messages
stringlengths 1.87k
34.5k
|
|---|
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account 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": "Case was originally opened under CFPB Case : XXXX and Capital One Case : XXXX. I received an letter which was also uploaded into this cfpb system stating that my accounts were eligible for SCRA benefits and we enrolled. I called to pay off the remainder of the debts to be determined following a recalculation of the new amount due only to find that Capital One only applied SCRA benefits on the account ending in XXXX. The account ending in XXXX was not recalculated and the file was documented as " no adjustments to be made to account. '' I am a Ohio resident and SCRA applies to all debt prior and during military service. The interest rate for this account should have been capped at 6 %."
}
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": "I had a lease with XXXX XXXX XXXX . My car was in the accident and it was a total loss. I had full coverage with insurance and they supposed to pay for the car in full. But they had issues with body shop where I took the car, so they did n't pay in full. I was suing insurance company and I won in court, but the whole litigation process took over a year. Once I got money from insurance company after winning in court, I paid to XXXX XXXX XXXX Since insurance company did n't pay in full initially, XXXX XXXX XXXX put a mark on my credit bureaus profiles that I have a derogatory account. And even after I paid them with the money I got from the insurance company, it still stays on my credit report. I always pay everything on time and do n't have any missed payments ever, but with this situation with XXXX and insurance company now I had a bad account on my credit report which stays there for 7 years and prevents me from buying a house or getting any loans."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "There is a collection reporting in my credit that was just reported that is a result of Identity theft and someone using my personal information.
I tried reaching out to the company and letting them know and also disputed the account with XXXX and they continue to try to collect and damage my credit. the account number is CAPIO PARTNERS LLC Original creditor : XXXX XXXX {$590.00} Balance updated XX/XX/2021 Account Info Tap labels for more details Account number XXXX Collection opened XX/XX/2021 Account type Collection Paid off : 0 % Balance on XX/XX/2021 Original balance {$590.00} {$590.00} Payment Info Original balance {$590.00} Balance {$590.00} Status Seriously past due date / assigned to attorney, collection agency, or credit grantor 's internal collection department Status date XX/XX/2021 Additional info Comments Account information disputed by consumer Your statements **CONSUMER STATEMENT** Y ITEM DISPUTED BY CONSUMER"
}
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": "Mortgage",
"Sub-product": "VA mortgage",
"Consumer complaint narrative": "Carrington Mortgage Services , LLC took over our mortgage from XXXX in XX/XX/2022. The account # is XXXX. We made our 1st payment to them XX/XX/2022 online for the regular payment amount. Then I sent in a check for principal only, and put that on the front of the check, for {$1300.00} and sent with their payment voucher and put that amount in the principal only box. They took that payment and treated it like a regular payment. I contacted them XX/XX/2022 when I discovered the mistake and was told it would take 5-7 business days to correct.
I sent in our next regular mortgage payment XX/XX/2022 online and they have just put it in a suspense account. This should be been treated as a regular payment.
On XX/XX/2022 I sent in a payment of {$700.00} for a shortage of escrow and they did post that correctly.
XX/XX/2022 I sent in a payment online for the regular payment amount of {$1200.00} plus additional principal of {$710.00}. I don't know why they posted the regular payment amount to principal. They messed up this payment as well.
I am fed up with this company. They are rude, and are misappropriating our money. I am afraid this is going to affect our credit score negatively. This is an absolutely TERRIBLE mortgage company. I have never had such terrible service.
PLEASE HELP."
