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You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "i have been trying to get a resolution with the lender and we keep going back in forth with mixed updated. When I call they advise my file was under review but they later advised that it was removed due to missing documents.
I am just getting the run around when i am trying to save my home. They even advised me to file a complaint and don't care about their customers Please help"
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have filed a dispute in regards to an inaccurate Account on my consumer report. It has been well over 60 days and I haven't received any investigation results."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Investigation took more than 30 days"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I file disputes with Experian and their response was that it wasn't made by me. SERIOULLY!!!!! ... I fill this is a stall tactic on their part and I should not have to go thru all this just to get my results. This is taking time away from my job to have to keep filing complaints. Please help me with this matter."
}
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 have disputed erroneous accounts with this company several times now, I have attached all the proof this company needed in regards to verifying the inaccurate accounts. But to my surprise, this company has not fixed anything whatsoever. Up to now my credit report still has the same inaccurate accounts that I had disputed. For yet another time, please rectify all the errors mentions below : 1. XXXX XXXX XXXX XXXX XXXX Account # XXXX I have always paid in time, please delete this erroneous late payment and Charge-off.! 2. XXXX XXXX XXXX- Account # -XXXX - I have always paid in full, please remove the erroneous late payments history Asap This account has never been charged off please rectify immediately. 3. XXXX XXXX XXXX Account # XXXX - Please investigate and tell me why this account has been charged off. This account was just late for a month after which it was settled in full. Please remove this late payment and charge-off asap. 4. XXXX Account # XXXX - The data youre reporting is completely false and must be removed immediately, I have never always paid in time. thank you kindly for honoring my request. 5. XXXX XXXX XXXX Account # XXXX I would like to request a deletion of late payments History since I have always paid in a timely manner without fail. 6. XXXX XXXX XXXX Account # XXXX - I have always paid on time, the 120,60 and 30 days late payments are erroneous and must be removed immediately, thank you. I have attached herewith my recent dispute to this company."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "On XX/XX/2019, multiple emails were received from Synchrony Bank, the issuer of the Amazon Prime store credit card. These emails contained lines such as : " A Trial Deposit Has Been Successfully Made to Your AMAZON CREDITBUILDER '' " Action required on your application '' In doing a quick search, it appears this happened to thousands of Synchrony Bank customers today. They have not notified customers, and when reached, their phone reps hang up the phone.
This appears to be a major breach of some sort, and they are not addressing it appropriately."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Privacy issues"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I want XXXX account and collection removed from my credit reports. Ordered products from XXXX all the way back in 2013, never received my order as it was sent to an incorrect address I no longer lived at in a different state. I dont know how they got that address as that's not what I put. When I saw it was being shipped to incorrect address I contacted right away to inform to change address as it hadnt actually shipped yet. They refused to change to correct address. After they shipped, I contacted fedex and they said the shipper could change the address with them for delivery. They would not. Was delivered to my old address in different state. I even contacted apt office at that address and they said my package was delivered there but they gave it back to fedex to return to sender since I didn't live there anymore. XXXX should have received package back but still kept my bill for order as is. They would not reship to me. Obviously I was not going to keep paying for an order and goods I never received. No one would help me with this and ended up getting bill sent to collections with collection account and closed/charged off XXXX account show on my credit report which is not fair again for goods I never even got. Collection company would not remove when I disputed. I have screenshots from 2013 of all the emails I sent to XXXX informing about the address and trying to get them to do something."
}
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": "Debt collection",
"Sub-product": "Medical",
"Consumer complaint narrative": "*Complaint number XXXX of XXXX My credit report shows collections accounts that seem to have been unlawfully re-aged by the collection agencies and law firms that hold the debts.
In XXXX XXXX, I had an emergency hospital stay and did not have insurance, so I was billed for the stay. The bill was split up into at least XXXX different collections accounts for varying amounts between {$100.00} and {$2000.00}. XXXX of the XXXX show dates far later than XXXX XXXX.
Those items are : - XXXX/XXXX/XXXX : XXXX - {$1900.00} - XXXX/XXXX/XXXX : XXXX - {$100.00} - XXXX/XXXX/XXXX : XXXX - {$980.00} These debts were all incurred in XXXX XXXX, but the date of last activity for each of them incorrectly indicates a much more recent date. I have had no contact with any of these creditors and have taken no action that would result in a new date of last activity. I feel that these accounts are being re-aged as an attempt to circumvent the statute of limitations set by federal regulation."
}
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": "I previously submitted complaint ID XXXX regarding fraudulent report freeze/unfreeze practices of Experian. In its response Experian said to go to www.experian.com/freeze to unfreeze ( the unwanted and fraudulant freeze they had imposed on my credit report ). On XX/XX/XXXX ( the date of the company 's response ) I opened that and was redirected to https : //www.experian.com/freeze/center.html where I selected Remove or Lift a Security Freeze, which went to https : //www.experian.com/ncaconline/removefreeze. I filled out the form but when I tried to submit it the web site reported missing previous address. The form had no fields for previous address. This form is a fake. It is designed to look like Experian is willing to lift an unwanted freeze while actually making it impossible without buying their " protection '' service."
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I have been contacted several times by phone by Midland Funding to collect a debt that is NOT MINE. This person who owes has the same name, but not date of birth or social security number. I have already confirmed with XXXX person I spoke with and they verified through my DOB and SSN that I am not the person they are looking for and they informed me that they would take my name off the list and not call again. HOWEVER, THEY KEEP CALLING DESPITE THE FACT THEY HAVE VERFIED THE DEBT IS NOT MINE AND SAID THEY WOULD STOP CONTACTING ME."
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "The information that 's on my file isnt being reported accurately per the Fair Credit Reporting Act 609 i requested contract and signature sign and date with accurate account balances from credit bureau that supposed to be in their files i have n't received any information"
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Navient solutions has never once actually sent me any loan deferrment paperwork and without any notice or further contact to actually send what they said they would and even email the paperwork for deferrment on payments, they have reported me to credit bureau for nonpayment Not once not twice but 4 times every month the last four months and have ruined my credit score. They have shown no support or follow through with anything they said they would either via paperwork mailed or sent via email and every month they have the audacity to keep reporting me for nonpayment of a wrong amount and interest on top of it all to the credit bureau."
}
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": "Debt collection",
"Sub-product": "Payday loan debt",
"Consumer complaint narrative": "Illegal interest rate being charged/collected. Bulk of original pricipal paid before being send to collections.
At collections at XXXX - XXXX XXXX XXXX XXXX XXXX, XXXX XXXX XXXX, AZ XXXX Original creditor acct - XXXX XXXX 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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "Navient has a " Document Upload '' form on their site that never accepts documents that you absolutely need to upload, requirements for things like an Income-Driven Repayment Plan. I have tried using this form on multiple computers and it's just not working. They also have no general customer service email to get help with the situation. Not to mention that a form I have already given them in the past has the information they are looking for now. They seriously try to make everything increasingly difficult. I have been working to upload a single form for the last hour and a half. Something that should have taken 1 minute."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Problem with customer service"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "This morning, XX/XX/2019 I spoke with M & T Bank 's Customer Service Department regarding the status of my case # XXXX. This case/claim was filed on XX/XX/2019 for fraudulent use of my checking account with M & T for eight ( 8 ) XXXX transfers totaling {$4800.00}. I was told this morning by that the claim against my case was denied.
To resolve this problem, I am requesting that M & T Bank Fraud division IMMEDIATELY RE-OPEN my case.
I obtained a letter from my employers designated IT Company, XXXX XXXX XXXX to confirm virus, malware, phishing attacks on my computer. This letter explains the virus that were present on my computer on the date that the transfers were made from my account WITHOUT my knowledge or consent.
I need my bank to re-investigate this case and return my fraudulently taken funds of {$4800.00}."
}
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": "I do not know",
"Consumer complaint narrative": "Dear CFPB, I am reaching out in desperate measures to bring to light the way my file is being handled. I have an account listed on my credit report that was NOT me. I have been writing the credit bureaus and the company for the last 4 months or so with no avail. I have explained the problem and also included all necessary documentation such as FTC affidavit and the police report. This is extremely time-consuming and becoming costly with letters and running to the police station. I am writing this complaint to effect immediate change.
The following accounts are opened fraudulently. I never give consent or authorize these accounts. I only discovered these accounts upon speaking to the mortgage officer. I never receive any collection notices about these accounts.
XXXX XXXX {$1500.00} XXXX XXXX {$1400.00} XXXX XXXX XXXX {$1100.00} XXXX XXXX {$620.00} Here are the accounts I have been writing about. I did not open these accounts and I did NOT receive any money or services. I did not sign these applications and I am unfortunately suffering for these accounts being on my credit. I requested both the creditors and the credit bureaus to stop reporting this account. The account is still reporting. My request was pursuant to FCRA 605B ( 15 U.S.C. & 1681 c-2 ) which states that these accounts must be removed within 4 Business Days of receipt. The fraudulent information is still on my credit report and I bring this to your attention. Please review why the companies are neglecting to remove this information as it is extremely damaging."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Money transfer, virtual currency, or money service",
"Sub-product": "Domestic (US) money transfer",
"Consumer complaint narrative": "I wired {$7000.00} to my Coinbase account and it never showed up. The company said they could not complete the wire, even though it originated from the same account linked to my Coinbase account.
To resolve the issue, they said they would reverse the transfer and send it back to my bank account and that I should see it in three business days. The money never showed up. I would like the {$7000.00} returned to my bank account immediately."
}
Output:
{
"Issue": "Other transaction problem",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "Representative named XXXX called my cell phone without authorization failed properly identify right party and herself or company. The representative XXXX called my cell phone number without authorization on a predictive dialer and I incurred charges as a result. This is a violation of the FDCPA. In addition the rep failed to properly identify her self or the company or the right party. When I asked for her to provide the company name and her name she became very rude and unprofessional and terminated the call in the middle of the conversation. When I called back I asked to speak to supervisor whom was also reluctant to provide company name and his name, then he concluded by removing my phone number and ensured there would be no more calls."
}
Output:
{
"Issue": "Improper contact or sharing of info",
"Sub-issue": "Talked to a third party about my debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have a fraudulent account listed on my credit report, called XXXX XXXX, which date was opened on XX/XX/2018. I have disputed this fraudulent account with Experian and they have not removed this account from my credit report and Experian and XXXX is violating the fair credit reporting act by reporting inaccurate information on my credit report. Please remove this fraudulent account immediately and if it is not removed, then I will be filing a lawsuit against Experian and XXXX for not removing this fraudulent item from my credit report. I have attached a police report and affidavit which I already reported to credit bureau already and they have some kind of way verified it accurate and it is not my account. Also, I have attached a copy from the creditor verifying that they have removed this fraudulent item from my credit report. Therefore, I am uploading additional documentation which is related to my dispute."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "I contacted the original creditor several times and asked them. why I owed the {$50.00} dollars balance. at the time of visit I pay them {$50.00} dollars copay and my insurance paid them for the rest. I contacted my insurance company regarding this bill and I was told they got paid what XXXX eligible. this collection company has been calling me and trying to collect what I do not own. I told them stop calling me and they continue calling me and harassing me."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I did business with XXXX XXXX and charged it to my Bank of America credit card. They added a second charge for {$920.00} that I did not authorize or even tell me about. The charge is for towing my vehicle and is for {$920.00}. If they had approached me with this amount I would obviously have denied it as it is not only my responsibility but completely exorbitant.
I reported this to the credit card company and they say that the charge is to a third party, because my insurance coverage with " XXXX XXXX '' was supposed to pay it, but XXXX XXXX would not pay it because they said it was not authorized because of the high price. I would have told XXXX XXXX the same thing IF they had contacted me and told me they were going to charge me that amount for towing.
The bottom line is I NEVER authorized XXXX XXXX to make this charge to my account. I was never even informed by XXXX XXXX that they were going to charge my account. Merchants are not allowed to add charges to a credit card " just because they want to '' without the consent of the credit card owner."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Card was charged for something you did not purchase with the card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I am submitting proof from XXXX credit reporting agency to show that they did a proper investigation in a issue I am having with experian credit reporting agency and XXXX credit reporting agency. There are XXXX accounts I have been disputing for months now that do not belong on my credit reports. Experian and XXXX are not fixing the problem. If XXXX received to delete these XXXX accounts then experian and XXXX should be receiving the same response. I feel they are not investing my disputes or they would have received confirmation to delete these XXXX accounts off my report."
