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You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "As of XX/XX/XXXX, Equifax has not investigated or responded to the following issues on my credit report at all. The creditors have attempted no contact with me resulting from said investigations. Please see items below ACCOUNTS XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX INQUIRIES XXXX XXXX XXXX XXXX XX/XX/XXXX PUBLIC FILINGS XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XX/XX/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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "World Finance is repeatedly calling people I listed as references over & over, but are also calling people I did NOT list as references leaving messages for me to call them. Per 1692b of FDCPA they are only to call my references one time each & are not supposed to say where they are calling from. They have my correct phone number & address so it is not being used for the need of " location purposes ''. This has been going on for two months. They have told me directly they will continue to call my references daily until I come into the office because that is what is required for them to do."
}
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": "I received an offer to change my Banana Republic store card into a Visa Banana Republic card ( use everywhere ). I accepted the offer but soon noticed that when I went online to pay my bill that the balance would say {$0.00} and no minimum payment would be showing. I would call and pay the bill express frustration that I am unable to pay online. I have been told repeatedly that the issue is fixed but it has never been fixed. I have been charged XXXX {$35.00} late fees by the company for making payment late due to this system issue. I request all fees to be waived and for the system issue to be fixed. I called and spoke to XXXX employee number she provided was XXXX . XXXX was not helpful and quite rude. I explained why I was requesting the fees to be waived but she kept reiterating that the payments have been late even after I explained it several times. After I advised that she did not understand and was not listening she began to speak loudly and I disconnected after getting her information and advised that I would submit a complaint. I can not be the only person this is happening to. I was told nothing would change when I switched cards. I have attached a screen shot of me trying to look at my account today so you all can see it shows {$0.00}. I can not force the system to take the payment."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "The Credit Bureaus have stated that they have verified a bankruptcy on my credit file as accurate and it was verified with the USBK COURT XXXX OKLAHOMA. Which is incorrect.
I have uploaded the letters that describe the issues I am having.."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "not my account"
}
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": "My daughter, passed away on XX/XX/2015. After she passed away I had to take care of her affairs. Thats when I discovered she falsified my signature to obtain a loan for educational purposes. I did not authorize her to use my signature or my likeness to obtain a loan or financial purposes. I have contacted all three credit bureaus, the department of education and XXXX who holds the loan and they refuse to remove it from my credit report. Its hard enough to lose a daughter but to have to deal with this as an ongoing reminder is more than one should bear."
}
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": "While buying plane tickets with XXXX, a popup appeared to apply for their XXXX card and get a {$250.00} rebate. Nowhere did it say that there would be an annual fee. I bought and used the ticket and was approved for the card. I recently got the statement and noticed that a {$99.00} annual fee had been added to my bill. I called XXXX at XXXX and spoke with rep XXXX and then XXXX XXXX on XX/XX/22 and they stated that I would be charged an annual fee. I let them know that's not what I signed up for and that's not what their offer specified. I also let them know that their offer on line for a barclays XXXX card SAYS there's a " XXXX '' annual fee ( see screenshot ). My card looks exactly the same as the one on line. They stated nothing could be done and I let them know this was bate and switch and I demanded that the annual fee be erased as that's not what I signed up for. They refused. I let them know I would be paying my bill minus the fraudulent {$99.00} fee."
}
Output:
{
"Issue": "Advertising and marketing, including promotional offers",
"Sub-issue": "Didn't receive advertised or promotional terms"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have sent letters to each TransUnion, XXXX, and XXXX on three different occasions and have yet to receive a response from any of them. The exact dates I sent the letters were on XX/XX/XXXX, XX/XX/XXXX, and XX/XX/XXXX.
I believe I am getting ignored and the credit bureau 's are infringing on my rights under the FCRA.
It has been over 120 days with no response from the bureaus so I would like the negative information being reported on my credit profile to be deleted."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I contacted TransUnion on XX/XX/XXXX about the following accounts that are not mine ; XXXX XXXX ( Opened XXXX XXXX, XXXX, Highest Balance {$710.00} ), XXXX XXXX XXXX XXXX ( Opened XXXX XXXX, XXXX, Highest Balance {$690.00} ), XXXX XXXX ( Opened XXXX. XXXX, XXXX, Highest Balance {$1800.00} ), XXXX XXXX XXXX XXXX ( Opened XXXX XXXX, XXXX, Highest Balance {$3500.00} ). TransUnion have not responded to my request to investigate as they are required by the FCRA. Attach is a copy of the disputes I send them"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I paid off an outstanding account in full with XXXX credit card. I have asked the company to update my credit report and that they provide me with a letter stating that all the accounts I had with XXXX were closed and paid in full. I have made XXXX phone calls so far and have been unable to get a letter or any results. I had XXXX accounts with XXXX. They keep sending me the same letter for XXXX account but not for both of the accounts especially the XXXX account I paid off that is still showing on my credit report. I called today to report that they only sent me XXXX copies for the same exact account and that I would need a letter for the other account. The XXXX person said I needed to be transferred and disconnect the call. The XXXX person would not give me his name was XXXX with reference number XXXX. After taking all of my personal account information he stated that he was able to review my account letters and saw that XXXX were mailed out. I informed him that they keep sending me the same letter for XXXX account number and needed XXXX different letters XXXX for each separate account I had. He then stated he could not hear me and disconnected the call. This has been going on for over a month at this point and I still do not have the letter I need to get this removed from my credit report, since XXXX keeps stating I need proof it was paid. Please help to get this resolved."
}
Output:
{
"Issue": "Payoff process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "On XX/XX/2020.
I sent a secure message to the bank asking them to close my account.
They replied on XX/XX/2020 saying that I need to go to the branch.
Theres a global pandemic going on and I have elders at home so I cant risk going out.
One month later XX/XX/2020 they still havent closed my account and now its over drawn from monthly fees."
}
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": "From XX/XX/2020 until the current date of XX/XX/2020... Experian, XXXX and XXXX have continually made erroneous reports of late child support payments on my credit report with inaccurate dates without my permission hurting my credit report."
}
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": "Im submitting a complaint to you today to inform you I was the victim of identity theft. I researched how to remove the fraudulent items in my report and found that I need to visit FEDERAL TRADE COMMISSION or https : //www.ftc.gov to file a report Per FCRA section 605b Credit Reporting Agencies are required to remove/block any items listed on an id theft report. Please find the ATTACHED documents to assist in blocking the erroneous information which is being posted to my report.
Here is the list of items that do not belong to me or were opened without my permission.
XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Balance : {$6000.00} ; XXXX XXXX Balance XXXX {$2300.00} ; XXXX XXXX XXXXXXXX Balance : {$990.00} ; XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX XXXXXXXX XXXX : {$670.00} ; XXXX XXXXXXXX Balance : {$1000.00} ; XXXX XXXX XXXXXXXX Balance : {$810.00} ; XXXX XXXX XXXX ( Original Creditor : XXXX XXXXXXXX XXXX XXXXXXXX ) XXXX Balance : {$410.00} XXXX XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX Balance : {$350.00} XXXX XXXX XXXX XXXX ( Original Creditor : XXXX XXXXXXXX XXXX XXXXXXXX ) XXXX Balance : {$220.00} XXXX XXXX XXXX XXXX I ( Original Creditor : XXXX XXXX XXXX ) XXXX Balance : {$250.00} ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX ;"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Bank account or service",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I HAD MY WALLET TAKEN FROM ME AND THEY TOOK {$2000.00} OF MY BANK ACCOUNT AND WHEN I CALLED MY BANK TO REPORT IT THEY SAID THEY WOULD TAKE CARE OF IT AND THEY TOLD ME THAT THE BANK CANT NOT LOSE THIS MONEY LIKE IT WAS MY FAULT MY WALLET WAS TAKEN FROM PURSE ALONG WITH ALL MY CREDIT CARDS AND ID. AND MY BANK WELLS FARGO DENIENED MY CLIENT AND NOW I AM OUT {$2000.00} I WANT TO KNOW WHAT PROTECTIONS DO I HAVE AGAINST FRAUD AND HOW CAN MY BANK IN 1 DAY COME TO A DENIAL SO QUICK AND JUST LET ME LOSE THAT MONEY I NEED HELP IN THIS MATTER IT IS NOT FAIR AND I FEEL THAT WELLS FARGO DOES NOT STAND BEHIND THEIR CLIENTS."
}
Output:
{
"Issue": "Using a debit or ATM card",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I am a customer with Simple Bank, they are transitioning to XXXX on XX/XX/XXXX On XX/XX/2021 they ( Simple ) withdrew {$8600.00} from my account. Without my knowledge or consent. I only found out when I used my card in the supermarket and it did not work.
They left me with {$10.00} in my account to buy food and gas.
After daily calls and messages to customer service, I eventually found out they were doing an investigation into my account. They could not give me a reason why as to why.
Then a few days later I got a message they were closing my account, they only said this is confidential and all funds are frozen, until my account is closed and then they would send me a check for the funds in my account. No mention of the previous money they withdrew.
After more calls and messages, they still can not confirm that what is happening.
So as it stands now, on XX/XX/XXXX I have {$460.00} in account, which they acknowledge will be returned {$8600.00} withdrew and they can not confirm its return Surely this can not be Legal?
I`m absolutely disgusted with their behavior over this.
I can't get a straight answer from them for anything they have done.
Customer Service even hung up on me!
Its caused me a lot of stress and anxiety, Ive had to borrow money from family to get by Its NOT right and it needs to be reported and they need to be held accountable for their actions"
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Deposits and withdrawals"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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 Account # XXXX, XXXX XXXX XXXX Account # XXXX, XXXX XXXX XXXX Account # 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": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "CITIBANK DISPUTE!!!! This account was closed after the first few uses, removed the authorized users on the account and paid it. Called the bank to close the credit card after payments were made, bank stated that it had been closed. Months later, come to find out I am in collections and the bank never closed the account and the person I had on the account continued to use the card without my consent. Therefore, these are unauthorized charges going into account. Called bank to resolve issue and find out why account is not closed and they stated they had no information. The bank stated that it was not their problem and I would be responsible for the balance. Credit card issuer still tries to collect a charge while its investigating my dispute. Please help me resolve as I can not come up with any other solutions. This is fraud that was done on my account by someone I previously authorized and then unauthorized. However, the bank continued to accept credit card charges after I specifically stated that I needed to remove all authorized users from the account and close the account."
}
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": "Mortgage",
"Sub-product": "Conventional fixed mortgage",
"Consumer complaint narrative": "Wells Fargo Home Mortgage continues to use the United States Postal Service to mail fraudulent statements to me, claiming I owe them over {$200000.00}. I never contracted with Wells Fargo Home Mortgage. They continue demanding unsubstantiated amounts, usually in the range of thousands of dollars at a time. I have written many times asking for verification of this alleged debt and have not received proof they are the holder in due course."
}
Output:
{
"Issue": "Loan servicing, payments, escrow account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I was charged VAT for a rental car in XXXX. I am an American citizen. Tourists are exempt from VAT."
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Wells Fargo deposited my check into wrong account according to land lord. Wells fargo is not responding to my bank XXXX XXXX to resolve issue. Check was payable to XXXX XXXX account number XXXX at wells fargo on XX/XX/XXXX two checks were deposited XXXX and XXXX only XXXX went to his account. XXXX was with drawn from my account and not credited. wells fargo need to return my money. {$1500.00}.
Please respond to my bank. I have filed a fraud complaint. You have stolen my money! I am going to pursue small claims court. This is not going away. I will file a complaint with the XXXX next. I want my money back! I had to pay an additional month of rent dt this. Or prove it went to the correct account! I am out XXXX dollars that I worked for ... Wells fargo ... you are stealing. I will call radio stations .... I am not going to drop this, the sac bee and I'm a XXXX ... I am going to contact the XXXX XXXX ... I am not going away. It should not take so many complaints to get my money. Thieves. Respond to my bank if you have nothing to hide. My bank has the proof of canceled checks. You messed up and need to take responsibility.
Last response from from XXXX.
XX/XX/2020 XXXX XXXX XXXX XXXX XXXX XXXX, CA XXXX Re : XXXX XXXX XXXX case # XXXX Dear XXXX XXXX : We are responding to your recent correspondence filed with the XXXX XXXX XXXX ( XXXX ) regarding your recent experience. We appreciate the opportunity to provide additional information and clarification.