}
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": "Money transfer, virtual currency, or money service",
"Sub-product": "Mobile or digital wallet",
"Consumer complaint narrative": "i have an account with COINBASE so i was trying to get refunded thru bit connect with customer support and what they did to make it easier THE process they guided me on a share screen and needed my wallet address TO REFUND they made me send like $ XXXXto XXXX next thing it happened they empty my XXXX account from COINBASE for about XXXX XXXX coins value at over {$34000.00} on XX/XX/2018 called 10 minutes after it happened to coinbase and they gave me case number XXXX and they locked my account right away and said a specialist would take over i only have a confirming email stating that somebody will get in touch soon i called again on XX/XX/XXXX and they say they got no response and was never seen by level 2 specialist and They were gon na put a higher priority so it can be contacted and no time frame at all being that i called after 10 minutes and when i tried cancelling online i wouldn't have an option to cancel luckily found the XXXX number thats when i called so I'm really desperate for help looking at previous reviews they said it could take months to get it resolved so just trying to get ahead and see i was hoping transaction will be cancelled since large amounts it could take time to be released so 10 minutes should've been more than enough I'm waiting for my account to be reopened with all my funds included if you can intermediate so get this issue resolved it will be greatly appreciated and if you have any suggestions on how to handle this org if i need to report to police authorities"
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I sent a letter on XX/XX/22 to 3 Credit Bureaus to remove erroneous items that are UNKNOWN to me. I came across this law today and according to the Per FCRA 605B ( 15 U.S.C. 1681c-2 ) they are required to remove any accounts or information not later than 4 business days after the date of receipt. Ive already included this in my previous Complaint # XXXX, XXXX, and XXXX & I am including it again., XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I purchased a car from XXXX - XXXX XXXX through XXXX XXXX XXXX on XX/XX/2021 for my business, XXXX XXXX XXXX XXXX XXXX. The finance manager told me that XXXX XXXX XXXX does not report to the personal credit file with a XXXX unless you default on the loan. My contract shows my business name only, but they have reported this on my personal credit file when it should only be reporting to my business credit file because I have not defaulted on the loan."
}
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": "Hi I am submitting this XXXX XXXX without any influence and this is not a third party. TransUnion has low and unfair credit number for me in their report. I have complained. The problem has not been resolved. my XXXX has me at a XXXX XXXX over XXXXtransunion has me at a score around XXXX. That is a huge difference. transunion paints me as a XXXX. my XXXX say I have good credit. What the heck is going on here. i have almost no debt and my identity was stolen causing my score to drop XXXX i made this clear for XXXX days straight with transunion i spoke to a representative agent name XXXX and XXXX and XXXX from the fraud department I prefer to speak to a XXXX rept but they refused they had me on mute for XXXX hours which was hurtful I have a perfect repayment record. I have very low credit utilization. I have XXXX negative credit items outstanding debt now. I have modest but ok income. Social Security. Something is wrong with Transunion. I do not understand why they are XXXX consumers .This was a fist step towards attempting resolution. They kept lying telling me they disputed XXXX its not reporting but it keep reporting this inaccurate information without my authorization. They refused or were unable to verify XXXX remove the inquiries and its been XXXXdays XXXX they record the calls XXXX admitted they had my police report XXXX ftc and XXXX That was after attempting to contact Transunion more than XXXX times. transunion is an XXXX company. They are supposed to be protecting consumers. They need to be reigned in. they are causing me severe XXXX and stopping me from getting this job offer XXXX now im XXXX XXXX cant provide to my XXXX year old XXXX PLEASE HELP ME PLEASE"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the fair credit Reporting act XXXX Account # XXXX, has violated my rights.
15 USC 1681 Section 602 States I have the right to privacy.
15 USC 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions.
15 USC 1666B : A creditor may not treat a payment on a credit card account under an open end consumer credit plan as late for and purpose."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/XXXX XXXX XXXX XXXX, started reporting a debt on my Equifax report as in collections. Stating that this is an old XXXX XXXX Account. The account in question has been closed since XX/XX/XXXX and charged off by XXXX. Date Reported : XX/XX/XXXX Original Creditor Name XXXX XXXX XXXX, Balance Date XX/XX/XXXX Date Assigned XX/XX/XXXX Account Designator Code INDIVIDUAL_ACCOUNT Original Amount Owed {$1300.00} Account Number XXXX XXXX Amount {$1300.00} Creditor Classification Financial Status Date XX/XX/XXXX Last Payment Date Status UNPAID Date of First Delinquency XX/XX/XXXX The DoFD has been re aged by this collection agency. I have reprinted copies of my XXXX XXXX XXXX from this account that clearly shows a XX/XX/XXXX date."