}
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": "Here is a copy of the complaint mailed to Capital One.
On XXXX/XXXX/16 I disputed my charge with XXXX XXXX XXXX. I received two secure messages regarding the dispute, almost immediately. Printed copies are enclosed.
On XXXX/XXXX/16, I received another secure message advising, verbatim - Unless the merchant resubmits the charge within 45 days of the date of this letter, we consider your case closed. If this occurs, we 'll notify you and may re-bill your account for the amount of the credit. Please keep all documentation related to this dispute for 90 days. Again, a printed copy is enclosed.
On XXXX/XXXX/16, we received another message, stating that the merchant resubmitted the charge, with proof. Here is where the problems arise.
XXXX/XXXX/16 through XXXX/XXXX/16 is 73 days. 52 Business days. This was WELL beyond the 45 days, so I initiated an online chat on XXXX/XXXX/16 to find out why. I chatted with XXXX. I asked outright - what date did the merchant rebill the charge. He said I was rebilled on XXXX/XXXX/16. I reiterated, asking if XXXX/XXXX/16 was the actual date. He said yes. I explained the issue. With that, the chat ended fairly quickly - printed copy enclosed.
On XXXX/XXXX/16 I received mailed letter of documentation from Capital one. All correspondence is dated XXXX postage stamp is dated XXXX/XXXX/16. Unfortunately, the rebuttal from XXXX XXXX XXXX is all dated XXXX/XXXX/16.
I was advised, as written above, if this occurs, we 'll notify you and may re-bill your account for the amount of the credit.
Is your turn around time normally 63 days, 45 working days from the merchant 's response? Is that how it works? If so, why do you claim 45 days from date of this letter?"
}
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": "Mortgage",
"Sub-product": "VA mortgage",
"Consumer complaint narrative": "Regions Bank, XXXX XXXX XXXX, XXXX XXXX XXXX Bank Officials XXXX XXXX and XXXX XXXX XXXX Customer Service I have received two notices concerning my mortgage payments, they are delinquint and am being charged fees. Since I started payments on the mortgage XX/XX/XXXX I have always made my payments sometime a little before to a little after the date, this last month I have been falsely accused of missing a payment. My loan details can be confirmed that I have made payments, they have been posted, and no issues until now.
I have sent copies to the bank in Mississippi requesting the fees be dropped and any negative comments on my credt if any to be removed. I have paid the late FEE of {$250.00} as of XXXX XX/XX/XXXX just to avoid additional issues. I make my paymenys electronically and always have since my account has been active. This unfair and unjust action against me has caused severe emotional XXXX, lack of sleep and constant worry. Please help. Respectfully XXXX XXXX XXXX"
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "I had a great credit score I tried to keep all my life and now an attorney from Texas shows up on my credit as an added new account from XX/XX/2016 which I had no Idea they can add an account to your credit with out your consent. so each month it shows you didn't pay on a bogus account they added, I had no contact or communication with these people at all ever. they keep hitting my credit every month, this was also on my credit I believe with another collection agency awhile back and also affected my credit score and then it gets sold to the next collection agency. why cant these people do their research first to make sure it was already submitted to my XXXX as a claim in 2016 and they processed it. before they go unto damaging peoples credit with no proof of claim was processed correctly or incorrectly? On Medical debt for all people is mainly error from medical billing people can not process or submit claims correctly so alot of these end up in collection at no fault of ours all has to do how medical billing submits claims. if they would submit the right way we wouldn't have this big problem. medical claims in collection should be reevaluated with medical insurance company first to resolve before, then can take next step if they need to. Sometimes the claim would need to be resubmitted correctly to process and will show paid or completed. Why is it that we have to do the work for everyone in billing dept with XXXX and try to retrieve OLD information on a claim that was submitted along time ago."
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Threatened or suggested your credit would be damaged"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have a complaint.
On the XX/XX/XXXX I asked for a due date change.
On the XX/XX/XXXX I asked for a due date change.
I made complaint to the Cfpb on XXXX REQUESTING A DUE DATE CHANGE I made a complaint with the XX/XX/XXXXon XX/XX/30 REQUESTING A DUE DATE CHANGE How many times do I need to ask for a XXXX due date change.
In XXXX I called 3xs. 2 of those times were to request a due date change.
it was a clear breakdown of communication in the past.
How ever to remedy this I requested a due date change for the 3rd. It was explained to me I need to wait for my account to be in good standing.
I did.
Now here I am ready to pay my card on the and I get hit with ANOTHER LATE.
I spoke to XXXX Id number XXXX She informed she couldn't change it. The late fee was because I failed to get confirmation on the late. And the the same information of how I change the due date and the time line in which to do it.
She informed me Target sent a statement so it's my fault if I didnt see it.
I told her and the staff before..literally every one in spoke too..I need a set date. I'm in and out of the hospital..I don't check I go in on the 3rd and pay. Per my record I've had one genuine late I've always paid on the 3rd. Because when I forgot my card the operator target said it was ok.
I understand there was a miss understanding. But at this point its very clear.
I trusted you guys to change it when I called 2 other times.
You failed.
I don't know your process it's not my job to know. It's my job to pay on time. And if I cant request a due date change Via phone in the time that was stipulated by Target.
Which I've done in the time line you given me by 2 of your operators. AND IN WRITING 2XS via cfpb and bbb. All times my account was in GOOD STANDING.
I dont feel like that's ethical for your staff to not change it.. then charge me a late fee. Then make it seem like you didn't know or I failed in some manner to ensure your staff follow thru."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Take note that I did not sign up for an account with these original creditors.. XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX, XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX XXXX XXXX XXXX ( Original Creditor : XXXX XXXX ) XXXX, XXXX ( Original Creditor : XXXX XXXX ) XXXX, I am extremely upset about this situation because I find it absolutely ridiculous that this information is being reported without contacting me first ... I requested bureaus to validate this information and provide me with copies of any documents associated with this account bearing my signature but they were unable to supply it. CFPB step up and remove XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX, XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX, XXXX XXXX ( Original Creditor : XXXX XXXX ) XXXX, XXXX ( Original Creditor : XXXX XXXX ) XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "This is my fourth endeavor to tell you that I am a victim of identity theft and I complain to question specific records in my document coming about because of the wrongdoing. The records I am questioning connect with no exchanges acquiring any possession of goods, services, or money that I have made or authorized. Assuming no one cares, either way, block the noteworthy of any information in my credit record that came about due to alleged fraud or extortion.
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 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 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 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": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "the year was XXXX XXXX was the month, times was hard working part time due to pandemic not making enough money to buy things cash or debit bank card, with a newborn on the way i knew i was going to need and want things for my son, so i walk into best buy trying to apply for a credit card to be able to afford few things right away everything came back negative was denied instantly not knowing why i question and luckily the guy that was helping throughout the store told me get a credit check with the 3 credit bureaus to see what you owe or what's going, so i did after weeks of disputing 10 accounts that were open back in XXXX 7 out of 10 creditors drop after investigation two back came saying no you owe us, both creditors told me if you really do not owe these accounts and your not at fault file a police report that will help all of us remove these accounts and we will continue the investigation, so i did attach police reports to my disputes like i've told everyone i ever spoke to about these accounts, the time all these accounts were open i didn't have a dollar to my name never work didn't file taxes had no source of income sadly i've been a victim of identity theft no way someone without a job could get approve for any amount of credit line thanks to these two creditors who don't care about laws suchs as FCRA they have no prove in any way paper on the phone that these accounts belong to me. if you guys can please take a closer look nobody cares now they just hang up once i ask for more of there time the credit beuras suchs as XXXX and XXXX all they do is dispute the creditors but don't solve anything just like the creditors they hang up or say a supervisors will call you in the next 24 hours no call, the creditos macy and XXXX dont give me more info other then its my accounts with zero proof no billing statement that matches my accounts since i didn't own a bank then its a nightmare with no help they are no holding of an telling XXXX to tell me that these accounts are going to drop in XXXX that's not the point 7 years of bad credit and nobody care"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was result of identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX is a credit processing center who is reporting erroneously. When an account is past due they take 60 days to send the information to the credit bureaus. I called and spoke to a XXXX and she does n't understand that it is incorrect to say that since they do n't report an account as past due for 60 days that this is considered the 1st delinquency date. She said I was 60 days past due as of XX/XX/XXXX. If I was 60 days past due as of XX/XX/XXXX then the first date of delinquency would be XX/XX/XXXX. This should be reported to Equifax who refuses to remove this outdated account from my credit report. XXXX XXXX sent me a ledger which I sent to Equifax however Equifax refuses to use that information and XXXX XXXX XXXX continues to say their information is correct when Equifax verifies information with them when in fact it is not correct. AN ACCOUNT MUST BE DELETED 7 YEARS FROM THE DATE OF THE 1ST DATE OF DELINQUENCY NOT WHEN IT WAS REPORTED BY A CREDITOR. AGAIN IF AN ACCOUNT IS 60 DAYS PAST DUE AS OF XX/XX/XXXX THEN THE FIRST TIME IT WAS PAST DUE WAS XX/XX/XXXX. I WANT THIS INFORMATION CORRECTED AND IF IT IS N'T I WANT XXXX XXXX FINED AND TURNED OVER TO THE THE FTC FOR WILLFUL NEGLIGENCE. A proper resolution would be the following : To fine XXXX XXXX XXXX for willful negligence to correct erroneous reporting according to the Federal Law. And for violating of Federal Law regarding their negligence reporting method. Turn them over to the FTC for willful negligence for not correcting erroneous information when it has been brought to their attention. They should be fined {$1000.00} or more for the infraction and I should be compensated for their willful negligence. XXXX XXXX XXXX has over 65,736 complaints by consumers because they their accounting is erroneous and they do n't keep accurate records."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "My car was totaled in an accident. My insurance company paid off all of the loan, or what I thought was the entirety of the loan, but Capital One is claiming there 's still a small balance. I was unaware of the discrepancy. The car was sold as salvage titled and registered to another person. Capital One reported my car as repossessed or in the repossession process which is now preventing me from getting another car loan. How was the car titled and registered to another person if Capital One is still the registered lien holder and there 's still a balance due? How can Capital One repossess a vehical over a balance less than the original cap payment amount? I need the repossession removed from my credit immediately. This is affecting my lively hood as I am unable to get a car to go to work"
}
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 fixed mortgage",
"Consumer complaint narrative": "US Bank National Association initiated foreclosure proceedings via Publication within 13 days after I received the final paperwork for the Hemp Modification from the Loan Servicer after passing the trial period for the payments cashed and kept the fourth mortgage payment while according to US Bank the account was in default.That payment was not applied to the mortgage instead my online account was deleted.US Bank offered me a XXXX Dollar bribe if I no longer would proceed.I have Senator XXXX involved."
}
Output:
{
"Issue": "Application, originator, mortgage broker",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Been a card member of CitiBusiness since XXXX. We have not had a lot of fraudulent transaction regarding this company. Charges for XXXX XXXX XXXX started in XX/XX/XXXX and occurred each until XX/XX/XXXX. We paid the statement amount in full every month and disputed said charges. Below is the charge amounts for each month : Date of Charges : XX/XX/XXXX {$110.00} XX/XX/XXXX {$38.00} XX/XX/XXXX {$150.00} XX/XX/XXXX {$150.00} XX/XX/XXXX {$150.00} XX/XX/XXXX {$150.00} XX/XX/XXXX {$160.00} XX/XX/XXXX {$150.00} I am attaching documents and letters sent to Citibusiness letting them know these were fraudulent charges and we disputed them.
Citibusiness started issuing credits against said charges in XX/XX/XXXX. Credits and dates issued are listed below : XX/XX/XXXX ( {$150.00} ) XX/XX/XXXX ( {$150.00} ) XX/XX/XXXX ( {$38.00} ) XX/XX/XXXX ( {$150.00} ) XX/XX/XXXX ( {$110.00} ) XX/XX/XXXX ( {$150.00} ) XX/XX/XXXX ( {$150.00} ) XX/XX/XXXX ( {$150.00} ) In XXXX when the last credits were issued, we consider the matter resolved and settled.
We received a letter dated XX/XX/XXXX from Citibusiness with a copy of the merchant 's response which had inaccurate information in their dispute reversal request. They had only partial matches on the authorization information and the email address associated with the XXXX account is not associated with me or our companies. We signed the document from Citibusiness stating that I nor anyone authorized by me made this charge ( s ), along with a letter asking them to add on the auto biller updater suppression to all cards related to the account. It was faxed back over to Citibusiness on XX/XX/XXXX.