Wed like to extend our apologies for any inconveniences this matter may have caused. Our goal is to provide products and services that are easy to use and to always create a positive banking experience for our customers. We regret any instance we may have fallen short of these goals while working with you.
Please understand, you will need to reach out to Wells Fargo to file a claim for the checks, as they were made out to and deposited into the wrong account at that bank. XXXX XXXX has attempted to reach out to Wells Fargo on your behalf regarding this issue but was unsuccessful, as Wells Fargo needs to work directly with you, the account holder at the institution where the error occurred. We apologize for any confusion surrounding which institution should file the claim.
Regarding the customer service you received at the branch, we are more than happy to research and provide additional clarification ; however, we would need to know more specific details, such as the date and name of an employee. Please submit your request with specific question ( s ) in writing to : XXXX XXXX XXXX : XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, MN XXXX We again regret any frustration or inconvenience this may have caused and hope the information contained in this response is helpful. If you have further questions, please contact XXXX XXXX XXXX Customer Service at XXXX ( XXXX ) where representatives are available to assist you seven days a week.
Sincerely, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Customer Service"
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Banking errors"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "This is an unfair system, to be ignored and taken advantage of while plenty of laws are broken due to malpractice, and the severity of not taking my situation has caused financial injury and defamation of character along with 60 days of no response.
i am a victim of identity theft due to the XXXX data breach and all items shall be removed/blocked until this investigation is over."
}
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": "We have a mortgage that the company holds us in escrow. In reviewing our tax history they have not paid our taxes on time for the past 4 years of taxes, and of course it comes out of our escrow so we actually are paying these fees, the fees total {$430.00}. I contacted the Federal Trade Commission and they advised me to contact you regarding what we can do. I did email the mortgage company and they gave a non-answer or explanation."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I was a victim of identity theft inXX/XX/2016 whereas an application with XXXX XXXX for 1 credit card became a 39 inquiries and 22 credit cards mailed, applied over and over again. I had my mail and ID stolen for over a year. When contacting the company for over a year I was assured all accounts were closed. I spent over {$600.00} dollars in applications, monitoring services with all credit bureaus. I had over 5 police reports filled and shared with banks, credit bureaus and FTC report in order to stop the abuse. When calling the company it was like I had had never called them to inform of the theft and after the XXXX data breach this closed accounts started showing up in my credit decreasing the credit report from 850 to 450 ; According to State Laws across the nation when such theft occur all account and related credit cards must be closed and removed from the credit report for a period of 10 years. I can no longer afford to spend an average of {$30.00} dollars a month mailing this police report and requesting the removal ad blockage of this cards according with FCRA consumer credit laws.
When you are notified by a CRA that a consumer 's identity has been stolen, you have specific duties under the FCRA. FCRA 605B ; There was a failed to communicate information within each phone call, each mail of police report.
After reaching the toll free number and speaking with 28 representative they couldn't locate most of the credit cards through the social security number, and I was told : __ Wait there is another consumer with the same name and same accounts but your social security number does not match. That is when the XXXX XXXX XXXX was announced nationwide.
That said, the whole experience was rattling. I can't imagine what a mess it might have been if I had not inadvertently checked the account day by day and spent {$300.00} a year in monitoring services with the credit bureaus, and if additional charges were racked up, wiping out the account, credit cards showing up in the mail after a year.
My vehicle registration, my auto insurance with XXXX and personal mail were also affected by the mail theft ; When you are notified by a CRA that a consumer 's identity has been stolen, you have specific duties under the FCRA. FCRA 605B."
}
Output:
{
"Issue": "Credit monitoring or identity theft protection services",
"Sub-issue": "Problem canceling credit monitoring or identify theft protection service"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I mailed a certified dispute letter to Equifax, and XXXX, asking them to valid that the account they are reporting is reporting accurate information on my credit report. Both Equifax and XXXX mailed me a letter stating all on the information on my credit reported was accurate, however, as you will see on my attach file the reporting information is inaccurate and inconsistent. The date of last payment is inconsistent and inaccurate, along with the date of last active, and comment section says two different reason for account delinquency, which is miss leading and deceptive. I've made attempts to get in contact with XXXX XXXX XXXX to resolve the correct amount owe and to dispute the inaccurate information reported, but still have not gotten a response. If this matter does not get resolved, I am looking to file civil suit because this clearly violate the Fair Credit Act."
}
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": "Credit card",
"Consumer complaint narrative": "Approximately a year ago, I opened a Macy 's account and in the next few months purchased several items. In early XXXX 2016, I received a letter from the collections department stating that because they could n't contact me by phone, I might not be aware that my Macy 's American Express Card account was past due. ( Our mailing address has not changed in some 10 years. ) I called the number listed in the letter and explained to the agent that the account was past due because I had not received any monthly statements or bills and asked for an explanation. None was given, but the agent said that I would begin receiving monthly statements and that all late fees and interest charges would be credited back to the account.
I finally received the XXXX thru XXXX statements in early XXXX, but the late fees and interest charges had not been credited and though monthly statements are now received regularly, not only have the late fees and interest charges not been removed as promised, but new ones are added each month.
In mid XXXX we sent Macy 's a check for the merchandise that had been purchased and not returned.
My husband and I have both spoken with various collection department agents and explained our position that we have paid for the merchandise and since I was told that the late fees and interest charges would be removed, we do not owe them any money. None will discuss why no statements were received until numerous late fees and interest charges had been added to the account or why those fees and charges have n't been removed as promised.
The last of several letters I 've received since the initial one in XXXX, all urging me to call them to discuss getting my account " back on track '', arrived in early XXXX from Citibank via XXXX XXXX. Apparently the need to capture a phone number to use to harass us is important enough to use such silly gestures."
}
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, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I, XXXX XXXX, consumer, and natural person, am aware of all rights that I have, and which are protected by The Congress under the Fair Credit Reporting Act ( FCRA ). The following accounts have violated my federally protected consumer rights to privacy and confidentially under 15 USC 1681. As a reporting agency that has assumed the role of assembling and reporting information on consumers such as me, the FCRA required that it is done so in a manner that is fair and equitable. The following accounts, illegally obtained personal and private information and sold it to other reporting agencies, including Experian, XXXX, and XXXX. I did not grant the following accounts permissible purpose to obtain the information and report it, nor did the debtor. I have not been notified that this information is being provided to the highest bidders. I am contacting you because you claimed to have completed a reasonable investigation with the furnisher of information and verified the account listed below as reporting 100 % accurate. However, these accounts are still inaccurate, and your company is continuing to report errors on my credit file, which is causing damage to my credit score.
In accordance with the Fair Credit Reporting Act, XXXX, Account Number XXXX XXXX, Account Number XXXX XXXX, Account Number XXXX XXXX XXXX, Account Number XXXX XXXX XXXX, Account Number XXXX XXXX XXXX XXXX Account Number XXXX XXXX Account Number XXXX Hard Inquiries XXXX XXXX XXXX, XX/XX/XXXX XXXX XX/XX/XXXXXXXX XXXX XXXX XXXX, XX/XX/XXXX XXXX, XX/XX/XXXX XXXX, XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXX/XX/XXXX XXXX, XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX, XX/XX/XXXX XXXX, XX/XX/XXXXXXXX XXXX, XX/XX/XXXX XXXX XXXX, XX/XX/XXXX XXXX, XX/XX/XXXX XXXX, XX/XX/XXXXXXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX, XX/XX/XXXX XXXX XXXX XXXXXX/XX/XXXX XXXX, XX/XX/XXXX XXXX, XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXXXX/XX/XXXX XXXXXXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX, XX/XX/XXXXXXXX XXXX XX/XX/XXXX, has violated my rights.
I have previously requested that you investigate this information and you have failed to correct the errors you are reporting on my credit report. Now is not the time to update, I demand that you IMMEDIATELY DELETE this inaccurate information from my credit report.
15 U.S.C. 1681 ( A ) ( 3 ). Consumer reporting agencies have assumed a vital role in assembling and evaluating consumer credit and other information on consumers 15 U.S.C. 1681 ( A ) ( 4 ). There is a need to insure that consumer reporting agencies exercise their grave responsibilities with fairness, impartiality, and a respect for the consumers right to privacy.
15 U.S.C. 1681i ( 2 ) ( A ). Before the expiration of the 5-business-day period beginning on the date on which a consumer reporting agency receives notice of a dispute from any consumer or a reseller in accordance with paragraph ( 1 ), the agency shall provide notification of the dispute to any person who provided any item of information in dispute, at the address and in the manner established with the person. The notice shall include all relevant information regarding the dispute that the agency has received from the consumer or reseller.
15 U.S.C. 1681i ( 5 ) ( A ). 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 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 promptly notify the furnisher of that information that the information has been modified or deleted from the file of the consumer.
15 U.S.C. 1681s-2. After receiving a notice of dispute from a consumer pursuant to subparagraph ( D ), the person that provided the information in dispute to a consumer reporting agency shallconduct an investigation with respect to the disputed information ; review all relevant information provided by the consumer with the notice ; complete such persons investigation of the dispute and report the results of the investigation to the consumer before the expiration of the period under section 1681i ( a ) ( 1 ) of this title within which a consumer reporting agency would be required to complete its action if the consumer had elected to dispute the information under that section ; and if the investigation finds that the information reported was inaccurate, promptly notify each consumer reporting agency to which the person furnished the inaccurate information of that determination and provide to the agency any correction to that information that is necessary to make the information provided by the person accurate.
15 U.S.C. 1681b. Subject to subsection ( c ), any consumer reporting agency may furnish a consumer report under the following circumstances and no other : In accordance with the written instructions of the consumer to whom it relates.
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. 1681c ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items. 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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I took out a loan back in XX/XX/XXXX with XXXX XXXX to attend classes for my XXXX XXXX at XXXX . In XX/XX/XXXX I had a baby and gained a Military fiance ' so needed to put school off due to not able to juggle, baby, working full time and Military man. I called in to XXXX XXXX around XX/XX/XXXX to figure out what to do with my loans. I was told I need to pay ov er 1,000 a month on my loans until paid in full. I advised XXXX XXXX there is no way I can afford this being a single mother with XXXX kids. The only option they had for me was to put my loans in forbearance until I could afford the payments. Well my loans pretty much sat there until I had no forbearance left as I never have been able to affo rd over 1000 a mo nth. I received a phone call from Navient who took over my loans from XXXX XXXX stating I was past due. My forbearance had run out. I found out about a lawsuit at this time that allows you to go on a monthly payment based on your pay, the income driven repayment plan. After a lot of headaches and back and forth with Navient, I finally got my payments down to XXXX a month. In XX/XX/XXXX , my year was up and needed to resubmit my proof of income. Since once child turn ed XXXX and my pay went up a tad, I now am being told I have to pay XXXX a month. I asked if there was anything else I can do because I want to pay, just ca n't afford XXXX a month. I feel this loan has continued to grow as it is from the amount I originally took out, I ca n't afford much and the loan is never going away. I feel I was forced into forbearance and find I may have and still may qualify for some kind of assistance."
}
Output:
{
"Issue": "Struggling to repay your loan",
"Sub-issue": "Problem lowering your monthly payments"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/XXXX, XXXX notifies me of technical difficulties in not reporting to bureaus from XX/XX/XXXX. They also stated that " None of XXXX XXXX 's customers credit was negatively impacted. '' This is not true. My bureaus had not had any payments listed since XXXX, XXXX. I contacted XXXX on XX/XX/XXXX for an update & request a credit letter because I received a letter from my credit card company ( which was paid off ) stating that they are closing my account " since I don't pay my bills. '' Since XXXX was the only account I had open but not reported, I can only assume it was them. I was told my account was scheduled to be updated by the end of XX/XX/XXXX and that they would fax the credit letter over -- it never came. I called again XXXX more times, spoke to supervisors, all to get the same answer. On XX/XX/XXXX, a fax arrived but with someone else 's information. This has been ongoing since. Of all the Bureaus, 2 show my account as " closed '' and 1 is " open '' and XXXX blames the bureaus. My credit rating has been severely impacted and still is to this day.
