAVE 30 DAY VIOLATIONS IN DIRECT VIOLATION OF THE STATUTES UNDER THE FCRA."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Investigation took more than 30 days"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card",
"Consumer complaint narrative": "I received notification that someone applied for a credit card with this company. I was unsure as to why it happened as I have a freeze and fraud alert on my credit reports to prevent unauthorized inquiries from happening."
}
Output:
{
"Issue": "Disclosure verification of debt",
"Sub-issue": "Right to dispute notice not received"
} |
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 was looking at my credit report and noticed multiple accounts that's not familiar. I'm not sure where they can from. Please delete these accounts immediately."
}
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": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "I'm in the process of losing my home due to less income and death of borrower. I have reached out for over an year for help. Filled up for several mortgage assistance and keep getting turned down. I currently owe {$32000.00} after trying to resolve the matter. ( to bring current ). Specialized Loan Servicing is not trying to assist but take my home of 40 years. I have sent in several documents that always come up missing or unreadable, so I have to send again.
This loan was transferred in XX/XX/2019 and I have been trying to resolve the matter. I keep getting told the investors doesn't want to invest. I have sent in several documents showing my hardship and my plan of action going forth."
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681. XXXX XXXX XXXX # XXXX XXXX XXXX XXXX # XXXX XXXX XXXX XXXX # XXXX XXXX XXXXXXXX # XXXX XXXX XXXX XXXX XXXX # XXXX XXXX # XXXX XXXX # XXXX 15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than XXXX years.
15 U.S.C. 1681s-2 ( A ) ( 1 ) 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."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
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