}
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": "Prepaid card",
"Sub-product": "Mobile wallet",
"Consumer complaint narrative": "I have a Paypal account where I have funds. Suddenly, Paypal sent a notice that my account is limited and that I can not withdraw my own funds."
}
Output:
{
"Issue": "Managing, opening, or closing account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I signed up for a Southwest Airlines credit card through Chase using a link Southwest had sent me in an email about a promotion for XXXX mile bonus offer with its credit card. The promotion required that I apply and be accepted before the offer ended on XX/XX/2021. I received my approval on XX/XX/2021. The requirements for the XXXX bonus miles were a ) spending {$3000.00} within the first 3 months and b ) spending a total of {$12000.00} total during the first 12 months. I met the spending requirements, but I only received XXXX bonus miles. I thought this was a computer error and would resolve itself, so I waited until XXXX to contact Chase.
XX/XX/XXXX, XXXX I called and spoke with a customer service agent, who put in a request for the XXXX mile bonus offer. He told us to wait 30 days for the additional XXXX miles to be credited to my account.
XX/XX/XXXX, XXXX I called for an update on the request for the missing XXXX miles and was told that the note in the file indicated that the marketing team researched the issue and found that there had NEVER been a promotion for XXXX bonus miles. I asked what I needed to do to submit documentation of this promotion. I was told to use the secure messaging on the Chase website.
XX/XX/XXXX, XXXX I submitted a written request for the missing XXXX miles and submitted two articles ( one from XXXXXXXX XXXX XXXXXXXX, the other from XXXX XXXX about this XXXX mile bonus offer. Additionally, I submitted the approval email from Chase dated XX/XX/2021 to show that I met the deadline.
XXXX wrote a complaint to Southwest Airlines about using Chase as a partner for its Rapid Rewards program and explained what Chase had done.
XX/XX/XXXX, XXXXChase responded via the secure messaging. This response was vague and unacceptable.
XX/XX/XXXX, XXXXSouthwest Airlines representative XXXX XXXX politely responded that this issue needed to be resolved with Chase that Southwest Airlines would do nothing to resolve the matter.
XX/XX/XXXX, XXXX wrote another request to Chase for an explanation for not giving me the full XXXX bonus miles.
XX/XX/XXXX, XXXXChase responded . The response was vague and unacceptable.
XX/XX/XXXX, XXXX-I wrote request the expiration date of the XXXX mile bonus offer.
XX/XX/XXXX, XXXXChase responded . The response confirmed that Chase had not changed the expiration of the promotion, so I still believe I have met all requirements for the XXXX bonus miles.
XX/XX/XXXX, XXXX called Chase and asked what error I made that I did not meet the requirements for the XXXX mile bonus offer. The agent told me I had made no errors. He said that the department which reviews and approves the applications can change the promotional offer you are approved for. I asked what the criteria is during the application process to change an applicant to a lower promotional offer. He could not tell me. I asked him for the date that I was informed that I had been informed of the change to a lower promotional offer. He could not tell me a date. I asked how I was informed of this change to a lower promotional offer. He said US mail. I requested that this letter be resent. He said this could not be done. I asked for him to send it as an attachment via email. He said he could not do that because of confidentiality standards. I requested to speak to a different agent. He transferred me to a different agent who then told me that the switch to a lower promotional offer is entirely random. I clarified with her that she was saying that Chase did not actually intend to make good on the XXXX mile bonus offer to everyone who meets the requirements. She agreed and said that it is random who they approve for the promotion. XXXX could not give me an employee identification number, but she was willing to give me the following case number : XXXX XX/XX/XXXX, XXXX called Chase one last time to try to speak to a supervisor one last time and warned I was calling to give Chase one last opportunity to resolve the issue about not honoring the XXXX mile bonus offer before I filed a formal complaint with the Consumer Financial Protection Bureau XXXX I was transferred to a supervisor and waited on hold for an hour and a half. I hung up and called again and was placed on hold while I waited to talk to an account supervisor. Finally, I spoke with an account supervisor XXXX XXXX XXXX who said that the case had already been escalated to the marketing department. If they had declined the request for the additional bonus miles, there would be nothing more she could do. I remarked a previous agent read the notes in the file that said the marketing department found that the XXXX mile promotion had never existed. I informed her that I would proceed with filing a formal complain with the consumer Financial Protection Bureau."