On XX/XX/XXXX we received another letter with XXXX response to more of the charges and again supplied documents that had only partially matched authorization information and again wrong email information. They requested proof we cancelled in accordance with the merchant 's terms and conditions. Example of this proof would be a cancellation email from the merchant. On XX/XX/XXXX we signed Citibusiness form stating this wasn't a charge authorized by us and sent a letter explaining we can't provide proof of something we never signed up for. Letter and signed document were faxed back to Citibusiness on XX/XX/XXXX.
On XX/XX/XXXX Citibusiness has sent a letter advising us the charges are being reversed and the investigation is closed.
These are fraudulent charges and we have disputed every charge from this merchant since it began."
}
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": "On XX/XX/XXXX, I found out for the first time that my credit report from XXXX had report of serious delinquency regarding a Chase/XXXX XXXX credit card with numbers starting with : XXXX.
When I called Chase phone number, XXXX which appears on that entry, I was told to call Monday to Friday.
I called back on Monday XX/XX/XXXX at XXXX and I was transferred to Chase credit department . I informed the lady that answered that I just discovered that there is a wrong entry of " serious delinquency '' in my XXXX credit report, an account that I never opened or used and that it has been harming my credit since XX/XX/XXXX until now! I have been affected by this account causing me to always obtain higher interest rates and being denied of my applications. I never knew why until XX/XX/XXXX. I asked her to tell me information regarding that account : who opened it? And, she she said that I was not a primary in that account. I asked what is the mailing address of that account? She kept lying saying that she has no further information on that account but that she would remove it form my record since I was not a primary. Furthermore, she kept repeating that it may not even be related to me at all, that it could have been wrongly placed on my account. I have a right to know what or who has harmed me financially all these years. When I confronted her saying that I do not believe she does not have further information, and that I have been a Chase customer with good standing payments for years, she accepted that she has more information but can not share it with me. I am very upset of all the financial harm this wrong entry has caused me all these years, and all she could say is " if you had called before when you found out, we would have removed it before ''. I said I just found out, I pay all my bills, why would I think there is something like that in my credit report?
I have the right to find out more information on an account that has been damaging my credit report for almost 7 years. Please help me find out who was the owner of that account and what was the mailing address?
Thank you, 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": "Mortgage",
"Sub-product": "VA mortgage",
"Consumer complaint narrative": "I purchased a home, on XX/XX/XXXX. The loan was a 30-year VA loan with Guranteed Rate , Inc ( `` Mortgagee '' ).
The Mortgagee, during the course of XXXX, failed to update the original address given to my homeowner 's insurance company XXXX XXXX XXXX, XXXX ( " XXXX '' ). XXXX invoiced the Mortgagee at the address provided to them at closing and the Mortgagee did not pay the invoice. In XXXX of XXXX, we received a notice from XXXX that our insurance would be cancelled imminently if payment was not made. We made numerous phone calls to the Mortgagee and XXXX in order to facilitate this payment, which is not the responsibility of the Mortgagor but the Mortgagee via the escrow agreement.
The Mortgagee, by error, issued three checks to XXXX, from our escrow account. Each check was for {$5500.00}, totalling {$16000.00} the amount due under the insurance policy. The checks were dated XX/XX/XXXX, XX/XX/XXXX, and XX/XX/XXXX.
On XX/XX/XXXX, one check was refunded for {$5500.00}. One check was accepted as payment for our policy and one check was cashed by XXXX but not returned to our escrow account, representing an overpayment of {$5500.00}.
On XX/XX/XXXX, Guaranteed Rate, as servicer, sold our loan to XXXX Mortgage. The loan was transferred with the overpayment of {$5500.00} still outstanding, leaving our escrow account short for the new lender. As a result, XXXX increased our total loan payment ( Principal, Interest, Taxes, and insurance ) by hundreds of dollars to account for the shortfall. No further action was taken by Guaranteed Rate to recoup this mistaken payment.
I was not made aware of these transactions until I received the notice from XXXX that my escrow accounts were short after the mortgage was transferred.
I called the guaranteed rate on XX/XX/XXXX and was told to contact XXXX. XXXX informed me the servicer would need to request the refund of {$5500.00} in overpayment. On XX/XX/XXXX I called Guaranteed Rate again and after XXXX hour on the phone and speaking to XXXX separate people about the issue, I was told by a supervisor that she had put in a work order and that Guaranteed Rate would reach out to Orchid, recoup the funds, and cut me a paper check in the mail. She even asked me if I wanted a wire or a check. She said to call back on Monday the XXXX of XXXX, XXXX to check on the status of the refund.
When I called back to check on the status, I had to speak to three other people, some of whom reiterated that it was not Guaranteed Rate 's issue and that I should take it up with the insurance company, I spoke with another supervisor. She insisted she could not do anything since the loan had been sold/transferred. We then had a conference call with XXXX and the Guaranteed Rate supervisor where the guaranteed rate repeatedly asserted that it would not accept the refund and issue me a check, even though it was their mistake and that the new servicer would need to do that. I asked the supervisor for the contact information for her general counsel, which she said she did not have.
Guaranteed Rate made this overpayment in error, and despite being informed of the error, has not corrected it. It has also failed, per regulations, to transfer or refund my escrow accounts within 20 days of being paid off and if not for my intervention, would have failed to make my homeowner 's insurance payment on time in XXXX per the terms of the escrow agreement. They have cost me time and money due to their incompetent service.
On Monday, XX/XX/XXXX, I refinanced my mortgage with XXXX Loans, paying off XXXX Mortgage, in part because the monthly payments for my mortgage under XXXX have gone higher than I had budgeted for because of the Guaranteed rate 's mistake. Part of what I had to finance was the replenishment of the missing {$5500.00}. Guaranteed Rate refuses to accept responsibility for their mistake and I still do not have a clear understanding of how I will be able to recoup the {$5500.00}. I also had to pay over {$10000.00} in state transfer taxes and fees in order to refinance.
Guaranteed Rate needs to pay a price for their negligence and lack of responsibility as a servicer in this case. Their error has cost me money and time and they have violated regulations and the terms of the escrow agreement."
}
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 card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "The XXXX washing machine that we purchased on XXXX has a severe washer cycle dysfunction when the '' heavy cycle is needed. A repairman came out two times for that same problem. The agitator makes this large unbearable noise while it is suppose to be washing, then the rinse cycle goes on for another hour, using excessive amount of water, then it takes all day to drain, then it cycles again. My wife and i went out to the store in person XXXX XXXX NC, and we were told, that the repairmen must come out to see and that it is stated in our Warranty, it is nowhere in the warranty it states any 3 time repair rule. On XX/XX/XXXX, a rude customer service agent named " XXXX '', Gave us an ultimatum, " the repairman has the last decision. We asked to talk to the manager, and she said FINE! - she is not here today. but here is her name and number " XXXX XXXX XXXX it is a general store number. That is how deceitful they are."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I had a credit card bill due on XX/XX/XXXX in the amount of {$75.00}. I paid the complete amount due on XX/XX/XXXX. My account is now XXXX. When I received my new bill, it said that I now owed {$1.00}. When I called to complain, I was told that it was due to Residual Interest. I told them that it is impossible. I paid the total amount due and before the due date. The operator agreed to take the {$1.00} off my account. I informed the operator that because I knew that I paid the account in full, I wasn't even going to open the new bill because in my eyes I did not have a bill. I thanked the representative for taken the {$1.00} off but imagine all the people that just pay the {$1.00} without dispute it. If she states that its considered Residual Interest then why did she agree to take it off?
It's not the amount that I'm complaining about, it's the practice of the way they charged me. Is it legal?"
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Charged too much interest"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX XXXX XXXX-Account Number : XXXX Original creditor : XXXX XXXX XXXXXXXX XXXX XXXX has violated my rights.
15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than seven years.
15 U.S.C. 1681s-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or has reasonable cause to believe that the information is inaccurate.
Remove Account form 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": "Checking or savings account",
"Sub-product": "CD (Certificate of Deposit)",
"Consumer complaint narrative": "I had a checking account will wellsfargo and they closed my account because they said it was fraud activity on my account and took my tax check XXXX my work check XXXX and my stimulus check of XXXX in XX/XX/2021 I am a victim of identity theft .and wellsfargo. Sent me a letter stated that and said they some one was trying too open up a new account in my name"
}
Output:
{
"Issue": "Closing an account",
"Sub-issue": "Funds not received from closed account"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Information and inaccurate reports are being filed and hindering us from doing business. The tortious interference of these companies and their failure to abide by the law in the proper correction, removal, verification, reporting, and/or removal of the information or providing in writing the verification information as requested and lawfully required has caused gross personal/private economic damage and loss."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "This is my formal complaint against BRIDGECREST CREDIT C : In regards to Acct # XXXX. On XX/XX/2020 this company whom I did not receive financing through, repossessed my vehicle during a global pandemic. They failed to provide me with documentation regarding the repossession as stated in 9-611. NOTIFICATION BEFORE DISPOSITION OF COLLATERAL, 9-612. TIMELINESS OF NOTIFICATION BEFORE DISPOSITION OF COLLATERAL. 9-613. CONTENTS AND FORM OF NOTIFICATION BEFORE DISPOSITION OF COLLATERAL. I want Bridgecrest to provide a copy of the original sales contract with my signature that states the vehicle was initially financed through their company. BridgeCrest also did not have my written consent to report this account on my consumer reports which is a direct violation PURSUANT TO 15 USC 1692 ( 4 ) THE TERM CREDITOR MEANS ANY PERSON WHO OFFERS OR EXTENDS CREDIT CREATING A DEBT PURSUANT TO 15 USC 1692 ( D THE TERM CREDIT CARD MEANS ANY CARD COUPON BOOK OR OTHER CREDIT DEVICE EXISTING FOR THE PURPOSE OF OBTAINING MONEY PROPERTY, LABOR, OR SERVICES ON CREDIT NOTICE THAT I AM THE CONSUMER IN FACT, THE ORIGINAL CREDITOR PURSUANT TO 15 USC 1692 ( K ) ANY DEBT COLLECTOR WHO FAILS TO COMPLY WITH ANY PROVISION OF THIS SUBCHAPTER ( FDCPA ) WITH RESPECT TO ANY PERSON IS LIABLE TO SUCH PERSON IN AN AMOUNT EQUAL TO THE SUM OF WHICH I SUFFERED NUMEROUS XXXX XXXX, JOB LOSS, AND RUIN MY REPUTATION I DEMAND YOU PAY INVOCIE IN CHECK FOR VIOLATION UNDER THE FAIR DEBT COLLECTION PRACTICES AND PURSUANT TO 15 USC 1692 ( C ) ( C ) CEASE COMMUNICATION WITH ALL REGARDING THIS ACCOUNT AND BLOCK AND ZERO OUT THE BALANCE AMOUNT PLEASE ACT QUICKLY AND PROMPTLY! The credit card used in this matter was my social security card which I did not provide to BRIDGECREST!!! As a result of their failures I want this account DELETED from my consumer reports."
}
Output:
{
"Issue": "Struggling to pay your loan",
"Sub-issue": "Loan balance remaining after the vehicle is repossessed and sold"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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, my FICO score went down from XXXX to XXXX. I immediately log in to the 3 Credit Bureaus website to check what happened on my credit score ; I ended up submitting online disputes. I wasn't able to provide and attach any file supporting my claim because I don't have any documents at that time. There was no phone call, email, nor a final statement of account about this collection claim of {$86.00} that was reported by XXXX XXXX XXXX. The credit bureaus investigation results say that the claim is valid but didn't provide any reason. I decided to call XXXX XXXX the credit collection agency in Texas that reported this claim to the credit bureaus. I asked what was the {$86.00} collection claim on my credit report that they also failed to notify me ; there's was no statement of account for me to verify. In the end, they ask for a dispute letter for them to investigate on this case. On XX/XX/XXXX I sent a dispute letter to XXXX XXXX. On XX/XX/XXXX, I received a collection bill from XXXX XXXX XXXX. The very first bill I got regarding this claim, but then again, I don't know the details of how I ended up having a credit of {$86.00}. On XX/XX/XXXX, I sent the dispute letter they asked to dispute the validity of the debt that they are claiming. In reply to my dispute letter, onXX/XX/XXXXXXXX sent me a copy of all the documents they have on my account. For the first time, I finally see the final account statement billed by XXXX XXXX XXXX. I can finally explain myself and again dispute those charges, those charges on my account are not valid. On XX/XX/XXXX, I sent another dispute letter to XXXX XXXX XXXX to explain what really happened and that their claim is not valid. I asked them to remove the collection claim that they reported to the credit bureaus. With all these steps of explaining and sending the dispute letters nothing solved my problem. I still have a collection report on my credit history and my credit score is still low. As my last resolve, I am filling this complaint and I will attach copies of those disputes letters and other supporting documents."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Problem with personal statement of 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": "In accordance with The Fair Credit Reporting Act XXXXXXXX XXXX Account Number XXXX violated my rights.