The issue is the comment XXXX has added to the account ; " Chapter XXXX Bankruptcy Dismissed ''. My car was not apart of the bankruptcy and if this information triggers the account to close. When it is removed the accounts is open again. This have been going on for almost a year and a half impacting my credit reports. I finally had to send an e-mail to the CEO of XXXX XXXX with documentation showing that when the information was added to the comments the account became closed and once it was removed it opened again. This worked until they recently updated my bureaus and the remarks were added back in again. My scores look like a EKG reading."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "This is an issue im having with my BJ 's credit card from comenity bank. Very long story, when originally opening the account around late XXXX i received 2 different cards both under my name, since i'm XX/XX/XXXXthere i went and asked if theres anything i should do and I was told to just activate one and leave the other which is what i did, ever since that day I opened an online account to manage my card and it would come up under the other account number, called a couple of days after this to see if the issue could get fixed, was told they are updating their systems and i would be able to have my issue sorted XX/XX/XXXX or after. Called middle of XXXX to see if it could be fixed, had online account deleted and remade still with the same issue, used their easypay feature to make a payment on the card i was using ( also on the easypay page would come up as the other account number instead of the one being used ) made a payment for the XXXX of XXXX about a month ahead. Come the XXXX of XXXX when payment was due i check the account and the payment has gone to the blank account that i have not activated or used. Called them for a refund and to pay off the bill once again. Thought this went smoothly and called a couple of hours later to make sure everything was gon na go through and was told the payment was never processed and that the refund also wasn't put through. This other person did push the refund through and also take the payment which did go through in the future, unfortunately to the same blank account when i was told it would pay off the correct one. Called this morning about getting another refund and finally paying it off and this person helped with the refund and the payment and assured it would go through to the correct account but that i'm still waiting for. They also very kindly credited me the late fee that was added for my " missed payment '' .Very frustrating issue to deal with that nobody can fix apparently and its been going on for about 3 months now and i very much dont want to have any issues with my credit so i'm looking to get some help."
}
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": "This is the several time I've complained about being a victim of identity theft. The 3 credit bureaus must remove any item that was opened without my knowledge, consent, or authority within 4 business days with ( 1 ) appropriate proof of the identity of the consumer ; ( 2 ) a copy of an identity theft report ; ( XXXX ) the identification of such information by the consumer; and ( XXXX ) a statement by the consumer that the information is not information relating to any transaction by the consumer.
XXXX XXXX Balance : {$0.00} ; XXXX XXXX XXXX Balance : {$0.00} ; XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX Balance : {$510.00} XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX XXXX ) XXXX Balance XXXX {$410.00} ; XXXX XXXX Balance : {$110.00} XXXX XXXX XXXX Balance : {$210.00} ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "I have been XXXX in the XXXX XXXX XXXX XXXX at the XXXX XXXX XXXX XXXX since XX/XX/2007. I recently obtained a current copy of my credit report to only find out that I've been a victim of Identity theft. I wrote a letter to capital one regarding the XXXX store card open in my name without my knowledge or consent, along with a dispute with supporting documents showing my ( CRC ) consolidated record card along with my prison ID, social security number, a Identity theft sworn affidavits.
Where capital one denied the claim without an active investigation, its response was nothing more then a smoke screen dancing around the issue. Nothing in there response substantiated how I opened this account, they sent any credit card to a prison. Any address on my credit report is a result of my Identity being stolen. And I ask that this company remove this account from my credit report because I had not opened it nor authorized and one to do so."
}
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 spoke with a rep with Wakefield & Associates on XXXX XXXX regarding collection on my credit report for {$190.00}. I specifically asked the man I spoke with, if I pay this today, can it be updated as paid or deleted off my credit report and I'd make arrangements on a few others they had on me. He told me yes that he would most certainly do that for me. I paid over the phone with my debit card and it cleared my bank on XX/XX/XXXX on the amount of {$190.00}. I also set up ACH payments on the other accounts ( they are not on my credit reports ). I disputed this account on XXXX XXXX and got the results today that it was a verified account and nothing would change. I want this off my credit report! I paid it in full and it should be showed as paid or removed as it is affecting my credit."
}
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": "DiTech Mortgage is improperly placing a 30 day late on my mortgage trade line. I make extra payments nearly every month, just in case a payment isn't received by mail, or if payment changes and I haven't been warned ( See attached Payment History ). I received a notice that showed an account number different than the one I have on my ACH payments from XXXX XXXX. There are 2 accounts with DiTech that are in my name, but one of them is managed by my ex-wife and has been for over 10 years. So, I thought it might be hers. I would have been able to take care of this notice of short payment if the letter had the property address on it, but it didn't. I found that really odd. So, by the time I was able to hear back from my ex-wife, we were at the XX/XX/XXXX and a bit over {$100.00} was due. Since I was not at home or had bank routing information when speaking with a diTech representative, I had no choice but to send an ACH payment THAT DAY from my mobile phone ( See attached DiTech Supp Pmt ). My bank shows to have executed a payment on XX/XX/XXXX. DiTech is noting they didn't receive the electronic payment until XX/XX/XXXX ( See Payment History ). DiTech also is not applying my money payments WHEN RECEIVED. They claim to keep money " in suspense '' until the full amount is paid. My note ( See attached ) makes no mention of lender being allowed to hold payment and not apply it to the balance immediately. They are also not sharing with me where the money is being held, if it is collecting interest, and who is entitled to that interest. I ask that DiTech and ALL OTHER lenders CEASE and DESIST from this business practice of holding money in suspense.
So, the bottom line is that I am paid well in advance of my amortization schedule, I pay extra every month so that nobody may report me as not being credit worthy, or paying back on time. I feel they are trying alternative ways to make money. I want late fees credited, the 30 day late removed from my credit, and a letter from DiTech stating their apologies for improperly handling this matter."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I have attached a copy of my recent invoice of my capital one Visa Signature card. The first page has a total charge of {$7.00} for this billing cycle. The second page has an interest charge {$3.00}. That means ( $ XXXX {$3.00} ) {$4.00} is the charge for this billing period which was carried over from XXXX.
What has happened?
In XXXX, I disputed few charges and since the dispute was under investigation, I did not pay them. However, Capital one did not stop charging me interest. Disputes were resolved in my favor. Capital one did not update my account. Capital one continued charging me interest. I have written to few people at Capital One explaining the matter but ended up without any response or help.
In XXXX, I have submitted a complaint to US Consumer Financial Protection Bureau ( CFPB ) Due to CFPBs interference, Capital One removed some interest, but not this {$4.00} which ended up in {$7.00} today.
So, I am writing to you, to help me. I have tried to resolve it> I wrote to XXXX XXXX but no response. I tried to close the account, they did not respond.
.
I Have closed all other accounts with this improper business. I do not owe them any money."
}
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": "In accordance with the Fair Credit Reporting act XXXX Account # XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX XXXX 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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "To Whom It May Concerns : I am filing complaint against XXXX XXXX XXXX. The reasons are the following : 1. I don't recognized the account or amounts.
2. They are claiming its a joint account with my and XXXX XXXX. I do know that person.
3. They can not provide me any personal documentation such as my DL.
4. They are not claiming that I am 60 days late, when they said the file was open in 2015, none of their information is accurate."
}
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": "As an XX/XX/XXXX CONSUMER of currently unproven compliant and or valid misinformation, I DEMAND you remove immediately the claim ( s ) as related to the alleged delinquencies and claim of CHARGE-OFF/ COLLECTION and further ANY ASPECT OF ACCOUNT potentially injurious to me. Prove alleged derogatory reporting claim of CHARGE OFF/COLLECTION is fully true, correct, complete, timely, valid, physically verifiable by document testimony certifying all data of allegation ( s ), every notation, all aspects, dates and balances, payments and balance calculations, accounting and audits, Identifications and personal identifiers, pay statuses and source codes, all 426-characters of the mandatory correct and complete P6 statement with all of its trailing fragments, each of the sequential 386-pieces of confirmation to collection, creditor classification code, collector class code ( if any ), and all aspects of requisite perfect reporting, mentioned or not INCLUDING TO THE MANDATORY PERFECT METRO 2 FORMAT REPORTING COMPLIANCES! Compliance of reporting and VALIDITY of claim ( s ) are MINIMUM MANDATORY CRITERIA to REPORT, NOT OPTIONAL. You MUST adhere to my state and the applicable federal reporting laws or else be subject to civil action and suitable penalties and or fines for your dereliction, willful in nature OR in IGNORANCE matters not! REMOVE any and all aspects of delinquency ( ies ) and or derogatory allegation ( s ) now to OBTAIN, RETAIN, or to return to reporting with legal standing in full accordance of requisite mandates of law, be them mentioned here or not! I DEMAND you do so TODAY, even HERE and NOW!
I demand the following accounts be verified or removed immediately."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I have a complaint against Citibank. I called Citibank on XX/XX/XXXX and talked to XXXX. I told her that I would be going to XXXX for a few days. I asked her to please note my account. I returned to Maryland on XX/XX/XXXX. On XX/XX/XXXX I received mail from Citibank warning me of a fraud alert. It was at this time that I looked at my account. There was two withdrawals from XXXX XXXX in XXXX. One amount was {$810.00} another for {$440.00}. I called the bank and asked about the amount taken out. I talked to XXXX. I told her the amount was suppose to be {$1000.00} not {$1200.00}. She stated that I need to start an investigation for fraud. I told her that I would call the XXXX XXXX XXXX to see what happened with this overpayment before agreeing to a fraud investigation. XXXX gave me a reference number and a fax number and told me that they needed a detailed reason for a complaint, with the date and and my account number on it. They needed the letter before XX/XX/2019. I called XXXX XXXX XXXX I was told by XXXX to give them a few days and they will make the needed adjustments. I called Citibank and told them what XXXX XXXX stated. I received another letter from Citibank asking about a fraud alert. This time I checked my account and the whole page that showed the {$440.00} and other adjustments had been made on the online account. I asked them why did they change the statement. I was told that they had the right to make any adjustments that they need. They again said that I need to start an alert. I told them there was no need for an alert The Hotel was paid and and I received my refund which according to the XXXX exchange rate at that time amounted to {$620.00} the account has been paid no need for an alert. According to the online statement I received Citibank was paid XX/XX/2019 and I saw a credit for {$620.00}. I didn't look to see why because XXXX XXXX Representative had told me that was the amount I would have returned. At this time I had {$1300.00} in the bank which would have been enough to pay XXXX even at the USA rate.
On Saturday XXXX XXXX I receive another letter from Citibank. I called them and they told me that if I don't give them information by XXXX, XXXX they will be a deduction {$620.00} from my account. I told them they should not take anything from my account. I was told that the Hotel still had not been paid. I asked her if the Hotel had not been paid in all this time don't she think that they would have called me. She paused for a moment and then stated they will be withdrawing the money. I called XXXX XXXX, they confirmed that they were paid but stated they could not give information over the phone. On XX/XX/XXXX I went to Citibank at XXXX XXXX XXXX, MD XXXX and talked to XXXX XXXX and a lady that he said was his manager. Both of them reviewed my account. On my online account it showed the amount of {$620.00} as a transfer. On one of the statements he printed it showed a refund for XXXX XXXX. Both XXXX and His manager told me that I am not responsible for the account. They requested that I call back On XXXX XXXX, and the account will be corrected. I would not have the pay the {$620.00} back. Both of them told me that. I should not worry about it.
This morning XXXX call me and told me that they had deducted all the money that was left in my account leaving me a negative balance of {$490.00}. He told me I have to pay that amount because the {$440.00} was for a security deposit. I asked why didn't someone explain it to me. All I know is that amount was no longer showing on my online statement. No credits or debits for that amount. He said that he will send his coworker a statement about not discussing it with me.I can come in to discuss it again. When I looked on my online account it was showing a negative balance of {$490.00}. If XXXX and his manager couldn't look at the account and figure out what was going on with it after comparing it to about three different formats of the bill, how was I suppose to be able to figure it out? And for the thirty or more years that I have banked with Citibank, I have had overdraft protection. Why didn't the over payment go there. It is not even showing on the online account that I have overdraft protection."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Deposits and withdrawals"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "Portfolio Debt Recovery repeatedly harasses me by calling ( usually every other day ) despite being told that they are not legally able to enforce this debt! It was included in my husband 's bankruptcy and is considered community property here in CA according to community claims pursuant to 11 USC 524. This company was not only provided verbal notification including bankruptcy discharge number but also written notification that any attempt to collect this debt that was discharged in a bankruptcy inXX/XX/XXXX2015 is illegal. However, this has not deterred them and they relentlessly call and send notices still trying to collect this debt."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was already discharged in bankruptcy and is no longer owed"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Lease",
"Consumer complaint narrative": "I leased a XXXX XXXX XXXX. This is our XXXX XXXX in the last 9 years. For the first time I was getting close to the miles. Visited XXXX XXXX XXXX, XXXX XXXX and XXXX XXXX trying to get a solution becuase didnt want to go over the miles. None of them advicsd me that it would be better to pay the extra miles than getting a new car at the moment. None of them were able to work deal that made financial sense to me. At the end I decided to return the car and pay the XXXX remaining of the lease. At the time I received the first bill XX/XX/XXXX I contacted Hyundai Motor Financial and told asked them to wave the XXXX early termination fee so I can pay the difference Since a new need for a Hybrid came up I wanted to stay with the branch and with the Ioniq I can get a tax credit. At that time the ioniq wasnt available, I made an initial payment of XXXX to my account. The representative told me he will extend the due date until XX/XX/XXXX until the car became available. XX/XX/XXXX was here and the car I wanted wasnt available, At the moment paid another XXXX requested the fee to be refunded and was declined again. Asked the representative if paying after XX/XX/XXXX will affect my credit her asnwer was that everything stay in house and an internal recovery department will contact me.