}
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": "Government benefit card",
"Consumer complaint narrative": "I have a Bank of America XXXX debit card, into which XXXX ( California XXXX XXXX ) deposited moneys owed me by XXXX for my XXXX XXXX claim. I have only used the card for ATM withdrawals. One day I checked my statement and found that a number of store purchases had been made totaling {$720.00}. I reported the fraudulent charges to Bank of America fraud line. They refunded me the full {$720.00} for all of the fraudulent charges. However, they froze my account.
Today XX/XX/21, I called B of A to request a new card. At this time I had my ATM receipts and a calculator in front of me, and I upon examination of my account statements on the B of A website, compared with my ATM withdrawals, I discovered that {$100.00} was missing from my account. I was able to determine that the {$100.00} disappeared between XX/XX/21 and XX/XX/21, so I called the B of A fraud line again. This time the first person I spoke to saw the discrepancy immediately and transferred me to another individual " who could help you sir ''. This person told me that because they could not see any transaction for {$100.00} that there " ... is nothing I can do to help because I can only correct fraudulent transactions ''. The person they transferred me to, evidently, could not add, because, after some minutes of agreeing with me then disagreeing, finally started raising her voice, interrupting, then yelling at me. I had been on the phone for XXXX minutes and I spoke to XXXX people, all claiming they corrected fraud issues. The last person hung up on me because I called her out for yelling nonsense at me.
The facts are these : On XX/XX/21 I did a balance inquiry and found that I had {$1200.00}, so I withdrew {$1000.00}, leaving me {$220.00}. The next transaction I made was on XX/XX/21, and that was a balance inquiry, which informed me that my balance was {$110.00}. This means that {$100.00} was taken from my account. I have only used this card to deduct money from ATMs, and there was no withdrawal for {$100.00} nor was there a purchase for that amount. Thus, B of A somehow removed {$100.00} from my account. I meticulously added all of the transactions and sure enough, B of A removed {$100.00} from my account! B of A employees stated that they could not deal with this because although the funds are missing, there is no transaction by which the money was taken, and so finally, they hung up me. After XXXX minutes trying to get the {$100.00} B of A mysteriously lost from my account.
I will call again tomorrow, however, evidently, B of A fraud employees do not know how to write up monies missing from an account unless the money is missing via a fraudulent transaction. This theft occurred via their software, and they know it, but refused to do anything about it."
}
Output:
{
"Issue": "Problem with a purchase or transfer",
"Sub-issue": "Overcharged for a purchase or transfer you did 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": "Since XX/XX/XXXX-XX/XX/XXXX I've sent 4 separate letters to each credit bureau XX/XX/XXXX, Transunion, and XX/XX/XXXX asking that they provide me proof of an account on my credit report. Since they have not provided me proof. I've asked them to remove the account. As of the time of this writing they have not removed the adverse account.
It was my understanding that under SECTION 609 of the Fair Credit Reporting Act, that the credit reporting agencies are suppose to provide me a copy of the original contract that I signed and as the credit reporting agency they are supposed to have. I mean, if they are verifying the account as being valid/correct then they, by law, are supposed to have a copy of that contract to do so."
}
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 not a duplicate nor is this complaint being filed by a third party, I am filing this complaint myself. Please see this complaint is processed to the letter of the law. Im submitting a complaint to you today to inform you I was the victim of identity theft. I researched on how to remove the fraudulent accounts in my report and found that I need to visit FEDERAL TRADE COMMISION or https : XXXX to file a report and Per FCRA section 605b Credit Reporting Agencies are required to remove/block any accounts listed on an id theft report. Please find the ATTACHED documents to assist in the blocking of the erroneous information which is being posted to my report.
Here is the list of accounts/items which do not belong to me or were opened without my permission.