15 U.S.C 1681 Section 602 A states I have a right to privacy 15 U.S.C 1681 Section 604 A Section 2 : it states that the consumer reporting agency can not furnish an account without my written instruction"
}
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": "I do not know",
"Consumer complaint narrative": "They call numerous times through out the day. I have asked them to stop calling me and only communicate with me through mail. They refuse to take me off the list and ask me personal questions like my social security number and address and refuse to tell me anything else. They call from different numbers with the same area code. This has been going on for a little over 5 months."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Frequent or repeated calls"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I saw an inquiry on my credit report from Equifax consumer affairs that was done without my consent. when you contact them they give you the run around."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "The account is a XXXX account I had at my previous apartment unit about 3 years ago. I moved to a new unit in the same complex without being aware that I owe a balance of {$100.00} on my XXXX Cable.
I was never contacted about paying this debt. Partly because correspondence is sent to an old address or my name is spelled wrong ( As can be seen in the attached document ) I suddenly see this item on my credit report as I now check it often for inaccuracies. I immediately called the Collection Agency and paid the amount in full XXXX along with a {$5.00} processing fee paid to PAY IN SECONDS.
I made it known to the agent that if they had contacted me prior I would have made the payment to which she had no response, She however advised me that it can be deleted if i went through the right channel, which brings me to this site.
According to the credit reporting agency this damaging record will remain on my account till XX/XX/2021. Since I paid the full amount the same day it was reported, I request a DELETION of this derogatory item from my XXXX credit file."
}
Output:
{
"Issue": "Disclosure verification of debt",
"Sub-issue": "Right to dispute notice not received"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "XXXX, XXXX XXXX XXXX SEC # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX TER, XXXX XXXX, FL XXXX XXXX XXXX XXXX Opened XX/XX/XXXX {$1200.00} XXXX XXXX XXXX Opened XX/XX/XXXX {$620.00} + Your company have violated FCRA 615 ( f ) ( 15 U.S.C. 1681m ( f ) ) ( f ) Prohibition on sale or transfer of debt caused by identity theft You have not responded to my request to provide me with an investigation report.
I need this resolve, under the Fair Credit Reporting Act, these disputed items may not appear on my credit report if they can not be supported by any evidence ( 1 ) In general No person ( company ) shall sell, transfer for consideration, or place for collection a debt that such person has been notified under section 1681c-2 of this title has resulted from identity theft.
( 2 ) Applicability The prohibitions of this subsection shall apply to all persons collecting a debt described in paragraph ( 1 ) after the date of a notification under paragraph ( 1 ).
( 3 ) Rule of construction Nothing in this subsection shall be construed to prohibit -- ( A ) The repurchase of a debt in any case in which the assignee of the debt requires such repurchase because the debt has resulted from identity theft ; ( B ) the securitization of a debt or the pledging of a portfolio of debt as collateral in connection with a borrowing ; or ( C ) the transfer of debt as a result of a merger, acquisition, purchase and assumption transaction, or transfer of substantially all of the assets of an entity.
A person that furnishes information to any consumer reporting agency shall have in place reasonable procedures to respond to any notification that it receives from a consumer reporting agency under section 1681c-2 of this title relating to information resulting from identity theft, to prevent that person from refurnishing such blocked information.
The reference items are listed below please take this matter very serious MIDEWEST RECOVERY I have report your company to consumerfinance.gov As a form of record and I am prepare to sue your company XXXX XXXX XXXX XXXX XXXX XXXX, XXXX XXXX Consumer Protection Act Litigation, Case No. XXXX, in the XXXX XXXX XXXX for the XXXX XXXX XXXX California XXXX collection proceedings against me Signatures on applications and accounts Investigators report XXXX, XXXX XXXX XXXX SEC # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX TER, XXXX XXXX, FL XXXX"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was result of identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Complaint : I did not receive my XXXX XXXX Miles bonus promotion for my new Citi Platinum Select Master Card : XXXX XXXX 2015 I received an e-mail invitation from XXXX XXXX to apply for the new promotion which after making purchases of {$3000.00} within the first 3 months I would receive XXXX Bonus XXXX miles. On XXXX XXXX, 2015, I applied online and was approved for the new card. In a few days I received the new card. Within the 3 required months, I 've made more than {$3000.00} in purchases and made timely payments and to my surprise I did not receive the XXXX XXXX miles. I contacted Citi Card and after an investigation I received a letter explaining that because, " you either opened or closed another XXXX XXXX account within the past 18 months. ", they were unable to apply the XXXX miles.
If I had a previous Citi Card ( turns out I did - I closed a credit card 15 months ago ), I should have been informed when I applied that I would not be eligible for the XXXX promotion, and not to issue a new card apparently hoping it will go unnoticed by me. I consider this intentional and underhanded business practice by Citi Card. Last week, I spoke to someone on the " CEO Citi Card Team '' and she said it should have been stated in the Welcome letter that I was not eligible. I explained that It was not and they have not issued the XXXX miles. I noticed in today 's news that CFPB has issued a penalty against Citi Card Bank and I agree that are guilty of " deceptive business marketing ''.
P.S. I was not ware of any " XXXX Month Rule ''."
}
Output:
{
"Issue": "Advertising and marketing",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "To Whom It May Concern, I am writing to challenge the reporting and compliance on an account with XXXX XXXX XXXX that is being reported on my credit report with incorrect/ missing info. On XX/XX/XXXX, after reviewing my consumer account, I noticed numerous errors that show Experian is not reporting with 100 % accurate information as required by the Fair Credit Reporting Act.
Specifically, I am referencing that Experian is reporting an inaccurate account number with XXXX numbers and ends with XXXX XXXX. Along with still reporting late payments after the account was closed in XX/XX/XXXX. Experian has also reported inaccurate dates of the last activity.
Experian 's improper procedures are highly damaging to my credit score. Please immediately delete this item."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information is missing that should be on the report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have called several days to request that XXXX AMEX to remove the credit card account from my credit report because I am just the authorize user. I was told that they can not remove the account for several reason. The first was because my card was still active, so i asked to closed my card account. After I did this they still refused to remove the from my credit report. I called the XXXX AMEX credit bureau department and they stated that my marital status was the reason why the account will not be deleted. Every time I request to speak to a manager the call disconnects. I am again requesting for the account to be removed from all three credit bureaus because I am not the responsible party on the account and the account is closed. Please removes immediately."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX Bank 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 15 U.S.C 1681 section 602 A. States/ have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than XXXX years.
15 U.S.C. 16818-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person know 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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "CBNA is reporting a debt on my credit file that is not mine! I recently checked my credit to start working on buying a home and noticed that CBNA was reporting a charge off. I have never opened an account with CBNA!! This account is fraudulent and needs to be removed from my credit. This is severely affecting me and it's not my debt! I suggest they find who this debt belongs and remove this account from my credit entirely."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "Please see the attached letter I received in the mail Friday XX/XX/XXXX. This letter was sent by XXXX XXXX XXXX on behalf of national credit adjusters. In the letter it says that I owe original creditor balance credit. But on a previous claim I filed with the cfpb national credit adjusters responded that I owe money allegedly to XXXX. National credit adjusters did not confirm that I owe the money to the original creditor allegedly called XXXX XXXX. Now I have this letter from XXXX XXXX XXXX telling me that I owe the money in the letter itself please see that it stated national credit adjusters will not report this to any credit reporting agency. But I checked my credit report in this debt alleged debt that I owe is on my credit report why am I being collected on a debt that I do not owe secondly I do not have any or have received any notice of proof that I owe this money to the original creditor called balance credit. More importantly this communication dated XX/XX/2021 from XXXX XXXX XXXX clearly states the because of the age of your debt national credit adjusters will not sue you for it and national credit adjusters will not report it to any credit reporting agency. Why is this debt this alleged debt on my credit report then? Please remove this debt from a credit report."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "The Escrow Department for LOANCARE , LLC did an escrow analysis. They stated my XXXX County Maryland taxes were paid 4 times a year. They read the bill wrong. They kept telling me that my tax bill doubled but the XXXX County Treasury office confirmed that the taxes are only paid in two installments. I called LOANCARE today and the representative stated this was an error. With this erroneous analysis, they stated my mortgage payment would be an additional {$500.00} dollars. This was outrageous. The County confirmed it never doubled. I provided proof. ( see attachment ) On today XX/XX/XXXX, the representative saw it was entered into LOANCAREs system four times a year by error. The reps supervisor confirmed that it was an error. She put a request to have another analysis done and it should be fixed by XX/XX/XXXX. I should expect a new analysis delivered to me. If this does not happen, I will be submitting everything to my attorneys office for litigation.
My account was also reported over 120 days late as of XX/XX/2021 on my credit report. This is inaccurate. My loan was transferred to XXXX/LOANCARE servicing in XXXX while it was still in a CARES ACT forbearance. ( see attachment ) Federal Law states there is a two month period of no negative credit reporting or fees accessed while a mortgage loan has been transferred to a new mortgage servicer. While still in the transfer period, my loan remained in forbearance while LOANCARE processed a loan modification. The modification documents show that my first payment is due XX/XX/XXXX. ( see attachment ) My credit report should have never been reported late. Again, it shows that I am over 120 days late. This is current data reported as of XX/XX/XXXX. LOANCARE has damaged my credit with this. They have broken the law."
}
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": "Bank account or service",
"Sub-product": "Checking account",
"Consumer complaint narrative": "My accounts were closed and {$260000.00} was taken from me without due process or investigation and several banks are involved. I was promised its return plus interest accrued.
I filed separate complaints on each bank involved without any results, no accountability and basically no communication mediator. After XXXX years and XXXX of legal fees, I was XXXX and taken advantage of in court. I received a check that was an insult and lost by default when I was incapacitated in the hospital. The money was intercepted fraudulently by XXXX XXXX and re-issued XXXX times even though XXXX XXXX was not involved after they had foreclosed I year after my home burnt down and in modification as well as rebuilding. They were paid {$240000.00} and no money was owed."
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "I made arrangement to pay Statebridge Mortgage HOA fees they paid on my behalf in XX/XX/2018.
Had a payment plan with them to pay XXXX XXXX monthly.
The first month it was due was XX/XX/2018. I sent a check, but unfortunately I wrote {$24.00} in the check box. The correct amount of {$2400.00} was written out.
Statebridge returned check. I sent a money order. For Two months I sent them my payments.
In XX/XX/XXXX they sent a mortgage statement saying I owed {$10000.00}. I called. They said the payment plan was canceled because the 1st payment got to them late. They never called to say the plan was cancelled. The account was under Loss Mitigation.
They held on to the money order, the 2 checks, then referred it to an attorney almost 4 months after.
The check they returned could have been cashed because once the correct amount is written its acceptable.
I now racked up attorney fees because of their underhanded tactics.
As of 2 weeks ago I paid them over {$14000.00}.
I would like to file this complaint because Im sick of the illegal tactics being used by these companies & they being allowed to get away with it.
I would therefore like to regain the attorneys fees I paid.
To understand how they operate, every week at a total of about 3 calls a week, some rep called asking about how I intend to pay. Its like the left hand didnt know what the right was doing. They never had it in their records that it was referred to an attorney."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Other mortgage",
"Consumer complaint narrative": "I receive a Form XXXX last year from Ocwen because filed for modification of my mortgage loan. Ocwen modified my loan and canceled a portion of the debt I owed in the amount of {$19000.00}. I understand that according to the IRS, this means that Ocwen is writing that amount off as a cancellation of debt and I no longer have to pay it back the {$19000.00}. Therefore, I need to include the amount of debt from XXXX report on my tax return as income which I did. As a result, it caused me to have to pay in taxes to the IRS last year, which I never had to do before when I filled taxes.