In XX/XX/XXXX I received a call from XXXX a collection agency asking me all my information. I wasn't comfortable answering and the agent told me they will send a letter. That letter came in a week letter and than in XX/XX/XXXX the agent call me again. When he was asking my verification questions he told me he cant talk to me because my date of birth didn't match his records he had XX/XX/XXXX and my DOB is XX/XX/XXXX he promise a call when fixed. On XX/XX/XXXX he calls me back telling me they corrected the information and we can now talk. I told him that I will pay the invoice completely because I didn't want to affect my perfect payment history I have built for the last 12 years. He told me is to late because in XX/XX/XXXX I was reported to the credit bureaus with a delinquent account. I wasn't happy XX/XX/XXXX was the day he couldn't verify me over the phone because of their mistake. I told him I will pay when they remove that negative mark from my credit. He talked to his supervisor and told me they will do and exception and email Hyundai to get that off my credit in exchange I have to pay in full.
It is now XX/XX/XXXX and I haven't receive a call back, the mark still in my records, I called then 3 times and are always with a high call volume. I emailed Hyundai Motor Finance and they directed me to recovery who directed me to that XXXX number.
This is not the first time HMF damaged us."
}
Output:
{
"Issue": "Problems at the end of the loan or lease",
"Sub-issue": "Termination fees or other problem when ending the lease early"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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 requested a withdrawal by check and it was to be sent to me with my name as the payee. I have not received the check. The check was made on XX/XX/XXXX in the amount of {$100.00}. Transaction ID XXXX.
On XX/XX/XXXX, I spoke with XXXX at American Express. XXXX. I requested a stop payment of the check. XXXX told me that an investigation would need to occur that would take 10 days. I told her that I wanted to file a dispute and was told that an investigation needed to occur. I told XXXX that an investigation was not necessary as the check was made out to me, sent to my address, and that I did not receive it. Absolutely, nothing but it will at least take 10 days for the investigation and another 10 days to appear in my account.
I can not get through to these people, that this is not how it should work. It is purely unethical how they hold onto funds.
This company is purely unethical. Because their agreement states that they can hold funds for 7 days+, they do.
Another example of unethical business is XX/XX/XXXX I made a return to XXXX XXXX in the amount of {$180.00}. The transaction appeared on my XXXX statement XX/XX/XXXX ; however the funds were not available on XX/XX/XXXX. I called and asked and they told me that they needed to do an investigation. I requested what the investigation was as I was concerned that perhaps my information had been hacked. Then I was told that my cardholder agreement states that they can hold onto the return funds and they did for 10 days. Although it " appears on my card '' as a transaction of XX/XX/XXXX, it was actually 10 days letter before the funds were available.
This company is sitting on consumer 's money for their profit. The money is making interest for American Express not the consumer. Very unethical."
}
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": "Im trying to get a negative mark removed from my credit report that was reported by XXXX XXXX XXXX. I have been trying for the past few months. Twice I have spoke to different escalation managers and twice my request was turned down. This is ridiculous.
The facts are simple.
My ex wife got our residence from our divorce. In our paperwork it clearly states she was the sole owner and solely financially responsible for the mortgage payments on the residence. Stipulation of Settlement was executed on XX/XX/2018 by the XXXX XXXX XXXX. The late payment to which I am referencing was reported for the month of XX/XX/2018. Clearly after all papers were signed.
The below references relate to the Stipulation of Settlement from our divorce.
Page 13 letter K Unless expressly provided in this agreement, the party receiving the particular asset, whether tangible or intangible, shall assume any and all liabilities associated with the ownership of such asset, and shall indemnify hold the other party harmless of any and all such liabilities.
Page 15 letter B The wife shall remain in and retain sole ownership of the residence.
Page 16 letter C from previous page 15, The wife shall indemnify and hold the husband safe harmless and satisfied with respect to the XXXX XXXX now and forever.
This is pretty simple case. My ex wife assumed the home and the Mortgage solely on XX/XX/2018. On that date I lost all interest in the residence and was no longer obligated to legally pay the mortgage. The fact my name wasnt removed from the mortgage loan was a technicality and that it was being worked on to be removed. It takes months to do title searches and to get approved for a mortgage.
Our Judgement of Divorce Papers were executed by the XXXX XXXX XXXX on XX/XX/2018.
I just want this negative mark removed from my credit report."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Public record information inaccurate"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "SunTrust Failure to properly close personal checking account resulting in bank fees {$630.00} 1. Started approx XX/XX/XXXX with unauthorized charges on my personal checking account ... I filed written statement of unauthorized debit with my branch and all funds were returned to my account.
2. These unauthorized charges continued until I closed the account on XX/XX/XXXX.
3. The Suntrust rep promised me that the account was closed, after I opened a new account with Suntrust ( on XX/XX/XXXX acc't had XXXX balance ).
4. More unauthorized charges continued on old account until someone at work recommended I go to another branch. The first thing the rep at the other SunTrust branch did was block the old account on XX/XX/XXXX.
5. On XX/XX/XXXX Suntrust debited my new account {$770.00} stating I owe the bank for fees from the closed account.
6. I contacted the Suntrust Client Advocacy Resolution Dept and after one month the full amount {$770.00} was returned to my new account. I thought this matter was finished.
7. On XX/XX/XXXX Suntrust debited my account for {$630.00} claimed these were fees not unauthorized charges ( Right to Offset ) On XX/XX/XXXX I returned to the Suntrust branch and spoke with branch manager and was told she would be back in touch. I never heard from she again.
8. I tried contacting the Suntrust Client Advocacy Resolution Dept again, but until today no one has returned my calls.
9. I am a military ret and need the money to finish the XXXX XXXX I had started."
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Fees charged for closing 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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "XXXX XXXX XXXX XXXX Account # XXXX Property addresses XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX XXXX XXXX In XXXX my wife and I decided to get an apartment so we could be closer to work. XXXX XXXX XXXX was our electric provider and XXXX was the cable provider for this place rented in XXXX of XXXX.
We own XXXX houses in the XXXX area XXXX bought in XXXX another in XXXX have attached mortgage statements and water bills for both proving these were current addresses.
Throughout the week we would bounce from house to house or apartment. Living at all XXXX.
As XXXX went on we found the apartment at XXXX XXXX XXXX XXXX XXXX TX XXXX XXXX XXXX which XXXX XXXX XXXX was our electric provider. Was just too expensive to maintain.
We worked out a move early with the apartment complex and our move out date was XXXX.
I mailed documentation to all our service providers on XX/XX/XXXX including move out statements.
I just learned I was charged an early cancellation fee from XXXX XXXX XXXX that was incorrect.
I called and settled on my last bill and was told by representatives that they would put notes and remove the early cancellation fee as well as remove this reporting from my credit report.
Please send documentation that is false reporting is removed immediately and send too XXXX XXXX XXXX XXXX XXXX XXXX XXXX
XXXX XXXX Tx XXXX XXXX XXXX"
}
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": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "My mortgage documents are showing an audit and voluntary repurchase agreement in XX/XX/2013 from XXXX. The servicing has been transferred 2 times since then.XXXX does not acknowledge these documents. After demanding paperwork and making previous complaints, I just recently was told of their existence .I believe we have been defrauded by all the major banks who held this property.After I questioned Mr. Cooper they transferred me to XXXX XXXX XXXX..As of XX/XX/XXXX the loan number is the same under XXXX XXXX XXXX XXXX XXXX and Mr Cooper"
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "VA mortgage",
"Consumer complaint narrative": "My loan was in forbearance due to COVID-19. I was starting a new job. Approved forbearance on XX/XX/XXXX. I got several delinquent letters indicating my payment was past due. I was told the forbearance would not impact me negatively. Unfortunately, it did. I found out on XX/XX/XXXX my pending VA refinance loan with XXXX would be denied due to the forbearance ( in XXXX, XXXX told me it would not ). I immediately on XX/XX/XXXX, paid the mortgage for XXXX, XXXX and XXXX and cancelled my forbearance immdiately. I requested and was informed from Loan Care Servicing that I would receive a letter in the mail and an email stating my Loan forbearance was cancelled. To date, I have reached out to them 4 times and havenot been able to get any documentation refecting my current loan status with the cancelled forbearance. It is also reflected in my credit report that my loan is in forbearance. I paid my mortgage and cancelled the forberance weeks ago. I deserve documenation reflecting the update. I am trying to refinace my loan and need this documentation to move forward with the process. They didn't hesitate to send me past due notices but will not provide me with updated documentation despite numerous request from customer service."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have filed a dispute on the incorrect fraudulent accounts, inquiries and public records on my credit report with the credit reporting agencies and it has been very much over XXXX days and I have not received any investigation results, this is unacceptable and needs to be resolved ASAP."
}
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": "Auto debt",
"Consumer complaint narrative": "XXXX XXXX XXXX was owed by myself for a XXXX XXXX XXXX and a XXXX XXXX XXXX amounts shown on credit reports as {$13000.00} and {$22000.00} both sold to :1patenaude & felix a.p.c.add:XXXX XXXX XXXX XXXX XXXX XXXX ca XXXX and 2 : XXXX XXXX XXXX add:XXXX XXXX XXXX XXXX XXXX nj XXXX XXXX is currently showing on XXXX and XXXX I ask that they both be deleted a.s.a.p. please respond XXXX thanks"
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Threatened or suggested your credit would be damaged"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Other personal consumer report",
"Consumer complaint narrative": "I have contacted Equifax several times via phone online, and via first class mailings on many occasions in regards to several accounts that were placed in my name that did not belong to me and that I did not authorize or was not familiar with. I get the same results and I have had it, It makes it look very bad on my credit report, and reporting negatively. I am trying to refinance something on my credit report and it makes it difficult when theyre checking credit, and I have negative accounts that does not belong to me reporting inaccurately. I have disputed and reach out to Equifax several times and they say the same thing over and over, The companies either need to send proof or validation or it needs to be removed. This account does not belong to me. I've done the FTC Identity Report XXXX, I've called and they've told me that their handling it and they received proper paperwork and I should see an update and I never do. The last rep I talked to stated she was in special handling, I haven't had any luck with this. the following accounts that are reporting are : XXXX XXXX XXXX account XXXX. The following unauthorized inquries should be removed as well 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. When I file a complaint they instruct me to file dispute with them directly which I do just to keep getting the same results. I have even sent copies from XXXX & XXXX showing that they deleted items that Equifax keeps saying verified. How can they all be using the same information but some bureaus are correcting items & the other is saying verified which is totally incorrect."
}
Output:
{
"Issue": "Problem with a company's investigation into an existing issue",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Capital One has falsely navigated and put together a claim that I have an account established with them. Ive attached a copy of the initial police report and fraud report to the police department in my region. Lets see what excuse they have after this."