XXXX XXXX XXXX XXXX XXXX XXXX, NY XXXX, XXXX XXXX Date Opened : XX/XX/XXXX Balance : {$13000.00}, XXXX XXXX Date Opened : XX/XX/XXXX Balance : {$0.00} According to FCRA Section 605B ( a ) the CREDIT REPORTING AGENCIES shall block any information in the file of a consumer that the consumer identifies as information that resulted from an alleged identity theft, not later than 4 business days after the date of receipt."
}
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": "Student loan",
"Sub-product": "Private student loan",
"Consumer complaint narrative": "Navient took way more money out of my account than authorized and never issued my refund. I have been constantly calling them and sending them my bank statements to get this resolved, however they continuously give me the run around and each rep provides me with different information and different steps to take. I have submitted bank statements twice that reflect I have never received the refund.
I believe they are engaging in unfair and deceptive business practices."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Trouble with how payments are being handled"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I applied for a XXXX Credit card through Synchrony Bank in the middle of XXXX 2016. I was denied credit and later sent a letter dated XXXX/XXXX/2016 in which it reported i was denied because of " Bankrupcy Proceeding, credit Counseling or Creditor Settlement ''. It said its decision was based on information from XXXX. I contacted transunion and they report that Synchrony Bank did pull my credit BUT that there was none of the above information listed on my credit report that I was denied. I then call and talk to Sychrony Bank and they cant give me a reason for denial but then one agent reported that the credit bureau denied me which is false because that 's not how it works. No one including supervisors could tell me an accurate reason for being denied credit."
}
Output:
{
"Issue": "Credit determination",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In correspondence dated XXXX XXXX 2017 I mailed this credit repository my request for a dispute, as my credit report contained a number of erroneous entries. Now that over 30 days have passed I have found this company barely investigated any items and my report is still largely incorrect. In addition, when I reached out the data furnishers they claimed not to be reporting any of the errors the credit repository is showing and claimed they had not been contacted by the repository either."
}
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": "AES slash p h e a and XXXX XXXX XXXX reporting that I was making payments to them and then I stop making payments for 10 months showing 30 late payments on three different loans that are duplicates from three other different Loan Servicing Company which are all paid off including the ones held by these people who say that I made 30 payments. The loans were in deferment during this time and there's no way that I wasn't making the payments because they were in deferment. I wasn't making any payments so therefore there couldn't have been any late payments before or after the loans have been paid in full. I've disputed the loans with XXXX and XXXX and they're still showing up on three of the XXXX XXXX XXXX has 10 late payments on each loan totaling 30 late payments which is hurting my chance to rent an apartment and buy a car because of the late payments. I had a loan of XXXX one for XXXX and one for XXXX and they've all been paid off satisfactorily through XXXX. I never made a late payment!"
}
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": "Prepaid card",
"Sub-product": "General purpose card",
"Consumer complaint narrative": "I called American Express to inquire on how I could get a gift card to give to my son to use on his XXXX trip. He was leaving the next afternoon. I was told to go to any number of retailers and purchase a " serve '' card. I went to the bank and got {$300.00} cash and purchased the card for XXXX. plus a service fee. Later that evening I was told by American Express that the card could not be used internationally until I got my permanent card in seven to ten days. I would not have spent money and gone to the time and trouble to get the card if I had been told the right information at first.
Then I was told to register the temporary card and I could set up a pin and get my money form a aM machine. That also did not work. Today, 7 days later I received my permanent card. I called in to activate it. Next I was asked how I was going to put money on it. I inquired about the XXXX XXXX that I had already given them. I was told that I must not have entered the temporary card information and that my XXXX was still floating around in American Express 's pocket and I had no way to get to it STILL. I asked for a supervisor and got a condesending person named XXXX in the XXXX. He would not listen to anything I was saying and told me it was all my fault. Now he had me re-register the temporary card in MY WIFE '' S name and set up another pin. I told him I had done all thisin my own name when I first ordered the permanent card. He told me I did not. All he did was argue with me and talk over me. I then asked for his supervisor and he refused to let me speak to one. His name was XXXX and that was all he would tell me. If finally told him what I thought of his condescending attitude. Results are in. I have wasted a lot of time and tied up XXXX XXXX dollars, my son left on his trip without the money I had promised him, I am out the fee for the card and whatever the atm charge might be even if I am lucky enough that this scheme of putting it in my wifes name works to retrieve what is left on my {$300.00}. All the while American Express had had my money, charged me a fee for a worthless card that was sold to me on false pretenses. I have an XXXX, own XXXX and am very astute with computers. If this is what happened to me, imagine what would happen to a less experienced consumer. I highly suggest that you look into this American Express " Serve card Scheme. I await your response"
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Bank account or service",
"Sub-product": "Savings account",
"Consumer complaint narrative": "My CapitalOne 360 savings account was hacked. It took 3 calls to the security people before they answered. My account was subsequently locked. A week later I still do not have access to my funds. Ive called half a dozen times and waited literally hours on hold without reaching a representative. Their voicemail box option is full. They are holding my funds hostage and I can not access them. Completely unacceptable."