This year, I filed for another modification with Ocwen. while speaking to the agent he informed me that I was approved for the modification and went over the terms with me. Ocwen stated that with this new modification called trial plan period that my principal balance is {$79000.00}. I ask the agent why is my loan so high, that its almost back the amount of my loan before the first modification ( {$87000.00} ). The agent said that the add {$12000.00} of the {$19000.00} back to the loan. I told him that they could not do that because that amount was charged off and reported as income on my taxes.
My second complaint is also that there 's a balloon payment that will be of {$61000.00} at the time this note matures on XXXX XXXX. In my first modification the balloon payment was due XXXX XXXX, XXXX for {$16000.00}. In my opinion this is not a true loan modification. I feel that I am be taken advantage of, please help me. I have enclosed the first modification paperwork, the trial period paperwork that a sent me they will mot give me the permanent modification until the XXXX payment of {$400.00} have been paid, and the XXXX form I used to file my taxes."
}
Output:
{
"Issue": "Loan modification,collection,foreclosure",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "Navient is over-charging interest and late fees on my loans."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Don't agree with the fees charged"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "In XX/XX/XXXX, I received notice of a use tax from XXXX County Illinois of {$200.00}. On XX/XX/XXXX, I sent a letter of dispute to XXXX County. I received no reply to this letter. In XX/XX/XXXX, I received a notice from Penn Credit representing XXXX County indicating that I now owed {$230.00} for the use tax. I paid them {$230.00} on XX/XX/XXXX. In XX/XX/XXXX, I received a letter from XXXX XXXX XXXX XXXX indicating that I owed {$52.00} ( {$2.00} tax plus {$50.00} interest and penalties ). An administrative hearing was scheduled for XX/XX/XXXX. I contacted the XXXX XXXX XXXX and disputed the charges with XXXX. She told me to send an email to XXXX XXXX of Penn Credit with all relevant information. I sent 3 emails on XXXX, XXXX and XX/XX/XXXX. I received no replies. I was unable to attend the administrative hearing because my wife was discharged from the hospital as a XXXX XXXX requiring 24 hour care. I have since received a letter effectively stating that I now owe {$100.00}. I am filing a complaint regarding Penn Credit for failure to provide timely and adequate information for me to resolve this dispute. They have been completely unresponsive to my inquiries."
}
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "Our mortgage payments are, and have been, up to date. We have all the documentation to prove this, however, Selene Finance is continually sending us Certified and regular USPS mail stating that we owe for XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX and XX/XX/XXXX. They did not respond to emails. XX/XX/XXXX we sent a certified letter with our complaint and proof to Selene Finance. Over a week later we finally received a response via email stating that we owe for XX/XX/XXXX, XX/XX/XXXX and XX/XX/XXXX in spite of having just provided proof in a certified letter that XX/XX/XXXX - XX/XX/XXXX have been paid. We responded to the email and again have not received a response. Today we are sending yet another certified letter to Selene Finance to provide them with another copy of the same proof that those payments have been made and to correct the error in their system."
}
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": "Student loan",
"Sub-product": "Non-federal student loan",
"Consumer complaint narrative": "Have a loan thru XXXX XXXX and have been paying on it for over 11 years. Several times over the years I was unable to make the payments and they only offered me a 3 month grace. Most recently I was unable to make a payment because I was unable to work and was told I had used all my options and not to make the payment and to call when I could make the payment. Well that put me in less than good standing and they are now contacting my co signers which I was never told that I could ask to have them removed from the account after making a certain number of payments. Navient is the one that has the account right now and was the one who told me not to make the payments. I now owe more than the original loan was nothing has ever been taken off th principal because i was never able to afford the principal payment. I just wish Ii had never got involved with This company! This was a loan out for my childs education."
}
Output:
{
"Issue": "Can't repay my loan",
"Sub-issue": "Can't temporarily postpone 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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "On XX/XX/XXXX I submitted a complaint with CBE Group on debt collection and written notification about debt since I did not receive by mail. I got the response on XX/XX/XXXX from the company from XXXX XXXX a compliance officer. She states the client will forward debt validation. From what I hope by mail, not email. because she states CBE Group sent notice of collection account on XX/XX/XXXX meaning final notice before being sent to credit bureaus. CBE Group sent to an email address which is that she supposedly confirms by " matching ''. I do not see any attachments from my complaint # XXXX in which I provided documents ... why isn't this shown for me to inspect. XX/XX/XXXX and XX/XX/XXXX the accounts was opened in the credit bureaus. Then they send to my email address on XX/XX/XXXX for notice. Which is wrong and falsification. I do ask since you sent this " notice '' how come you couldn't send a BILL THROUGH EMAIL. Explain that. So now the client is forwarding my debt validation soon from which source email or mail it is not clear. So now I am filing a complaint on false statements. Again I do not believe anything was sent to my email, if it was this company they are not in my contacts, first I wouldn't know who they were to begin with, with no bill or account number, etc. It was probably sent to junk, If it got into my inbox I still would not know who these people were unless I got a bill. So since this is in credit bureaus I apparently have no resolve, in which I offered, but you want me to discuss the matter, to what record me. You all ready have made false statements. Please note XX/XX/XXXX. XXXX email was sent : by client on XXXX XXXX. Not in XX/XX/XXXX. The documents I enclosed are when this account went into credit bureaus XX/XX/XXXX and XX/XX/XXXX. XX/XX/XXXX a notice of collections was supposedly sent to email, a notice means before being sent to credit bureaus. How can this be?"
}
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": "I contacted Experian on XX/XX/18 to inform them that I consolidated my student loans with a new company. I disputed the innaccuracies reported out of spite by XXXX. Experian never followed up with a decision. I never received a response on the matter and XXXX has yet to stop reporting outdated information. At this point XXXX isn't sending updates and the outdated unconsolidated loans are on my credit report. So I have my student loans listed twice on my credit report because of Experian and XXXX."
}
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": "I have been a victim of identity theft and I have an unauthorized account from XXXX XXXX that doesn't belong to me as well as 20 plus inquiries that don't belong to me and were unauthorized"
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Around five months ago, I opened a US Bank checking account at the XXXX branch inside XXXX. The employee who opened the account for me was notified this account was being opened due to me going through a divorce. It was an account for me to start over with after taking my half of the funds we had accrued On XXXX I deposited a check for {$55000.00} at the XXXX branch inside XXXX. I was told a 7 day hold would be put on the check, but I was given a printout showing specific dates where I would have access to specific amounts of moneyone of them being XXXX XXXX. I was told I had access to {$220.00}, of which I took {$200.00}. The next day I had a negative balance, with an over draft fee. I had to go to the bank to get that taken care of. Then the day XXXX was promised access to more of my own money cameXXXX, XXXX XXXX. I had a negative balance and XXXX available funds. Then I realized it was a bank holiday, despite the fact that it was printed and highlighted as a day I was supposed to have additional access by the bank itself.
The next day I went back and forth on the phone with various employees after being very upset discovering I still had a negative balance. I was also told the back office had put an extended hold on my checkto the tune of another week. I had not been notified. I eventually spoke to the branch manager of the XXXX location, who I found was the employee who helped me, as she mentioned that. She stated she remembered my situation, and stated holds are often put on new accounts with no activity. Then she stated she and employees at that branch had tried to reach out to me multiple times but were unsuccessful, and had shredded my card. I dont recall getting but one phone call, but I had anticipated my card in the mail. I asked her what that had to do with my current situation, and felt she was trying to imply this was my fault, which she denied.
I understood at this point that they would be holding my large deposit as long as possible now. Since Im in the process of switching to direct deposit, I deposited my paycheck in person. I was told a one day hold would be instituted, but that in the meantime Id have access to {$220.00}. Once again, I took out {$200.00}, checked the app afterwards and it showed I have a balance of {$26.00}. The next morning, I have a negative balance, and another overdraft fee. I call the bank, and they waive the overdraft fee, and state they havent received the funds yet, but if there is no stop or hold on the check, I should see my funds by today. I was notified by another banker that the same type of hold was put on this last check, but it didnt say how long it would be. Today I found that, once again, without notice, my second check is being held until the XXXX. Using this bank has brought me copious amounts of anxiety, and I hugely regret trusting them with my finances. Im so nervous due to being given false information that Im trying to put together back-up plans in case I dont receive my funds, and have started searching for legal counsel. This is truly the worst experience Ive ever had with a bank. Using a new account should never affect your mental health or affect your quality of life."
}
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": "I have sent several disputes to each on of the credit bureaus : XXXX, Experian & XXXX and they have yet to respond to me or provide results. I do understand there is a pandemic going on, but they are still required to provide results under the law. They are reporting errors on my credit file and they need to respond to disputes. I have sent another round of disputes to them in hopes to get in touch and receive any kind of correspondence. I will await your response."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "There seems to be something strange going on with this bank handling an issue with a check that was improperly filled out. The check was numerically written for {$260.00}. But, on the narrative section I mistakenly wrote XXXX XXXX XXXX XXXX. I noticed the error before my manager, who was the recipient. And, since she had not noticed the error yet, she was unaware of the problem. I was informed by her that they " posted '' the amount in the accounting ledger at the numerical amount. She asked me to ask the bank about the funds. At no time, that I'm aware of, did anyone ever tell the bank that they received the entire funds. Yet, I'm being told that someone put a notation on my account that they confirmed the recipient received the full {$260.00}. What's more is I'm now being asked to prove that they didn't receive the funds. As far as I can discern, that particular narrative began with one of their own customer service representatives. I've tried to explain this twice already. It's very insulting. The implication being made here is that I'm lying about my manager not receiving the funds. They can check for themselves to see if they were credited the amount."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Funds not handled or disbursed as instructed"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Money transfer, virtual currency, or money service",
"Sub-product": "Mobile or digital wallet",
"Consumer complaint narrative": "on Sunday XX/XX/2021 i used my mobile phone to check my Coin base crypto currency account and it showed me a balance of XXXX Cent previously on the same dat XX/XX/2021 my account balance was XXXX i did not sell any of my currencies because i was holding them for long term investments. After contacting coin base i was informed that i needed to perform a 2 step verification process in which i log in and put a 6 digit code i receive from XXXX in order to upload my photo identification front and back then i would have to use my web camera on my computer to take a selfie well it lets me upload the front and back of my drivers license but when try to take the selfie with my web camera the page disappear and another page appear stating that my identity can not be verified although i have uploaded my drivers license front and back i only have one last step to complete but can not due to the web camera issue. previously when i signed up for the account and was approved i used my mobile phone but coin base does not allow you to go thru that process. it is very hurtful to know you invested to make money and not know what happen to that investment. i think Coin Base is using the money as personal investments because when i purchased $ XXXX it was valued at {$3100.00} and in 1 week its value is over {$4000.00}"
}
Output:
{
"Issue": "Managing, opening, or closing your mobile wallet 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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "Sent payoff in to payoff my personal loan. The payoff was received on XX/XX/21 but they waited to post the payoff after they received my ACH debit for the XXXX payment then paid the loan off instead of sending my monthly payment back. Lending point kept most of my money between the monthly payment and the payoff. I have all the documents to prove something isn't right with this loan transaction. Loan number XXXX. I would like my rightful amount returned to me."
}
Output:
{
"Issue": "Problem with the payoff process at the end of the loan",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I examined my credit report during XX/XX/ 2018 and noticed that there were many accounts that are fraudulent on my report. I am in serious need of those items being removed from my credit report."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I 'm including all of my CFPB complaints, my AG complaint, my proof that substantiates the fact that I never received a breakdown of the amount they are claiming that I owe, my identification and all other communication.
Reason for complaint : Experian and XXXX XXXX XXXX have failed to provide me anything that substantiates the amount that is listed on my credit report for a collection account. I am tired of going back and forth and it is my RIGHT to have the proof that backs up the amount I allegedly owe. Section 609 protects me against false reporting. Section 1681 guarantees me an investigation into this and for the bureaus to investigate independently. I 've provided the proof directly from the collection agency that I did not receive a breakdown and only received an invoice with a total amount.
I refuse to pay an amount listed on an invoice without the breakdown showing where that amount came from, the receipts that back it up and it must be within federal limits. XXXX XXXX of XXXX XXXX provided court testimony and was paid the federal fee of {$40.00}. I stated that I would pay for the expenses but wanted a breakdown, and this was the understanding. As you can see from the attachment, the only thing that I ever received was an invoice with the heading " itemized statement '' with 1 line that says " court testimony and prep {$540.00} ''. Where did this amount come from? Where are the receipts for this amount? Where 's the breakdown that lists the mileage, gas, and anything else allowed by law? When I call, I get hung up on. When I dispute this with Experian, they state that this has been verified.