}
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": "Mortgage",
"Sub-product": "Other type of mortgage",
"Consumer complaint narrative": "The night of XX/XX/XXXX I started an application for Amerisave. Before completing the application I was asked to pay {$540.00} for an appraisal. I messaged XXXX XXXX about it and she stated that is something I will have to do to proceed with the application. So I did so. I proceeded with the application and uploaded documents from the tabs they brought up. I mentioned prior to applying to XXXX XXXX that I need to get a pre-approval in writing and a letter of guarantee so that I know I will be able to receive a mortgage. I have yet to receive any letter for anything Ive asked for. But Im asked to keep uploading documents. I recently let them that they are in violation. I know there was a lawsuit in XXXX about this. It appears that they are still doing the same things as they were from seven years ago. I refuse to be another victim. I have consistently reminded XXXX XXXX and XXXX XXXX that I request to have a letter of approval in writing. Their portal does not allow me to see what I have uploaded nor see a pre approval letter for me to print out. Two days ago XXXX XXXX mentioned that if I upload my school info and my W-2 that I would be able to get a letter and I did so and I still dont have anything because XXXX XXXX was to clear credit and income. Please address this as I am trying to get my {$540.00} back. I have no information as to where my progress is with them and I find it unprofessional."
}
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": "IN REGARDS TO : XXXX XXXX XXXX XXXX XXXX, PA XXXX I HAVE DISPUTED WITH EQUIFAX ABOUT THIS ACCOUNT SHOWING UP ON MY CREDIT REPORT AS IF IT IS MINES WHEN IT IS NOT. I HAVE NEVER DONE BUSINESS WITH THIS COMPANY. I AM A VICTIM OF IDENTITY THEFT AND FRAUD HAS BEEN COMMITTED. I HAVE SUBMITTED THE PROPER DOCUMENTATION TO THE CREDIT BUREAUS REGARDING THE FORGERY AND IDENTITY THEFT REPORTS FROM THE POLICE DEPARTMENTS AND THEY STILL REFUSE TO COMPLY WITH THE FCRA. ONCE AGAIN I STAND AT A STAND STILL I HAVE NEVER HAD A STUDENT LOAN WITH XXXX OR DONE BUSINESS WITH THEM AND FRAUD HAS BEEN COMITTED TO OPEN UP AND ACCOUNT IN MY NAME UNDER MY CREDIT SO REMOVE THE ACCOUNT FROM MY FILE IMMEDIATLEY."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "Affiliate is contacting me constantly regarding a medical bill balance that I have given insurance coverage information. Insurance company paid for the service in full. Still sending threatening letters for a re-sold debt that they don't own and cant collect. Will not give original service provider."
}
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": "Student loan",
"Sub-product": "Non-federal student loan",
"Consumer complaint narrative": "A forbearance was process on my Private student loans serviced by Navient Inc for the XXXX 2015 to XXXX 2015 time period. Navient reported my student loans as 90 days past due as of XXXX 2015. I contacted the Executive Offices at Navient to update this reporting error and they refused. I 've attached a copy of the e-mail correspondence from the Navient representative which clearly states that a forbearance was processed on my account and therefore there is no way possible I could be 90 days past due in XXXX 2015.
According to a recent FTC advisory opinion which interprets Section 623 ( a ) ( 2 ) of the FCRA. The issue posed in the advisory opinion is how a lender is to handle a situation when subsequent information updates a report that was allegedly accurate when it was made but no longer is accurate in the present time ( the identical situation I am currently in ).
The advisory opinion states that the Section 623 ( a ) ( 2 ) of the FCRA addresses the duty to correct and update information by " furnishers, '' or persons who furnish information to consumer reporting agencies ( " CRA '' ) such as credit bureaus. In particular, this section requires a person that " has furnished to a consumer reporting agency information that the person determines is not complete or accurate '' to " promptly notify the consumer reporting agency of that determination '' and provide any information needed to make it complete and accurate. Thus, on its face, this provision requires a furnisher to provide corrected or updated information to the consumer reporting agency that it had reported to originally. This duty extends to all student loan accounts reported to CRAs, regardless of whether they were accurate at one point, because the section requires the furnisher both to " update '' accounts as well as to " correct. '' Navient representatives told me that because the delinquent payments were accurately reported in XXXX of 2015 that any subsequently initiated deferments and or forbearances would not allow for Navient to update reports to CRAs to show that the payments were not late and actually in deferment. However, Section 623 ( a ) ( 2 ) clearly shows that the reports must be updated/corrected regardless of whether they were accurate at one point. *"
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Don't recognize the inquiries bellow they are fraud and they did not have my permission or approval. I contacted each individual company they have no record or a file from me.
XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXXXX/XX/XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXX XXXX XX/XX/XXXXXXXX XXXX"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I was Victim of Identity theft as a result several account appear in my report that are fraudulent and not mine i have send these company all document of the illegality of these account XXXX refused to removed these account from my report and continuo to report then XXXX XXXXXXXX XXXX # XXXX.
DISCOVER FINANCIAL SERVICES # XXXX
XXXX XXXX XXXX # XXXX
XXXX XXXX # XXXX
These account are fraudulent and not mine"
}
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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "The information has been deleted and now they put back on."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Reinserted previously deleted info"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Payday loan debt",
"Consumer complaint narrative": "Hello, This email is in response to a collection account on my credit report. I sent a request to all 3 credit bureaus as well as your establishment to make sure everything on my report was accurate and they have verified the account was accurate, but I have not received any documents to support the validity of this account. Your company also has not responded to the certified letter I sent requesting validation of this debt. I am requesting that you provide validation of this debt.
*Proof of ownership with my approval to sell this debt to a third party company *Payment History with Original Creditor *Orignal Lease Contract with wet signature If you do not comply with this request, I will immediately file a complaint with the Federal Trade Commission and The Texas Attorney General 's Office . If my rights continue to be violated. I will also consider consulting with an attorney.
Sincerely, XXXX XXXX"
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I received a phone call on my work line. I was away at my desk at the time and they left a voice message. They said they are from the county process serving division, that I am named defendant and want to serve me court documents at my work or home between XXXX. They also requested that I have photo ID available."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Threatened to take legal action"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "For the 3rd time RPM failed to provide a solid document INVOICE or agreement signed by me for the Alleged debt claimed that I owe. Pursuant to my rights under the Debt collection law, RPM is trying to collect illegal debt that I do not owe. I have no contracts or deals signed by me to this scam agency called RPM stating that I owe them money.
They have been contacted by me by Certified mail to provide a solid invoice with a contract stating that I owe them the alleged debt, yet, they keep on sending me the same copied spread sheet that is copied or stolen without invoice number or signatures, and a fake copied letter made by them stating illegal debt. I ask CFPB to please investigate the validity of this scam and force this RPM to delete such scam from the credit bureaus."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have been harrassed by servicer after letters and documents produced by XXXX XXXX XXXX XXXX. On XX/XX/XXXXEquifax mailed a letter stating that someone [ XXXX XXXX ] attempted to recreate a loan in the amount of {$55000.00} by creating a different identiy in my name or assumed as XXXX XXXX BOD XX/XX/XXXX. When in fact my name is XXXX XXXX XXXX BOD XX/XX/XXXX THey have also been paid by way of money order on XX/XX/XXXXas they requested.
NOTICE REMINDER OF PAYOFF PAID IN FULL DISCHARGE PAYMENT MONEY ORDER AS REQUESTED. Payment or payoff {$54000.00} was received by XXXX XXXX XXXX XXXX on XX/XX/XXXX Registration USPS number Tracking : XXXX and before thus XXXX XXXX XXXX XXXX received the payment or payoff on XX/XX/XXXXXXXX. Posting 90 days late on XX/XX/XXXXis deemed harassment and possible extortion.
On several occasions since XX/XX/XXXX I have recieved multiple emails and letters requesting new and more information for a student loan. See attached docs emails and letters. I called to ask what was going on and was suggested to ignore the emails and calls. I have recieved a numberous amount of calls emails and letters for offers of free money for college, so on and so forth. This is an internal issue for the Department of Education and the servicer XXXX XXXX.
I would like to file a complaint due to the amount of harassment and extortion produced by your agency as XXXX XXXX XXXX services. I spoke with XXXX a manager on XX/XX/XXXXconcerning the payments and payoff with XXXX XXXX and on the behalf of The Dept Of Education, whom have received multiple payments before and thereafter XX/XX/XXXX. As well the credit agency filed a complaint on this behalf due to attempts thereafter the Dept Of Ed receiving payment in full that your agency attempted to file late on the credit agency thereafter payment was mailed and received. As well the documents stated that after payment my private and personal information shall be deleted and no more attempts to contact communicate or create some form of verbal contract or written contractual documented be it by phone mail or email. In no way is this communication a form of contract only to prevent legal recourse and false documented information. Attached to this email are the correspondences during before and final. XXXX XXXX on multiple occasion attempted to file new agreements or contracts forbarrences please ceist this type of actions for they shall produce legal recourse against you. I have not allowed or given permission thereafter payments in full has been received by the US Dept Of Ed address of US Dept Of ED XXXX. XXXX, XXXX GA XXXX. Now I am dealing with Identity theft and have taken such actions against those whom have created this dilemma and those assisting is such illegal actions, and if its found that someone inside your agency is assisting in attempts to slander my good name and reputation I shall alert the authorities of such things asap. Please be advised there are no other forms of communication thereafter nor a log in account addressed hereafter XX/XX/XXXX after the payment was received anyone attempting to collect on this closed account shall be forwarded to the proper authorities and legal ramification shall be pursued. You have 10 business days to remove all false reports from credit bureaus.
XXXX XXXX XXXX I spoke with XXXX manager of the complaint department onXX/XX/XXXX and she stated if any harrasment persist that I could file a compalint.
Payment or payoff was received by US Dept Of Education on XXXX XXXX, XXXX Registration USPS number Tracking : XXXX and before thus XXXX XXXX XXXX XXXX received the payment or payoff onXX/XX/XXXX XXXX. Posting 90 days late on XXXX XXXX XXXX is deemed harassment and possible extortion. The Credit Bureaus have noted the account ( s ) closed as of XX/XX/XXXX, please inform me of why you are claiming that i am 90 days late when there have n't been a payment maid since XX/XX/XXXX and thereafter XX/XX/XXXXpaid in full? As well the documents presented shows interest owed on this alleged account and a new balance as if a forbearance has occurred some how without my signature or permission!
Done this XX/XX/XXXX I declare under penalties of perjury in accordance with the laws of the United States of America ( without the United States ) Title 28 U.S.C. 1746 ( 1 ) the forgoing is true and correct and is admitted when not rebutted, so help me.
iam IAM ' ; a Sentient being in living flesh operating in the outer expression ( 3rd Dimension ) as : XXXX, : XXXX XXXX : XXXX : : : a naturalized original descendant of the ancient, XXXX XXXX XXXX XXXX, and XXXX of the ancient western hemisphere ; the Real Party in Interest, First Lien Holder [ FRCP Title IV. > PARTIES > Rule 17 ( b ) ( 3 ) ( A ) ] of : XXXX, XXXX XXXX XXXX : ( a corporation sole ) doing business as ( d.b.a ) XXXX XXXX XXXX ( XXXX XXXX created XXXX XXXX, XXXX ). : XXXX ; XXXX XXXX XXXX : has attained the Age of Majority and is competent in His own affairs having in his possession the Authenticated Documents pertaining to the full and authoritative operation within His own Due Process and rights as an Indigenous Aboriginal and Registered Owner, Pursuant to the Minnesota Court General Rules of Practice - Rule 220 pertaining to Birth Certificates.Notice to Agent is Notice to Principal, Notice to Principal is Notice to Agent- All rights Reserved [ U.C.C. 1-308, 12 USC 95a ( 2 ) ]. As such He demands All of His unalienable rights at all times and does not waive any of His rights.
iam IAM ' XXXX XXXX XXXX XXXX and upon this ROCK ( Revelation of Common Knowledge ) I will build my CHURCH ( Comprehensive Historical Universal Revelation Combating Hopelessness ) and the Gates of XXXX ( Misinformation ) shall not prevail against it.
A new money order was sent to the alleged agency of XXXX XXXX XXXX on XX/XX/XXXX.