This is willful negligence on part of both parties and this could have been easily solved by providing me what I had requested, what I had been promised and what is my right to have. Experian is now in violation of sections 623, 609, 1681 and 603. XXXX XXXX XXXX is purposely not providing me this information because THEY DO NOT HAVE IT. If I take them to court, they will need to provide this information during the discovery process. This means that the office of XXXX XXXX does not have, nor did they ever have, the required information to verify this alleged debt.
This MUST be removed from my credit report because over and over again, I have proven that this is a violation of my rights and that there 's no proof substantiating the claim that I owe this amount. Once again, I am not refusing to pay the actual amount that I owe. I am, however, refusing to pay a debt that was not legally verified, nor do I have to."
}
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 debt",
"Consumer complaint narrative": "I had a credit card with XXXX XXXX in XXXX or XXXX. I was unable to keep paying every month. XXXX XXXX put the owed payment to a debit collector named Midland Credit Management LLC. I noticed that it was reported on my credit report, which I understood. Yesterday, XX/XX/XXXX I was served with court papers stating that they were suing me for the amount I owed which is {$790.00}. This morning XX/XX/XXXX I reached out to Midland Credit Management 's attorney who is handling the case and left a voicemail stating that The amount appeared on my credit already and I'm confused why am I being sued. I also said in the voicemail that I'm currently unemployed so I can set up a payment plan or something but I don't have the full amount."
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Threatened to sue you for very old debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On Today, XX/XX/2019 I got an update that XXXX XXXX Debt Collection Agency had placed a collection on my credit reports. This is first and foremost a violation by this company because someone called not even a month ago and I asked would this be placed on my credit and they stated no. So, I would like that last call pulled.
Also, a few months ago. This company took money off my credit card after an automatic payment was cancelled. They even initiated the payment days earlier than the automatic payment. This is illegal and downright wrong! I would also like these calls pulled by this company as well.
This company is in many violations and for the credit bureaus not to fact check but only go by what these companies do, I am disheartened to find this information out. I am disappointed in the credit bureaus and XXXX."
}
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": "Inquiry from XXXX XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX XXXXXXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX XXXXXXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXXXXXX XXXX XXXXXXXX. on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXXXXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report.
Inquiry from XXXX XXXX on XX/XX/XXXX.
I dont recognize this lender and I dont remember authorizing them to perform a hard inquiry on my credit report."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I have had this credit card since 2018. I very responsible client and I pay all my credits on time. Today by a credit report I realized that I owe a money to one of my credit card ( at this moment I was surprise about this debt ). I started to check all my credit cards finding out which card was due. Then, I realized that the card that I owe was my XXXX card. This card I have not use it for a long time, but they charged the annual fee. After that, I went to the credit card website to pay, however I couldn't pay because I was already closed. I chat with somebody from the bank and this person gave me a phone number to solve the issue. I called and the agent told me that he cant solve the problem. I explained both of them that I didn't receive any information by post-mail, text, or e-mail. I really feel this situation very unfair because I want to solve the situation and pay the annual fee and continue being a client. Unfortunately, the miscommunication caused a problem with my credit score. I think they were not effective in communicate the issue with the annual fee."
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Problem with fees"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I am writing to dispute the following charge {$1400.00}. this item in dispute from XXXX also known as XXXX fares. I have send you documents, emails and phone records. I did release my account information over the phone at XXXX back in XXXX 2015, but I also did call this company within 12 hrs. to cancel the booking which was at XXXX the very next day, the travel agent I spoke with probably did not understand XXXX, as I told him to cancel the booking, he states he can not refund me the entire amount charged to my credit card, I told him I will be disputing this charge with my credit card company and to still go ahead and cancel this flight booking. I also pointed out to the agent that I did not respond back to an email stating " I agree and to go ahead and charge my credit card ''. I also did call American Express right after that phone call to let them know, the agent told me I have to wait for the charge to post to the account and then dispute it, I did my part by informing you not to allow the transaction to go through but instead you still allowed it. Please can you ask the travel agency to provide me with proof of email stating I agree and to charge my credit card besides just a phone call as an agreement. You can also subpoena the travel agency to send you a copy of the phone recording so you can listen to it.
I have been disputing this for months now. I have sent you all the corresponding emails from the merchant and my phone record 's to show the time I had called to cancel that booking. I have co-operated in every way possible and have submitted the supporting documents that you requested. I also want to bring to your attention that I have never received any documents from you by mail, explaining the dispute processes and also whether my dispute was closed in my favor or not, I get an email to view a message online and every time I try to access it I have been unsuccessful in doing so, every time I have to call in and get my dispute re-opened. Please can you send me a letter or documents in mail stating why the dispute did not go in my favor so I can forward it to the XXXX.
I am requesting that this item be removed from my account and to not pay XXXX XXXX as this agency took my credit information and provided it to XXXX XXXX XXXX to make the booking. I have also contacted the airlines and they stated I did not book the ticket directly through the airlines and I have to dispute it with the travel agency.
I am tired of going back and forth with this and been given the round around. If this matter is not resolved within 3-5 days I will be contacting the XXXX office in Ohio as well as the XXXX and the Federal Trade Commission.
I will be forced to call in and close my account with you, I will pay for the pending charges on my account but I refuse to pay for the charges from XXXX. Under the Fair Credit Billing Act, I am requesting this billing be corrected and any finance charges or fees associated with the disputed charge be credited to my account. Please re-investigate this and correct the disputed item and take care of this as soon as possible."
}
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": "XXXX has reported my score incorrectly, because they claim that I owe existing amounts to a debt collection agency for medical payments. Even though I have already send them a proof of medical insurance at the time. They also claim that Ive also yet paid my XXXX XXXX payment, but I have proof of payment and the payment being made on time. So this continues to effect my credit score without any help to resolve it."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Private student loan",
"Consumer complaint narrative": "In XXXX of XXXX, as an incoming college student, first of my siblings to pursue post-secondary education, growing up impoverished, under adverse financial circumstances, I sought a private XXXX XXXX college to seek a professional degree. Needless to say, I didn't have enough funds to pay for any percentage of my tuition. Through merit-based scholarships and federal student financial aid, I still lacked funds to enroll, so I turned to Sallie Mae for a private loan to pay for the expenses I encountered. From the firm, I was awarded with approximately {$6500.00} from Sallie Mae in the Fall of XXXX. After successfully completing my college, becoming a full-time employee, specializing in the work related to my degree, I sought to return for an advanced, XXXX XXXX XXXX, between Spring XXXX XXXX. Although I applied for the deferment of my federal and private loans, I was not given options or sufficient information regarding the repayment of my loan, held by Navient, formerly Sallie Mae. I was betrayed by a firm that pressured me to increase the amount of my repayments.I was deceived by not receiving sufficient contact from Navient, regarding my outstanding balance, in order to inform my repayment options. Since the inception of the loan, I have seen my principal balance double in amount and through consistent, consecutive payments, have just recently reduced this principal to its original amount. Most deceptive is the obscure information provided to me, according to the monthly statements I have received from the firm, indicating that never was there a " minimum payment '' due to Navient, giving me a false sense of security, as I hoped to maintain lower payments. Finally, I am frustrated that I am not afforded with enough transparency, on behalf of Navient, to thoroughly investigate the allocation and distribution of the funds I have provided to repay the loan, in a timely manner."
}
Output:
{
"Issue": "Struggling to repay your loan",
"Sub-issue": "Can't get other flexible options for repaying your loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Government benefit card",
"Consumer complaint narrative": "I had opened an XXXX XXXX and experienced fraud. I went though the process of doing claims and and the identity verifications for the XXXX. I was given direction to submit claims to Bank of America. A year later I still not have received my funds I am owed from XX/XX/XXXX - XX/XX/XXXX XXXX"
}
Output:
{
"Issue": "Problem with a purchase or transfer",
"Sub-issue": "Charged for a purchase or transfer you did not make with the card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I had a loans transfer to another company ED Financial Services. They kept old info on my report. I called them in XX/XX/2022 to rectify. Nothing was done. I did not have late payments to this loan. Only because of covid 19 and transfer of this account did these payments stop. account # XXXX amounts {$1800.00} and {$1000.00} In accordance with the fair credit Reporting act convergent account # XXXX, has violated my rights. 15 USC 1681 Section 2 : States I have the right to privacy.
15 USC 1681 section 604 A section 2 : it also states a consumer reporting agency can not furnish an account without my written instructions 15USC 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."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I paid off my home mortgage on XX/XX/XXXX. On that same day my credit score dropped by 56 points thru Experian. I have tried to call Experian to discuss this issue with them. I am looking at purchasing another home, but my credit rating has dropped which is pushing me into a higher APR."
}
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": "Re : Acct # XXXX XXXX XXXX XXXXXXXX Name on Card : XXXX XXXX XXXX Bank of America/Alaska Credit Card I am writing concerning my experience with Bank of America that is both a grateful thank-you and a pressing request concerning a tradeline in my credit files that I would like to have revised. I received my Bank of America/Alaska Visa Credit Card in XX/XX/XXXX and have been a loyal customer for almost 5yrs. I also have had a mortgage with Bank of America since I XX/XX/XXXX, that will be 12 yrs in XX/XX/XXXX of this year ( XX/XX/XXXX ). On both of my accounts I have never been 30days late or have been reported as 30days late on my credit report. So I was in disbelief when this error in their system happened and no resolution or consideration has be taken. If my other accounts have maintained a positive payment history why would i start defaulting now and on this card. On XX/XX/XXXX my account was paid in full ( {$13000.00} ) with no balance left. I did that because I knew I was going to have some home projects and additional expenses coming up and wanted to start fresh for tracking purposes. Pay the full amount on XX/XX/XXXX, I was explained by Bank of America customer service, would mean no payment would be needed until XX/XX/XXXX. I then began using my card as expected and to stay on top of my bills I called in on the pay by phone option, which I have done for the last 5yrs, on XX/XX/XXXX and made a payment for {$300.00} to come out on as soon as processed and a future dated payment of {$1000.00} to come out on XX/XX/XXXX ( to cover XX/XX/XXXXand XX/XX/XXXX, as I was explained by customer service that it works that way as long as it is more than what the min payment due ). I received my confirmation code and I was done, so I thought. I had no idea my account was late with no notice at all. To bring insult to injury and fuel my frustration I went into a local Bak of America Branch on XX/XX/XXXX and opened a checking account, ( to assist with direct payments and the possibility of using Bank of America as a business account down the road ) at that time I was not made aware or notified at that time on XX/XX/XXXX that my account had not had a payment since XX/XX/XXXX when I made the {$300.00} payment. Not even after they verified that I had a credit card with them and linked my accounts did they take the time to inform me of this issue. Then on XX/XX/XXXX I came in to make some transactions and that is when I was finally notified that my account had a payment due. I was working so I could not check and verify the problem because to my knowledge I was paid up. Later that evening I checked the account and noticed my scheduled payments had not been processed or done correctly. I made a payment for {$1500.00} on XX/XX/XXXX and then another payment that same day XX/XX/XXXX for {$590.00} to check and see if that would clear my balance and cover my payments for XX/XX/XXXX and XX/XX/XXXX ( like customer service had told back in XX/XX/XXXX ). I also sent an email asking for verification and this is what I was sent on XX/XX/XXXX : " What you need to know The account number ending in XXXX is up to date. Thank you for your payment of {$1500.00} received on XX/XX/XXXX. Your next payment is due on XXXX.I contacted Bank of Americas credit department and was told I was up to date and no issues would happen. I then received an email stating no payment would be needed until XX/XX/XXXX ''. Just to make sure on XX/XX/XXXX I called in and verified I was in the clear. Later that day I then received an alert from XXXX that a 30day late payment was reported from Bank of America. I called again and that was when I was treated as though I intentionally didn't make any payments and was told I constantly have late payments. By looking at my report I have a clean account. I asked for help clarify the issue but was told nothing could be done because it was mandated that they had to report a 30day late payment. As I understand it and what I believe the Fair Credit Reporting Act it is not mandatory but they have the choice to report or not. I then sent emails to the credit dispute side and CEO XXXX XXXX XXXX at XXXX I have yet to hear from him, which I figured he would not being the CEO and all. But I was contacted by a XXXX at XXXX extension XXXX. To " assist me in clearing things up '' I provided dates and amount of payments that were made and requested future dated payments, as mentioned above. I advised her that looking over my files I noticed that I indeed did make a payment and was not late. What had happened is the automated system never processed my payment. XXXX was able to locate my payments but not my request scheduled payments ( which of course how would she find them if the system did not process them, that was the issue ). All she said was I am sorry that is all I can do and have I resolved your issue? Really!! No it was not resolved and nothing was done. She also kept asking to verify my address and I did over and over. I later checked and after updating my address multiple times and setting up a bank account with my current address they still managed to have the wrong address on file. My address is XXXX XXXX XXXX XXXX and they have XXXX XXXX XXXX XXXX. Which is an address I have not lived at since XX/XX/XXXX when I moved to my home that is mortgaged by them at XXXX XXXX XXXX XXXX. So that is why I didnt receive any notification of missed payments and XXXX at at XXXX extension XXXX decided to omit that so I would not get my resolution. I was disappointed that in my time with Bank of America I have stayed current for just shy of 5 yrs. I have had issues in the past with the automated system but was notified. This time I was not notified and nothing has been done to address my issue other than getting an " I am sorry but we can see your payment for {$300.00} in XX/XX/XXXXbut no future payments appeared to be scheduled ''. I have been a loyal BofA customer and paid my accounts on time. Now I am not being taken care of or taken into consideration knowing that I have had almost 5 years of great payment history. In my time of need and simple request for a goodwill adjustment of a 30day late notice so my credit can be where it needs to be for a life changing event of buying a business I am treated like a person that has a history of payment offenses with Bank of America. I have my credit card, checking account, and Mortgage with them. I was in the process of planning a refinance on my new home and transitioning my finances to BofA but I am concerned that if I have issues in the future I will not be taken care regardless if the fault is with BofA out of an issue in their system. I am left with no option since I have reached out to every department in BofA with no resolution.
Since submitting my dispute to CFPB on XX/XX/XXXX Bank of America has issued another 30day late notice. I was contacted on XX/XX/XXXX by XXXX XXXX @ XXXX stating he would be looking into my dispute they received from CFPB. He gave me his extension of XXXX and said to contact him anytime for updates and progress. I contacted him Tuesday XX/XX/XXXX twice and left messages, Wednesday XX/XX/XXXX left message, Thursday XX/XX/XXXX three times and left messages, Friday XX/XX/XXXX and left message, Monday XX/XX/XXXX left message and again today Tuesday XX/XX/XXXX and left message. Each time I have left a message stating my full name and contact number of XXXX. Every time i called it would go through the system ask for the extension which i would input XXXX. I pleaded to get a call back and informed him in the message of the new 30 day late report. His hours are XXXX Mountain standard time per his statement. He finally attempted to call me back today XX/XX/XXXX and left a message. I was working and promptly returned his call leaving a message and he never called back. At this point I feel as thought Bank of America is targeting me for reporting them. Also on the second 30 day notice they reported to XXXX stating they updated my balance they conveniently updated the balance as of XX/XX/XXXX but did not report the payments of {$1500.00} on XX/XX/XXXX and {$590.00} on XX/XX/XXXX. The balance shows correct but they still reported no payment and past duXXXX XXXX times and amount of {$700.00} past due. How is it that my balance dropped from their reported XXXX XXXX and some XXXX to {$11000.00}? They chose to omit that and are not being truthful and providing false information. They also have not address how they have the wrong address on file and have had it wrong even after i have called and changed it in person multiple times. I first started this dispute with XXXX on XX/XX/XXXX and have not had a resolution but been given the run around and now have been targeted. This is against Fair Credit act and should be addressed for false reporting, retaliation, and no sense of urgency to repairing the damage they are causing. I was truthful in explaining my situation on my business purchase and how this is impacting me. It seems as though they have used that information to harm me and are using the process to hide behind and cause extreme damage with the time they are allowed to correct this issue. Something needs to be done and they need to be held accountable for playing with my life."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Difficulty submitting a dispute or getting information about a dispute over the phone"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "Since XXXX, 2013 I have been disputing the removal of US Bank 's credit card account that 's displaying on all XXXX of my credit bureau reports-XXXX, and XXXX. I first disputed this issue with US Bank directly before submitting my online disputes with the XXXX, and XXXX
I spent approximately {$40.00} in USPS Flat Rate Mailing Envelopes that I personally paid for which included documentation that the credit card account reflecting on my reports does not belong to me. I am showing up as a joint account holder, while at the same time I do not recall have any affiliation with US Bank except for owning a checking, and savings account with them. I no longer have those accounts, but nevertheless I never had a credit limit of {$2000.00} with US Bank, or any financial institution for that matter. If I did, I would certainly not have allowed it to get charged off, and I would have found some way of negotiating payment arrangements with them to settle an account with the status it 's reflecting on my credit reports.
I have exhausted my efforts with disputing this directly with the creditor, and the credit reporting agencies. It keeps coming back as verified. This is unacceptable and I would highly appreciate it, if US Bank would remove their credit card account off of my reports immediately.
This has caused significant damage to my credit applications submitted since the time period that I noticed it started becoming negative. I only noticed it on my credit reports once I received a mailing notice from them. Had it not been for their mailing notice they sent me prior to my initiation of the disputes I probably would not have noticed it.
I also find their correspondence department to be a primary cause of the delayed issue resolvement on removing this charged off credit card account from my records.
If it was mine, I would surely claim it, as I would anything else that may, or may not be negative on my credit reports.
I am individual who will pay dues that are owed by me, but I will not pay dues that are owed on behalf of someone else, especially with a charge off of {$2400.00}. That is not going to be tolerated any longer. I am trying to obtain an auto loan, and this account is what is keeping me from receiving one at an affordable APR, and monthly payment.
Therefore, as my last complaint description part of this, I would like to inform US Bank that if this matter is not resolved within 30 days from the receipt of this complaint, I will be going a step further in escalating this to a higher authority."
}
Output:
{
"Issue": "Other",
"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/2022, I was contacted on XXXX XXXX regarding some textbooks I was selling. The individual ( who deleted all of their account information ) asked to send me funds through XXXX, which I have never used before. I never received the funds, and they told me that their money ( {$180.00} ) was being held by XXXX because they selected the recipient ( me ) as being a business and told me I needed to send {$300.00} to a XXXX team member ( " XXXX XXXX '' ) in order to expand my business account limit. They told me that XXXX would give me all the money back ( {$180.00} + {$300.00} ). I got an email ( which I no longer have ) from " XXXX '' with instructions to send to them the money and I sent it to " XXXX. '' Afterwards, I got another email ( which I still have and is attached ) asking for the final process of expanding my business account, which was sending {$500.00}. I told the person that this was a scam and that you should never send money to anyone to open an account. They deleted all of their information and I was unable to settle the issue with the person. Requested a dispute of the transaction through my bank, USAA, and they denied the claim."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem using a debit or ATM card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "This is in regards to the company response on my case.
The number that was provided is a scam. I have called numerous times during normal business hours and left a couple of voicemails with no call back or response. This is just a voicemail line with someone named XXXX XXXX. They make it literally impossible since there is only one person they claim to handle disputes. but is never available, no one ever picks up the phone or call back. Its just a voicemail line and they claim they will call back but they never do."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I will continue to file complaints until this is resolved. Let me know if you need the police report number.
On XX/XX/2022, I was forced to send {$600.00} on cash app to cash app name XXXX XXXX ( XXXX ). I was held against my will and had to reach out to get the money from others on cash app. The police were called and he was arrested. Additionally, he stole my car keys. My cash app name is XXXX XXXX ( XXXX XXXX XXXX is my name that is listed there. I have the police report number which I've offered to provide to cash app. They have been unwilling to assist ; however, I do not believe he should keep my money after it was forcefully taken during the act of a crime."
}
Output:
{
"Issue": "Closing your 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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "In XXXX XXXX an apartment I moved out of was retaliating against me for some disagreements the property manager and I had to do with repairs in my unit that they refused to fix (law violation #1) and towing vehicles. I did not renew my lease with them however, upon giving proper notice of intent to vacate, I somehow acquired a lump sum of charges (XXXX) that I was never informed of (law violation #2). For the record, I was first notified of the charges on XXXX XXXX XXXX by the debt collection agency ProCollect, 2 months after moving. The claims made against me were clearly made in bad faith. XXXX XXXX XXXX XXXX XXXX submitted no evidence to support their allegations and instead the credit bureaus and debt collections agency let a mere copy of the lease agreement constitute as evidence. I sent multiple dispute letters claiming the false statement of fact provided by XXXX XXXX XXXX XXXX XXXX. On XXXX XXXX XXXX I sent Pro Collect a final dispute letter with attachments that refuted all of their claims against me. I received nothing back from ProCollect but now this is the only derogatory mark on my credit and it is completely illegal. ProCollect has violated the fair debt collection practices act. Section 807(5) States that a debt collection agency cannot threat to take action that cannot legally be taken. Section 809(b) States that if a consumer notifies the debt-collector within the thirty day period (a) that a debt, or any portion thereof is disputed, the debt collector shall cease collection of the debt."
}
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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "I received a collection account I believe was paid. In order for them to remove I just paid reduced balance but they still will not remove XXXX or update my payment."
}
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 am a victim of Identity Theft. I was advised by the FTC as to my rights as an Identity Theft victim. I sent in an affidavit and a valid police report. I was careful to include all fraudulent accounts and addresses which needed to be removed from my account. I was informed that the bureaus after receiving proper Identity Theft affidavit and police reports should according to FCRA laws, block these fraudulent accounts within 4 days. It has been several months and XXXX XXXX, Experian and XXXX are still reporting these fraudulent accounts on my profile. The law is very clear as to the civil liability and the remedy available to me ( section 616 and 617 ) if the Bureaus fail to comply with federal law. The bureaus have failed to comply with the FCRA laws and they have not blocked or removed these fraudulent accounts which were opened by a thief and not me. Please contact these bureaus about their illegal behavior and have then comply with federal law and respect the rights of Identity Theft victims, such as myself."
}
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": "Money network is now XXXX against me because of my XXXX. I followed all of their protocol ( ie : submit an identity theft report, submit a police report, submit dispute report and declaration ) yet, money network refuses to properly investigate my claim and replenish my stolen funds from my family and I."
}
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": "Mortgage",
"Sub-product": "Reverse mortgage",
"Consumer complaint narrative": "Wells Fargo keeps avoiding the issue. The most important aspect of deciding a Reverse Mortgage is understanding all that is involved in such an intricate complicated form of a home mortgage. They continue to produce everything other that the most crucial counseling documents such as, an approved Office of Elder Affairs Counselor, an opt in document, face to face counseling with an APPROVED Elder Affairs. The Counseling certificate that Wells Fargo has produced is not even valid in Massachusetts."
}
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": "When I was about 14 years old, I made a poor decision. I misused checking account and it resulted in permanent closure of the checking account. I was unable to open a new account with Chase ever since. When I turned senior in high school, I started regretting about what I've done and I decided to pay back negative balance on my closed Chase checking account. They were still unable to open a new account so I decided to open new account with other banks. I've been responsibly taking good care of my bank accounts and credit ever since then. Now I'm XXXX now, almost XXXX years old, I want to do business with Chase again for my company and personal financial management and growth. So I decided to apply for new account and I got an error message saying I needed to visit Chase branch. So I went to branch next day and banker was unable to open a new account because system didn't allow him to. So he told me to call 1 800 number about this issue and so I did and customer service agent told me that branch manager was the only one that can override it. So I called the branch manager and she said she'd get back to me but she never did ..."
}
Output:
{
"Issue": "Opening an account",
"Sub-issue": "Unable to open an 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": "This company has been notified on several occasions in regards to credit report removal ; however, it 's been removed from XXXX but still reporting as an closed account on XXXX , and still opened on XXXX and also on Experian. This company has violated several FDCPA laws and regulations that 's the reason it 's been disputed and had to be removed from my credit, but it 's still effecting my score and putting a strain on my life. I need this completely updated and removed from my credit ASAP! Otherwise I have no other choice than to pursue further w hich may resu lt in a lawsuit."