[ PRI ] VATE AND CONFIDENTIALITY-CLAIM : FOR THIS [ PRI ] VATE E-MAIL-MESSAGE WITH THE [ ATTACHMENT ( S ) WITH THE [ PRI ] VILEDGE-KNOWLEDGE AND/OR : CONFIDENTIAL-KNOWLEDGE IS WITH THE CLAIM OF THE SOLE-USE WITH THE VOLITION OF THE CLIENT-VESSEL ( S ) BY THIS [ PRI ] VATE AND CONFIDENTIALITY-CLAIM. FOR THE POLITICAL, PRIVATE, PUBLIC-ENTITIES, FEDERAL, STATE, LOCAL-CORPORATE-GOVERNEMENT ( S ), MUNICIPALITY ( IES ), INTERNATIONAL-ORGANIZATION ( S ), GLOBAL-ORGANIZATION ( S ), CORPORATION ( S ), AGENT ( S ), INVESTIGATOR ( S ), INFORMANT ( S ), ET. AL., AND & OR : THIRD-PARTY ( IES ) WITH THE CONS-PIRACY OF THE COLLUSION BY THE MONITORING AND & OR BY OTHER MEANS OF THE SPYING FOR THE COLLECTION OF THESE COMMUNICATIONS WITH THE LACK OF THE AUTHENTIC-AUTHORIZATION BY THE CONTRACT-PARTIES ARE WITH THE VOID-CLAIM FOR THE LACK OF AN AUTHENTIC-AUTHORIZATION FOR THE USE, [ RE ] VIEW, [ DIS ] COVERY- [ DIS ] CLOSURE AND : [ DIS ] TRIBUTION & TRANSFER OF THE KNOWLEDGE IN THIS E-MAIL. FOR THIS AUTHOR AND : SENDER OF THIS E-MAIL IS WITH THE CLAIM OF THE RIGHT OF A LIVING-SOVEREIGN WITH THE LACK OF THE [ A ] PARTHIED AND [ RE ] COURSE BY THE CLAIMANT. FOR AN OMISSION OF THE KNOWLEDGE IN THIS E-MAIL IS WITH THE LACK OF A CLAIM FOR A WAIVER OF THE OWNERSHIP-RIGHTS AND & OR : RIGHTS WITH A CLAIM BY THE AUTHOR.
FOR A CLAIM WITH THE PRINCIPLE IS WITH THE SAME-CLAIM WITH THE [ A ] GENT. FOR A CLAIM WITH THE [ A ] GENT IS WITH THE SAME-CLAIM WITH THE PRINCIPLE. XXXX XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have taken a major drop in my credit score because od this fraudulent account. I have never had an account with this company or agreed to any contractual agreement to repay A debt. On my credit report it says the original creditor was A place called XXXX XXXX XXXX. When I contact XXXX XXXX XXXX they inform me this was an online loan taken out back in XXXX and the debt has been sold to this company. This is A FDCPA violation! This company is reporting inaccurate FRAUD information on me report with A open date XX/XX/XXXX! I have informed the original creditor this is A fraudulent account as I have never had a loan taken out with anyone."
}
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": "Other debt",
"Consumer complaint narrative": "My Husband and I lived in these apartments back in 2013 and when we went to move out, My husband verbally told an office employee and he was told that she would put in the paperwork and begin move out process. We had 60 days. When we came to hand in some things to the office, the manager said she never received anything from the other worker that we were moving out. That was around 35 days we were supposed to move and we already had a house to move into. Well, She told us that it was ok that mistakes are made and she understood. Then we get a bill from a collection agency that we owe two months rent for breaking our lease. We absolutely did NOT break our lease. Not that this would matter, but this manager has done this to others we have know in that apartment complex. This is now a negative remark on our credit and a debt over XXXX that we do not owe. That company has always been very suspicious to us when we lived there. Everything that we requested while living there was always verbal, never had to fill things out. We should no AT ALL owe this. Very unfair and it needs to come off our credit. I have also disputed this with credit companies as well and nothing ever happens. I am aware that there was a blind broken and a hole that i believe they added to that bill, and i will be more that willing to pay for those."
}
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": "Experian Experian National Consumer Assistance Center XXXX XXXX XXXX XXXX, TX XXXX RE : Social Security Number:XXXX To Whom It May Concern : I have recently been informed that there is negative information reported by XXXX XXXX in the file you maintain under my Social Security number. Upon reviewing a copy of my credit report, I see an entry listing chargeoff/collection/late on XX/XX/2017.
I challenge the accuracy, compliance and reportability of this listing.
Please validate this information with XXXX XXXX and provide me with copies of any documentation associated with this account, bearing my signature. In the absence of any such documentation bearing my signature, I formally request that this information be immediately deleted from the credit file you maintain under my Social Security number.
Please note that you have 30 days to complete this investigation, as per the Fair Credit Reporting Act section 611 ( a ) ( 1 ) ( A ), and I am keeping careful record of your actions, including your Method of Verification. I do not consent to XXXX or any means of automated verification.
Failure to respond satisfactorily within 30 days of receipt of this certified letter may result in a small claims action against your company, seeking {$1000.00} per violation for : 1. ) Defamation 2. ) Negligent Enablement of Identity Fraud 3. ) Violations of the Fair Credit Reporting Act My contact information is as follows : XXXX XXXX XXXX SSN : XXXX XXXX XXXX XXXX XXXX, CA XXXX P.S. Please be aware that dependent upon your response, I may be detailing any potential issues with your company via an online public press release, including documentation of any potential small claims action"
}
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": "Hi I am submitting this XXXX XXXX this isn't any influence and this is not a third party. XXXX has low and unfair credit number for me in their report. I have complained more than 88 times nonstop this is exhausting im depressed they are not putting these fraudulent inquiries in dispute and accounts and they still haven't deleted them either I am depressed. The problem has not been resolved. my fico has me at a credit score over 709 XXXX has me at a score around 590. That is a huge difference. XXXX paints me as a XXXX. my fico say I have good credit. What the heck is going on here. i have almost no debt and my identity was stolen causing my score to drop n i made this clear for 90 days straight with XXXX i spoke to a representative agent name XXXX and XXXX and XXXX and XXXX and XXXX from the fraud department I prefer to speak to a us rept but they refused they had me on mute for 4 hours which was hurtful I have a perfect repayment record. I have very low credit utilization. I have one negative credit items outstanding debt now that is not mines. I have modest but ok income. Social Security. Something is wrong with XXXX. I do not understand why they are abusing consumers they don't help you at all This was a first step towards attempting resolution. They kept lying telling me they disputed n its not reporting but it keep reporting this inaccurate information without my authorization. They refused or were unable to verify n remove the inquiries and negative account its been 60days n they record the calls n admitted they had my police report n ftc and affidavit That was after attempting to contact XXXX more than 88 times in the last 120 days. XXXX is an abusive company. They are supposed to be protecting consumers. They need to be reigned in. they are causing me severe XXXX and stopping me from getting this job offer n now im homeless n cant provide to my XXXX year old daughter PLEASE HELP ME PLEASE im depressed now.with no help they keep ignoring me after I keep telling them repeatedly they are hurting my character of my name they won't respond and it not even in investigation with and me and my son are bout to be homeless please help I cant live like this The credit bureaus never did a proper " investigation '' according to the FCRA. Section 1681a of the Fair Credit Reporting Act defines an investigative consumer report as a consumer report or portion thereof in which information on a consumer 's character, general reputation, personal characteristics, or mode of living is obtained through personal interviews with neighbors, friends, or associates of the consumer reported on or with others with whom he is acquainted or who may have knowledge concerning any such items of information. They did not do this. Also they have violated my rights even more. The credit bureaus stated my account was properly investigated but how is that possible if I never gave these creditors WRITTEN CONSENT OR AUTHORIZATION TO USE MY PERSONAL INFORMATION TO OPEN OR CLOSE ANY ACCOUNT. THESE BALANCES IS IN ACCURATE, PAYMENT STATUS AND HISTORY IN ACCURATE, LAST ACTIVITY DATE IS IN ACCURATE, ALL INFORMATION REGARDING THESE NEGATIVE INSTALLMENT LOAN IS INACCURATE.Someone is using my personal information pretending to be me to obtain credit. I don't know anything about this fraudulent account. I don't have any closed OR OPEN accounts with THESE LISTED CREDITORS BELOW WITH MY DOCUMENTATION. Also they have violated my rights even more In accordance with the Fair Credit Reporting Everything has to be 100 % accurate on my credit report. The credit bureaus stated it was properly investigated but how is that even possible if THESE CREDITORS DID NOT GET ANY WRITTEN SIGNATURE, CONSENT OR AUTHORIZATION FROM ME. This account is fraud and does not belong to me, this is in accurate, I have never opened or closed any account with THESE CREDITORS I ask for documents with signature or authorization, they did not submit my request.This grounds for removal and deletion immediately. I never gave THESE CREDITORS ANY WRITTEN CONSENT OR AUTHORIZATION TO USE MY PERSONAL INFORMATION TO OPEN THIS ACCOUNT. THIS IS FRAUD. I'VE ASK FOR SIGNED DOCUMENTS WITH MY SIGNATURE GIVING WRITTEN CONSENT TO OPEN THIS FRAUDULENT CREDIT ACCOUNTS. SOMEONE USE MY PERSONAL INFORMATION PRETENDING TO BE ME WITHOUT MY AUTHORIZATION OR CONSENT TO OPEN OR CLOSE ANY CREDIT ACCOUNT WITH THESE CREDITORS BELOW They also violated the law because according to 15 U.S.C 1681 section 602 A. states i have the rights to privacy 15 U.S.C 1681 section 604 section 2 : it also states a consumer reporting agency can not furnish a account without my written consent. In accordance with the Fair Credit Reporting act WITH THESE CREDITORS BELOW EQUIFAX AND XXXX AND XXXX has violated my rights. 15 U.S.C 1681 section 602 A. States I have the right to privacy. 15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1666B : A creditor may not treat a payment on a credit card account under an open end consumer credit plan as late for any purpose SECTION 604 15 U.S CODE 1681 B THE LAW STATES THAT NO ONE CAN FURNISH INFORMATION ON MY CREDIT REPORT WITHOUT MY CONSENT. SECTION 605 A 15 U.S CODE 1681 C-1 WITH NO WRITTEN CONSENT OR INSTRUCTIONS THAT WOULD IN FACT BE IDENTITY THEFT SECTION 605 B 15 U.S CODE 1681C A copy of section 605B of the Fair Credit Reporting Act, whichrequires you to block the.fraudulent information on my credit report resulting from identity theft within four business days and to promptly notify the furnisher ( s ) of that information. Please block this information from my credit report, pursuant to section 605B of the Fair Credit Reporting Act, and send the required notifications to all furnishers of this information."
}
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": "iI have reported diffrent things appearing on my credit that are dealing with identity theft i have told the experian, XXXX, and XXXX and sent in police reports and im not able to get items removed the people were using my identity for alot of diffrent things"
}
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": "Other debt",
"Consumer complaint narrative": "I had XXXX satellite TV services they were no longer able to provide after I moved to locations the signal was not able to reach. I returned the equipment in the packaging they supplied, confirmed they received it and paid the final amount owed. Since then, XXXX continued to attempt collection, and repeatedly turns the account over for collection. The latest collection agent ENHANCED RECOVERY COMPANY, XXXX XXXX XXXX, XXXX, FL XXXX ( XXXX ) XXXX Reference No XXXX. About every 2-3 months, I 'm notified of another collection account and have to dispute it with the credit bureaus, who delete it until the next one pops up.This damages my credit history and scores, and necessitated time and aggravation while I needed to rest and heal while undergoing medical treatments. Please help to remove this incorrect information and hold these companies accountable for their inaccurate, sloppy and damaging practices."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was paid"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I SENT TO XXXX XXXX TO REQUEST VALIDATION OF THE ACCOUNT AND THEY SEND ME BACK JUST A COPY OF THE TRANSACTION AND THE AMOUNT WITH NO SIGNATURE AS I REQUESTED IN MY VAILDATION LETTER TO SHOW THAT I OWE THIS DEBT WITH MY SIGNATURE AND THAT WAS NOT THE RESPONSE I RECEIVED. IT IS BEING REPORTED ON ALL 3 CREDIT BUREAUS AND THAT IS AGAINST THE FCRA LAW TO REPORT SUCH IF CONSUMER BARES NO SIGNATURE OF BEING RESPONSIBLE FOR SUCH TRANSACTION. I REQUESTED A VAILDATION 3 TIMES AND THEY FAILED TO SEND ME ANYTHING WITH MY SIGNATURE STATING THAT I AM RESPONSIBLE FOR THE ALLEGED TRANSACTION."
}
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": "Transunion has violated my federally protected rights,. I recently received a copy of my credit report, and I found unauthorized credit inquiries made by XXXX and XXXX Theses inquiries were made without authorization, and without legitimate business purpose. Please remove these unconfirmed inquires lacking certifiable evidence unless physically certified and verified proof is demonstrated and prided per U.S. 15 U.S.C. 1681 SECTION 604 FCRA. These companies accessed my report without my consent 15 U.S. Code 1681b no permissible purpose these are not my inquires"
}
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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "I took out my loan on XX/XX/2019. I have since found out that their loans are not legal in the State and not licensed to do business here. I have revoked my ACH withdrawal authorization and notified them to not contact me via phone. They have texted me since."
}
Output:
{
"Issue": "Struggling to pay your 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": "Other debt",
"Consumer complaint narrative": "I have an amount of {$540.00} owed to a credit union bank, which was my job bank card for direct deposits, the total amount was around {$350.00} that was owed to the bank that I was unaware of. It was sent to collections, XXXX I agreed to make {$50.00} installments monthly to Professional credit services, with the promise of agreed to pay, and deleting the collections off my credit report, my credit has been updgraded 5 months after, and I have called seeing a balance of {$300.00} left on there and it still shows my amount is {$540.00} and not paid."
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "On XX/XX/2022, opened new checking and savings account in response to {$300.00} bonus offer for {$5000.00} direct deposits. Having met the direct deposit condition on XX/XX/2022 and waited the required payout time, contacted company on XX/XX/2022, and was informed I did not qualify since I had a previous bonus for a Sofi XXXX account. However, I presented the screenshot of the terms at the time of account opening which did not preclude this during a chat session. Agent informs me the terms had not changed but I provided evidence that the terms were changed when the company extended the bonus offer on XX/XX/2022. Because my account was opened under the attached terms, my eligibility remains valid and I ask CFPB to enforce the {$300.00} bonus payment."
}
Output:
{
"Issue": "Opening an account",
"Sub-issue": "Didn't receive terms that were advertised"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Auto debt",
"Consumer complaint narrative": "Westlake Financial is trying to collect on a debt that was paid off in a sale of the vehicle that I voluntary gave back due to them asking me a lemon. They are reporting this on my credit report and I am just now see it on my credit report four years later. It is pass the Statue of limitations for the State of California.
The amount they are stating I owe is {$630.00} For 2012. For a XXXX 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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I moved from XXXX to XXXX to start a business. I have never worked for this company. For the last 4 months, people from XXXX XXXX XXXX XXXX XXXX have been calling me telling me to pay them money. I have never gotten money from them. They keep telling me that I was an insurance agent for they company. I have explained to them over and over that I took a XXXX Insurance Agent test but I have never worked as an insurance agent. Today XXXX XXXX, 2016, I received an alert from XXXX that XXXX XXXX XXXX XXXX XXXX has reported an account as unpaid. I do n't know what 's going on. This company is committing fraud because I have never worked or received money from them."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Information 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": "There has been a card opened up on my credit report that just showed up today. The card was dated as being opened XX/XX/2021. I contacted capital one bank and they could not find the card in my name but it is showing on my credit report."
}
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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Tried to obtain my free annual credit report using AnnualCreditReport.com ( ACR ) but Equifax keeps denying access to the report and instead redirects me to a page where I have to buy the report. Also, the ACR website will not allow me to submit a complaint about the problems with Equifax."
}
Output:
{
"Issue": "Unable to get credit report/credit score",
"Sub-issue": "Problem getting my free annual report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XXXX I disputed the inaccuracy of a collections account XXXX & XXXX. This account is reporting as open, with XXXX XXXX, and XXXX. This is a violation to report a charged off account as open. This needs to be removed immediately."
}
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": "Debt collection",
"Sub-product": "Private student loan debt",
"Consumer complaint narrative": "XXXX XXXX XXXX XXXX XXXX XXXX XXXX contacted me about an alleged debt. I responded with a debt validation letter requesting several items. They sent me two of the things I asked for. I responded telling them that they did not provide what I asked and that if they did not provide it, I would assume that they did not have the right to collect on it. They did not respond. Instead, they sued me. I know they are currently barred by the CFPB in regard to filing suits in which they do not have proper paperwork but yet they are still doing it."
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Sued you without properly notifying you of lawsuit"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "Some time ago I lost XXXX speeding ticket trials and was forced to pay the fines. Due to unemployment issues I was unable to pay the fines. The fines were sent to GC Services where they got my driver 's license suspended. I sent a cease and desist certified letter to GC Services while I determined what was going on. I found out the GC Services is extorting money from me illegally so that I can get my license back. Since I do n't have the money nor will I for quite some time, I am sure this act of extortion is illegal. This action also places a huge burden on my employment situation since I ca n't drive to get to work. Without a job, how can I pay them anyway? For the record, I fought XXXX tickets in court and from my point of view I won. But was found guilty. I fought XXXX tickets in appeal and from my point of view again, I won. I lost XXXX appeals. I have already filed complaints with every state and federal agency asking for an investigation and so for I ca n't get any help. I am hoping that the CFPB can help.
I define extortion as an act of forcing someone to pay for something they ca n't. GC Services suspended my license forcing me to pay or I will never be able to drive again. Since I do n't have the money and I ca n't get a job without paying, they are extorting money from me ... illegally."
}
Output:
{
"Issue": "Taking/threatening an illegal action",
"Sub-issue": "Seized/Attempted to seize property"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I received a credit alert indicating a unknown status for XXXX and XXXX on my 2 students loan accounts which hit my credit file with a hard hit and decreased my credit score by 60 points because of incorrect information sent to my credit file.. XXXX sent incorrect information for late unknown payments 1 for {$18.00} and the other for {$22.00} with a balance increased for the month of XXXX when my accounts are in a postponement status and pending with the Borrower 's Defense. I'm confused because the late payments hit my credit file back in XXXX and was a significant hit.. They have now done another hit indicating unknown and Transunion is reporting the incorrect information."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Received bad information about your loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "After filing four disputes on XX/XX/2021, Equifax failed to timely investigate and remove the inaccurate, outdated, and/or unverifiable information that I disputed. In addition, Equifax failed to indicate the actions it allegedly took in response to my disputes, when it finally acknowledged one of my disputes on XX/XX/2021.
I again ask that Equifax remove the inaccurate, outdated, and/or unverified information that I requested more than eight months ago, in my disputes numbered XXXX, XXXX, XXXX, and XXXX.
I also reserve all my rights under state law to bring civil action against Equifax for these failures."
}
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": "On XX/XX/19, I filed a complaint with Capital Accounts on the CFPB website. The Companys response was incomplete and, in one instance contains, I believe, erroneous data. I was advised by the Consumer Financial Protection Bureau that the proper way to handle the situation was to file a new complaint to address the three outstanding issues.
The first issue deals with the XXXX XXXX XXXX that Capital Accounts sent to me. It shows total charges of {$3100.00} with my insurance company ( XXXX XXXX ) paying only {$330.00}. This can not possibly be correct. I have excellent dental insurance through the XXXX XXXX XXXX XXXX XXXX XXXX. Depending on the service provided, the cost to me would be 75 %, 50 %, or free.
The XXXX XXXX XXXX shows the payments from XXXX XXXX that were made. XXXX would have received an Explanation of Benefits ( EOB ) along with each payment. The EOB would show each line claimed and the amount paid. I believe the EOB will show XXXX failed to submit all the claims. Please have XXXX provide a copy of the EOBs so we can resolve this issue.
In support of my comments re the levels of benefits that would be paid under my dental policy, Ive attached an EOB and some Pre-treatment estimates.
Second, in their last response, Capital Accounts stated that they had accrued interest in the amount of {$530.00} but did not provide any details e.g. dates and amounts of the interest charges. Please do so. I would also like a copy of the agreement that I signed that allows me to be charged interest. In the absence of such an agreement, I have been advised that I can not be charged any interest.
Third, in their last response, Capital Accounts said I have made {$500.00} in payments. Originally I had requested that Capital sent me a detailed schedule showing the dates and amounts of all payments so I could reconcile the payments to my records. They have not done so.."
}
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": "Bank account or service",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Hello, I 'm a XXXX and a new Chase Bank checking account customer. I was familiar with the case against XXXX Bank XXXX. in which they failed to correct deposit discrepancies below a certain threshold, resulting in millions of dollars in lost funds to depositors. Today I received a notice with my bank statement from Chase informing me that there were going to be changes to my account terms. XXXX of them included a statement that any deposit slip discrepancies below {$10.00} would not be corrected unless they were found by the account holder and reported within a certain timeframe. This would result in the same situation that occurred with XXXX Bank XXXX. I submitted an email to Chase, and included a link to the press release for XXXX. I have only made XXXX in-person deposits to my Chase account, the rest have been electronic. XXXX deposit was made via their ATM, and it did not have an option to use a deposit slip or a deposit envelope. The deposit amount was determined via an internal scanner. There is also no envelope for depositing cash, you just feed cash into the machine. Due to potential mechanical failure, I have chosen not to deposit cash through this machine. I prefer to have a sealed envelope, with no risk of comingling of cash that might make it impossible to fix a system glitch. Additionally, I prefer to calculate my deposit and write in on the envelope. I understand that there is a risk of fraud by consumers who deposit empty envelopes, however, if you are n't going to correct discrepancies and rely on a computer to complete the transaction, then there is a much greater risk to consumers, as proven with the case of XXXX Bank."
}
Output:
{
"Issue": "Deposits and withdrawals",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I the consumer/ natural person and original creditor sent affidavit of truth along with exhibits addressing the issue with my report on XX/XX/2020 with invoice violations of 15 USC 1681b ( a ) ( 2 ). As of 11/27/2020 I have not received any mail with my report about the issues the bureaus did not get my written instructions to furnish the information I listed as exhibits."
}
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": "Mortgage debt",
"Consumer complaint narrative": "Me and my boyfriend moved into XXXX XXXX XXXX on good standings.We paid our rent every month and never gave our neighbors any problems. My boyfriend is in a XXXX and we explained that to them. Everything was ok in the beginning until things started going bad with my neighbors upstairs apartment. The people upstairs had a bad leak that took them days until they decided to come fix it, they did n't treat the problem properly just covered it with paint and the problem kept coming back. We asked time and time again we put in work order after work order for them to fix the problem but they just brushed it off. Well due to their brushing the problem off, the walls accumulated mold which caused my boyfriend to catch a XXXX XXXX that put him in the hospital for many months at XXXX XXXX. We were not the only people that had mold and the apartments knew that. We stayed in our lease and told the leasing office about our situation and they still did not want to fix the problem or work with us. We did n't leave because we just wanted to, we left because it was a XXXX emergency We had proper documents from the Hospital and Doctor appointments. Not only that allot of crime was going on inside of the apartment complex and the leasing office felt like it was ok not to inform the residents on what was going on, my neighbors right next door and upstairs apartment got broken into and a lady three doors down got XXXX and when I asked the leasing office what they were doing about it as far as safety, they told me it was none of my business and they did n't feel like it was a concern. Not knowing that I knew already that the man who did it stayed there and still had a gate card and a key due to another neighbor telling me what was going on. They had people running through the bushes running from the cops and one of the guys ran into my apartment as I was going in with my niece, when I told the leasing office about it all the lady said was " Awww I 'm sorry to hear that '' .The gate was never working and security was just letting anybody in. I 'm a female and my husband is in a XXXX it just was n't safe for us. Even though we was right down the street from the jail the police still take their time to get there."
}
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": "Store credit card",
"Consumer complaint narrative": "While paying for groceries at our local XXXX store the cashier asked if we would like a Walmart card we said no and she said we would get {$25.00} off our bill for signing up and we could cancel if we decided we did not want it.We were paying cash and spending close to {$200.00} so my husband said ok and started giving her the information she needed. I really did not want him to get it and told him but he was paying with his cash so I backed off. This is why I remember this incedent. So when he paid the cash he looked at the receipt and the {$25.00} discount was not on it. She said oh it will come off when we get the card on the next bill, he said that's if I use it, I signed up because I thought it come off this bill, so we left a little upset because we felted miss led. This was last summer I'm guessing around XXXX or XXXX, then forgetting about the incedent we got the Walmart card and the statement in the mail and when he looked at he said how can we owe this when it just came in the mail? The bill was over a XXXX dollars and I said that's crazy I think they charged us what we paid cash for. No {$25.00} discount was on the statement. So he called them and they wanted proof and frankly never believed us. I started looking for the receipt and I must have thrown it out. So without that we don't have solid proof, so when we asked them for proof of our purchases all they said was they would check into it. From then on it seemed the burden was on as and frankly I feel they ripped us off and they have not shown us proof of purchases yet they say we are uncooperative. We do not want the card asked them to cancel it. They said after investigating they felt we still owed it. That is rediculus. How could we charge anything when it wasn't issued yet and where did the cash go - in someone's pocket. This is exactly why I hate credit cards. We do not owe this and XXXX has lost my affection and respect they are just another ripoff retailer."