}
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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "On my credit reports they have me down as having a bankruptcy charge and it 's incorrect and because I was late on a couple of bills my name was put on the bad credit list! After paying them off I 'm still on the bad credit list and I paid them off on time in fact early. So what can I do to get my name off or fix this problem please help me. Thank you for your time"
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Account status"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "Hi, I disputed charges with Banana Republic credit card, and they are charged through Amazon and XXXX. These charges were dispute about two months ago, and I have periodically called Banana Republic as to where I can submit additional evidence to support my claims, and they told me they would contact me when they needed something. I also called last month asking the same question where I could send additional evidence, and they told me the same response. Today I called again, and they told me the disputes were favored to the merchant. I asked to re dispute, and for them to show me the documents that they received because I did not receive anything. Then, I asked them if AGAIN, if I could please send them information because I have not received anything through the mail, email, or phone calls. They basically decided the disputes against me without listening to my side, receiving my evidence, and not even notifying me of what they needed. Again, I've done my due diligence by reporting the charges and asking where I can send my evidence, and they told me the same thing every time I called. I am requesting the documentation they made their decision with, and to redispute the charges and credit my account."
}
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": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "I have disputed this item with the credit reporting agency and they reported you confirmed the account as valid. I honestly do not believe to ever have any relationship with this collection agency or original creditor. Also there are several inaccuracies with this account the way it's reporting and that's a total violation of FCRA and FDCPA laws and regulations. In a good faith effort to resolve the matter amicably, I must demand proof of this debt, specifically the alleged contract or other instrument bearing my signature, as well as proof of your authority in this matter. Absent such proof, you must correct any erroneous reports of this past debt as mine. I am writing to request that you please provide the following information : 1. Please evidence your authorization under 15 USC 1692 ( e ) and 15 USC 1692 ( f ) in this alleged matter. 2. What is your authorization of law for your collection of information? 3. What is your authorization of law for your collection of this alleged debt? 4. Please evidence your authorization to do business or operate in this state. 5. Please evidence proof of the alleged debt, including the alleged contract or other instrument bearing my signature. 6. Please provide a complete account history, including any charges added for collection activity."
}
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": "WHAT HAPPENEDProduct : Equifax Credit ReportingIssue : Credit reporting agencies ' ( CRA ) investigation was inadequate and failed to consider Creditor Payment History spreadsheet and Borrower Check LedgerDescribe what happened so we can understand the issue ...
Equifax Refused to Require Credible Debt Verification on the Part of Major Subscriber XXXX.
The Federal Trade Commission and the Consumer Financial Protection Bureau should take the lead in forcing Equifax and XXXX to comply with Federal Law and Federal Court Order. Moreover, CFPB has recently issued a Bulletin finding inadequacies in the CRA validation process and there is a pending Federal Court case challenging same ( State of Mississippi XXXX Experian Information Solutions , Inc. XXXX. ) Equifax refuses to require " Furnisher '' XXXX to bring its credit reporting system into compliance with the Fair Credit Reporting Act ( FCRA ) ; acceptable debt validation process in general ; and, the XXXX 2014 United States District Court Consent Order, in particular. Equifax continues to use unverified data provided by XXXX in its valuation of credit reports and resulting credit scores. Although the Consent Order systematically invalidates XXXX 's data collection/reporting process, Equifax appears to be acting in tandem with XXXX to refuse to facilitate credible/acceptable debt verification. In fact, Equifax simply ignores the existence of clear and convincing evidence that XXXX has " routinely '' misreported debtor payment history and otherwise made inappropriate " assessments '' to Borrower accounts. XXXX has repeatedly refused to provide Borrower with monthly statements and attributes the failure to inadequate systems capability. Given this self-acknowledged limitation, it is impossible for a reasonable person to accept XXXX 's position regarding alleged arrearage.
In the instant matter, Equifax unilaterally accepts XXXX 's undocumented position that I am {$15000.00} behind in mortgage-related " assessments '' and 12 months behind in mortgage payments. Among other credit report wrong-doings, I am legally entitled to copies of Equifax 's debt validation referral to Furnisher XXXX - and XXXX 's response. XXXX and Equifax should not be allowed to engage in secret communications concerning debtor account and inquiries. I have repeatedly requested a detailed breakdown of the alleged and bogus {$15000.00} assessments and identification of the specific 12 months of mortgage payments I am allegedly in arrears. Providing this information is crucial given the District Court 's invalidation of XXXX 's internal reporting system, and the fact most of these charges were assessed during the period I was under Chapter XXXX protection ; a clear violation of Federal Bankruptcy law.
Equifax should not be allowed to ignore un-refuted evidence ( in the form of the Consent Order, check ledger and Payment History spreadsheet ) of wrong-doing and systematic misreporting on the part of one of its major subscribers.
Equifax refuses to address XXXX 's ongoing refusal to provide Borrower monthly statements. XXXX attributes the failure to inadequate systems capability. Borrower asserts such self-acknowledged limitations invalidates any payment information advanced by creditor, including any alleged arrearage."
}
Output:
{
"Issue": "Credit reporting company's investigation",
"Sub-issue": "Problem with statement of 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": "XXXX XXXX is attempting to collect an alleged debt without providing any proof of contractual obligation of myself to said debt. XXXX XXXX is reporting to all major credit bureaus. I have requested legal proof of the obligation from the credit bureaus and the furnisher from XXXX XXXX. My requests have been denied by all credit reporting agencies, and have been ignored by XXXX XXXX. The credit bureaus have all returned a status of " Verified '' for the account upon my dispute initiations. I dont understand how the account is being verified with XXXX XXXX XXXX XXXX and Experian XXXX when the company doesn't even respond to my verification and validation requests, the person in which they are attempting to collect the alleged debt from? The company is clearly unable to provide proof or contractual obligation for the alleged debt, yet, XXXX XXXX is still able to post to my credit report, which is a violation of my consumer rights. I have reached out to XXXX, XXXX, and Experian to ask for the proof the company is providing as verification and validation of the account. My requests were denied. I was advised by XXXX, XXXX, and Experian that it is not their obligation to provide me with proof of the account, and I should reach out to the creditor. I have reached out to the creditor, and the creditor has ignored the request."
}
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": "There is a closed account from XXXX, Account # XXXX on my credit report that doesn't belong to me. This account was opened in XX/XX/2016. It has been disputed by all three credit bureas & XXXX/XXXX. It was resolved & deleted from my credit report but for some reason it keeps reappearing on my Equifax report with no explanation. I have disputed this again with no luck. I am demanding that Equifax abide by the FCRA & delete this account from my credit report. It is fraud & damaging to my credit!"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Old information reappears or never goes away"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, North Carolina XXXX Date of Birth : XX/XX/XXXX SS # : XXXX Experian XXXX XXXX XXXX XXXX, TX XXXX Dear, Experian Re : This is not a regular dispute. Take action immediately you are in violation.
I am a victim of identity theft and I write to dispute certain information in my file resulting from the crime that i recently became aware of. The items I am disputing do not relate to any transactions that I have made or authorized. Please REMOVE this information at the earliest possible time.
As required by section FCRA 605B ( 15 U.S.C. 1681c-2 ) a copy of which is enclosed which states that this information must be removed within four business days of receipt, I am requesting that the adverse information on the items be removed to correct my credit report.
Please investigate these matters and remove the disputed items as soon as possible.
1. The following personal information is incorrect Account Number : Also Known As : XXXX XXXX XXXX -This name belongs to someone else. Delete it from my report immediately.
2. The following personal information is incorrect Account Number : Previous Address ( es ) : XXXX XXXX XXXX XXXX XXXX, NC XXXX XX/XX/XXXX -This address belongs to someone else. Delete it from my report immediately.
3. The following personal information is incorrect Account Number : Previous Address ( es ) : XXXX XXXX XXXX XXXX XXXX, NC XXXX XX/XX/XXXX -This address belongs to someone else. Delete it from my report immediately.
4. The following personal information is incorrect Account XXXX : Employers : XXXX XXXX -This name belongs to someone else. Delete it from my report immediately.
5. XXXX XXXX XXXX XXXX
Account XXXX : XXXX I never had any business relationship with this company. Hence, there is no way this account belongs to me. Please delete this unknown account from my credit report right away.
6. XXXX Account XXXX : XXXX Please verify the payment status, balance, comments, payment history and account number as they might be incorrect. Fix or delete this account from my report completely.
7. XXXX XXXX XXXX Account Number : XXXX Please verify the payment status, balance, comments, payment history and account number as they might be incorrect. Fix or delete this account from my report completely.
8. XXXX XXXX Account Number : XXXX Please verify the payment status, balance, comments, payment history and account number as they might be incorrect. Fix or delete this account from my report completely.
9. XXXX XXXX.
Account XXXX : XXXX I never had any business relationship with this company. Hence, there is no way this account belongs to me. Please delete this unknown account from my credit report right away.
10. XXXX XXXX Account XXXX : XXXX Please verify the payment status, balance, comments, payment history and account number as they might be incorrect. Fix or delete this account from my report completely.
11. XXXX XXXX XXXX XXXX Account Number : XXXX Please verify the payment status, balance, comments, payment history and account number as they might be incorrect. Fix or delete this account from my report completely.
12. XXXX XXXX XXXX XXXX Account Number : XXXX Please verify the payment status, balance, comments, payment history and account number as they might be incorrect. Fix or delete this account from my report completely.
13. XXXX XXXXXXXX XXXX XXXX Account Number : XXXX I never had any business relationship with this company. Hence, there is no way this account belongs to me. Please delete this unknown account from my credit report right away.
14. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Inquiries have reached the statute of limitation.
Enclosures : Proof of identity : Proof of Address, ID, FCRA 605b Enclosure Identity Theft report Please note that you have 30 days to complete this investigation, as per the Fair Credit Reporting Act Section 611 ( a ) ( 1 ) ( A ). Be advised that the description of the procedure used to determine the accuracy and completeness of the information is hereby requested as well, to be provided within ( 5 ) days of the completion of your re-investigation and I am keeping a careful record of your actions, including your Method of Verification. I do not consent to e-Oscar or any means of automated verification.
Failure to respond satisfactorily within 30 days of receipt of this certified letter may result in a small claims action against your company, seeking {$5000.00} per violation for : 1. ) Defamation 2. ) Negligent Enablement of Identity Fraud 3. ) Violations of the Fair Credit Reporting Act 4. ) Financial Injury My contact information is as follows : XXXX XXXX XXXX XXXX XXXX XXXX XXXX, North Carolina XXXX SSN : SS # : 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. I am also including a copy of my complaint to the organizations below : CC : XXXX XXXX XXXX XXXX of XXXX Consumer Financial Protection Bureau XXXX XXXX XXXX XXXX Washington, XXXX XXXX CC : Law Firm XXXX XXXX XXXX XXXX XXXX XXXX XXXX : Consumer Financial Protection Bureau CC : Attorney Generals Office CC : XXXX XXXX XXXX XXXXXXXX : XXXX XXXX XXXX : XXXX XXXX XXXX XXXX XXXX : Comptroller Of The Currency CC : Federal Reserve XXXX XXXX : Credit and insurance CC : Federal Trade Commission XXXX : XXXX XXXX XXXX XXXX : California Regulatory agency Division of consumer complaints Thank You, Sincerely, XXXX XXXX"
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Chase violated its own credit card agreement. The agreement information is located on the statements. All of the balance on this card is under a " My Chase Plan. '' The " My Chase Plan, '' agreement states that no interest will be charged on any portion of the associated plan. The statement, under " How to Avoid Paying Interest Charges, '' states that in any no-interest promotions, " balance '', is not a purchase, it's a balance it does mention purchase subject to an interest-free balance, but then states that the interest charges are not charged on a balance. Per the My Chase Plan agreement, the balance includes the fee associated with the plan. It's 0 % interest but is a fixed fee charged for the plan. The balance also includes late fees as it does state that missed payments can result in late fees. These would be directly related to the balance. I have a strange charge going from {$.00} going up to {$1.00} this month. The balance was {$0.00} when the Chase Plans were created. The balances are subject to the 13.24 % interest are odd low dollar amounts. The only additional charges on the account would be late charges and the plan fees. Per the card agreement on the statement, interest is charged for purchases, cash advances, overdraft advances, and balance transfers. There is no mention of late fees being part of interest rate calculations. I have been late on this card for a few months, due to unfortunately having financial issues due to my XXXX XXXX. The agreements Chase has do allow for speaking with them for help with financial issues. Which in my opinion, gives you the allowance to set up additional payment options if needed. The agreement specifically states, pay the payment by the due date or we will assess a late fee. I let them assess the late fees. They violated the agreement by charging interest on the My Chase Plan fees, which is not allowed in the My Chase Plan agreement. Also, Chase calls me constantly, I have asked them to stop, but they won't. The one time I asked for help, they told me, and I quote " I'm sorry, I can't help you. '' This was a gentlemen I spoke with. This was a call that I initiated. I called the collections department for this. I was specifically told that."
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Charged too much interest"
} |
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