}
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": "Hi I am submitting this XXXX XXXX this isn't any influence and this is not a third party. TransUnion has low and unfair credit number for me in their report. I have complained. The problem has not been resolved. my fico has me at a credit score over 719transunion has me at a score around 590. That is a huge difference. transunion paints me as a XXXX. my fico say I have good credit. What the heck is going on here. i have almost no debt and my identity was stolen causing my score to drop n i made this clear for XXXX XXXXays straight with transunion i spoke to a representative agent name XXXX and XXXX and XXXX from the fraud department I prefer to speak to a us rept but they refused they had me on mute for XXXX hours which was hurtful I have a perfect repayment record. I have very low credit utilization. I have XXXX negative credit items outstanding debt now. I have modest but ok income. Social Security. Something is wrong with Transunion. I do not understand why they are abusing consumers XXXX was a fist step towards attempting resolution. They kept lying telling me they disputed n its not reporting but it keep reporting this inaccurate information without my authorization. They refused or were unable to verify n remove the inquiries and its been XXXXdays n they record the calls n admitted they had my police report n ftc and affidavit That was after attempting to contact Transunion more than XXXX times. transunion is an abusive company. They are supposed to be protecting consumers. They need to be reigned in. they are causing me severe XXXX and stopping me from getting this job offer n now im XXXX n cant provide to my XXXX year old daughter PLEASE HELP ME PLEASE im XXXX now.with no help."
}
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": "I would like all fraud alerts removed from my credit report"
}
Output:
{
"Issue": "Problem with fraud alerts or security freezes",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "Someone has opened some accounts in my name that are fraud I will be filing suit against XXXX XXXX XXXX There are XXXX XXXX accounts on my credit profiles and XXXX XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Dear Sir, I am going through the XXXX Credit Report dated XX/XX/2018 and also checking inquiries on my report. I have closed all my individual chase credit cards on XX/XX/XXXX and XX/XX/2018 respectively. I have noticed that, XXXX credit report has fraudulent inquiry of Chase Credit Card dated XX/XX/2018. Chase is a fraudulent credit card company and hence I have closed all the credit cards, same has been recorded in CFPB complaints, Fraud and Identity complaints.
Why did chase inquire about my social when I have not contacted chase card or I do not have any plans to have any kind of chase credit card. Could CFPB direct chase card/XXXX to explain me the reasons of this inquiry dated XX/XX/2018. Could CFPB direct chase card/XXXX to issue me an official letter on this inquiry, anyone fraudulently obtained any card using my social, need complete details of this fraudulent inquiry dated XX/XX/2018 on my XXXX Credit file."
}
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": "Conventional adjustable mortgage (ARM)",
"Consumer complaint narrative": "I locked in a rate with Loan Depot on XXXX/XXXX/XXXX to refinance my mortgage on my property in XXXX, XXXX After 4 months of run around, they informed me that I needed to bring $ XXXX cash to closing so I can close on the new loan! Considering we started with minimal cash to close, they changed their mind 4 times and each time the cash to close went higher.
It 's worthy to note that the consistent increase was not due to any new information I provided after the initial set of information they received, but after a long period of radio silence, when I was not getting responses to my emails or phone calls and messages, they told me that cash to close was increased because they had just noticed my Home Owner 's Association fee of {$120.00}! And for that they could n't close my loan unless I came in with $ XXXX in cash! Mind you, they had all this information from XXXX XXXX, but just realized it in XXXX XXXX, after they changed their interest rate and cash to close amount 3 other times, consistently going higher every time until it reached an amount that I had no choice but to drop the refinance.
I was dodged around among XXXX people for the 4 months and each one of them gave me a different reason for their unjustified very very late decision.
They wo n't even refund my appraisal fee so I can go pursue another refinance since I still need to change my mortgage from an interest only ARM to a 30 year fixed.
I am totally shocked with the lack of professionalism and ethics of this financial company and the fact that they are out for your money at any cost. I will never do business with them! Hope you wo n't either!"
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I was perpetually charged for ATM fees, overdraft charges and other misc charges keeping my account in the negative until I ended up loosing my house"
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Deposits and withdrawals"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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 was attempting to get a loan for a new house, when the lender informed me that my credit had dropped from XXXX to below XXXX and that I may not qualify for the loan. finding that Amcol systems collection agency had been sending a medical bill invoice to the wrong address for many months and attempted to call a 12 year old d isconnected numb er. I have no problem paying the bill and it has been taken care of. However may complaint is that I requested for AMCOL to remove it from my credit report as it was their fault in destroying my credit. " two of their employees told me that do n't do that and I could go pound sand '' This company is rude, and has no respect for someone with great credit, that was destroyed by their company and their lack of professional work ethics. the loan company is reviewing my credit information now and I may be denied a loan for a home of my dreams due to this company refusing to remove a bad debit that was created by a w rong address."
}
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": "To whom it may Concern, I informed the credit reporting agencies that the account XXXX balance {$320.00} account name XXXX XXXX XXXX is an account that should not be reflecting on my reports because the account has been paid off. I asked that these accounts be verified and unfortunately they have not due to the affect that they still are reflecting on my reports as a collection account. I am asking that these accounts be removed immediately. I have proof that this account was paid XXXX. Also there is another account that needs to be removed from my credit report due to identity theft. The account name is XXXX XXXX XXXX. The account number for this account is XXXX, and the balance on this account is {$2100.00}. I never had this account and this account was not opened with my permission. Please remove as soon as possible.
Also the only names that should be reflecting on my personal information on all three credit reports is XXXX XXXX XXXX. The only address that I should be affiliated with is XXXX XXXX XXXX XXXX XXXX. The only telephone number that I should be attached to on my credit profiles is XXXX only and the only Employers that should be listed is XXXX XXXX. Please update with all credit reporting agencies.
Thank You with kind regards, XXXX XXXX XXXX"
}
Output:
{
"Issue": "Credit monitoring or identity theft protection services",
"Sub-issue": "Billing dispute for services"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "Dear Sir/Madam : PLEASE ADD MY PHONE # Telephone : ( XXXX ) XXXX TO MY CREDIT FILE!
Dear Credit Reporting Agency : I am an identity theft victim. I am writing to NOTIFY YOU OF THE ACCOUNTS LISTED IN THE AFFIDAVIT. Each of the accounts and transactions listed were opened and made without my knowledge or authorization. I never authorized, used, or benefited from these accounts or Inquires transactions in any way : Pursuant to section 605B of the Fair Credit Reporting Act, and send the required notifications to all furnishers of this information.
RE : IDENTITY THEFT, FRAUDULENT ACCOUNTS & INQUIRES!
Copy of Social Security Card and Drivers License, Copy of Utility Bill Notification of Identity Theft, List of Fraudulent Accounts, FTC Identity Theft Victims Complaint & Affidavit XXXX 1. GM FINANCIAL Account Number : XXXX Please remove it from my credit report.
2. XXXX Account Number : XXXX Please remove it from my credit report.
3. The following personal information is incorrect Account Number : PREVIOUS ADDRESS : XXXX XXXX XXXX XXXX, NC XXXX 4. The following personal information is incorrect Account Number : PREVIOUS ADDRESS : XXXX XXXX XXXX, NC XXXX XXXX. The following personal information is incorrect Account Number : CURRENT ADDRESS : XXXX XXXX XXXX XXXX, NC XXXX 6. The following personal information is incorrect Account Number : EMPLOYER : XXXX XXXX XXXX 7. The following personal information is incorrect Account Number : EMPLOYER : XX/XX/XXXX 8. The following personal information is incorrect Account Number : EMPLOYER : XXXX XXXX 9. The following personal information is incorrect Account Number : EMPLOYER : XX/XX/XXXX XXXX. The following personal information is incorrect Account Number : PREVIOUS ADDRESS : XXXX XXXX XXXX XXXX, NC XXXX XXXX. The following personal information is incorrect Account Number : PREVIOUS ADDRESS : XXXX XXXX XXXX, NC XXXX XXXX. Identity Theft Account Number : XXXX Please remove it from my credit report.
13. Identity Theft Account Number : XXXX Please remove it from my credit report.
14. Identity Theft Account Number : XXXX Please remove it from my credit report.
Please delete the disputed items from my credit report within ( 4 ) four days as required by the Fair Credit Reporting Act."
}
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 acct # XXXX, Equifax Acct # XXXX, XXXX Acct # XXXX, has violated my rights.
15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "This is in response to CFPB complaint # : XXXX, that was closed because EXPERIAN said that they " VERIFIED ". <P/>CFPB said that EXPERIAN responded and verified that the disputed information was accurate as reported?. The result summaries were sent to you for review on XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX XXXX and XXXX XXXX XXXX? <P/>THIS IS INCORRECT. THEY FAILED TO SEND ME PROOF OF VERIFICATION FOR EACH ITEM IN DISPUTE. Under the FCRA 15 U.S.C. 1681i, all unverified accounts must be promptly deleted. Therefore, if Experian are unable to provide me with a copy of the verifiable proof that they have on file for each of the accounts listed within 30 days of receipt of letter then EXPERIAN must remove these accounts from my credit report. <P/>Only that I requested to EXPERIAN is that send to me proof of verification on items in dispute, and they failed in 9 certified mail letters that I send to them. I have copy of each letters. <P/>The LAW is clear, all unverified accounts must be promptly 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": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I used my fifth third MasterCard for a deposit with a merchant, the merchant has not provided the service promised. I tried to resolve with the merchant and they were not willing too. I then disputed the charge with fifth third MasterCard because the service was not provided the merchant withdrew from fulfilling our contract. I also made fifth third MasterCard disputes department aware that the merchant has no return or refund policy posted on their webpage where I paid my deposit. To my understanding this is a requirement from XXXX and XXXX. When I spoke with fifth thirds dispute department they confirmed this and assured me they were disputing this charge with the merchant"
}
Output:
{
"Issue": "Billing disputes",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "There are unknown hard inquiries on my credit report. Im requested the verification of the inquiries and the method of verification.
On XX/XX/2020, XXXX XXXX add a hard inquiry on my credit report per XXXX.
On XX/XX/2020, XXXX XXXX add a hard inquiry on my credit report per XXXX.
On XX/XX/2020, ALLY FINANCIAL add a hard inquiry on my credit report per XXXX.
But companies did not provide the following proof : Permissible Purpose My written authorization Proof that I personally initiated these inquiries.
To my knowledge, I have not signed any documents authorizing your organization to view my credit history therefore, your inquiry into my credit report violates the Fair Credit Reporting Act, Section 1681b ( c ) : Transactions Not Initiated by Consumer."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "My credit Report under my social was my Mom 's name, I was listed under AKA my name and my sister.
ALL of the information is incorrect!!
Was on the phone all day yesterday to fix it. They said they corrected it. Now my credit report show 's my name but as my sister 's name affiliated with my social security number.
AKA lists my name and My sisters ( twice ) with her first name then with her Middle name.
There are several address and loans. All the Addresses and loans are a combination of my mom 's my sister 's, one address is mine and it does show my mortgage loan. The address and Mortgage are the only correct things of mine.
It shows Their bank accounts, their loans and phones, my sister 's student loans and cars ... .and XXXX all credit reports run.
I own one house I have one car- paid off, no loan I have only one mortgage I have worked only one job in the past 20 years I don't have any credit cards or student loans I have one phone My only bills are my mortgage, my utilities and my cell phone.
Identity theft is apparent on Experian- All the address, phones, loans are in my name- All address are in Virginia, except one in Alabama which is my Mom 's wrong address number correct street address..
My mortgage is shown ( never late ), others mortgages are on it as well. Loans, credit cards, different Bank accounts all from Virginia, Connecticut and North eastern banks. Credit and delinquent loans under 6 different po box es in Virginia. I do not know any of them.
This is an obvious Identity theft situation that is serious. Thousands of dollars and accounts, loans, credit cards in My name!"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Public record information inaccurate"
} |
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