}
Output:
{
"Issue": "Deposits and withdrawals",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "These inquiries below are not authorized by me.
XXXX XXXX/XXXX XXXX XX/XX/XXXXXX/XX/XXXX XXXX XXXX, XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, AZ XXXX BYMAILONLY XXXX XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, DE XXXX XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX ( XXXX ) XXXX XXXX XXXX XXXX XXXX XXXX, UT XXXX XXXX XXXX XXXX XXXX XXXX/XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX ( XXXX ) XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX XXXX XXXX XXXX XXXX/XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX - non specific XXXX XXXX XXXX XXXX XXXX XXXX, MN XXXX XXXX XXXX XXXX XXXX XXXX/XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NJ XXXX XXXX XXXX XXXX XXXX XXXX/XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NJ XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX, TX XXXX XXXX XXXX XXXX XXXX"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On my credit report there are hard inquiries that I had from companies that I did not give my consent to and that I don't have an account with at all.. that makes improper use of my report..
XXXX XXXX XX/XX/2019 Bb & t dealer XXXX- XX/XX/2019 XXXX XXXX XX/XX/2019 XXXX XXXX- XX/XX/2019 XXXX XXXX XXXX XXXX XX/XX/2019 XXXX card - XX/XX/2019 XXXX - XX/XX/2019 XXXX XXXX - XX/XX/2019 XXXX XXXX XXXX XXXX."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/2021 I sent in a request for EQUIFAX to update my creit report information and remove unauthorized inquiries. I sent in documents to verify my identity which were my drivers license that shows my current address, my social securuty card, and a bill that shows my mailing address. I contacted EQUIFAX omn the following dates : XXXX, and XXXXI explained to EQUIFAX that I am unable to retreive my report from MYEQUIFAX.COM.It tells me that I have no identifying information and none of my credit accounts show on the report, everything shows XXXX.EQUIFAX claims to have escalated this issue 6-7 times to no avail. EQUIFAX gave me a case # XXXX on XXXX, and informed me that it would be escalated yet again. I pay for credit monitoring services and I'm unable to retreive my credit report neither are my creditors. I just attempted to retrieve my credit report today and it says '' NO INDICATOR ON FILE '' .I have attached the report from today as well. EQUIFAX continues to delay in correcting this issue that Iv ; e been dealing with since XXXX XXXXEQUIFAX has my identifying information and documents ( color copy ) that verify my identity."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/2018, XXXX XXXX pulled my credit and apparently gave permission to a third party, XXXX XXXX XXXX, to pull my credit on the same day ... which resulted in two credit pulls on the same day ... which I didn't authorize."
}
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": "Money transfer, virtual currency, or money service",
"Sub-product": "Debt settlement",
"Consumer complaint narrative": "This company is continuing the same business practices as described in legal action brought forth by CFPB. I am referring to the below which describes my situation exactly.
WASHINGTON, D.C. The Consumer Financial Protection Bureau ( CFPB ) today filed a lawsuit against Freedom Debt Relief, the nations largest debt-settlement services provider, and its co-CEO XXXX XXXX for deceiving consumers. The CFPB alleges that Freedom charges consumers without settling their debts as promised, makes customers negotiate their own settlements, misleads them about its fees and the reach of its services, and fails to inform them of their rights to funds they deposited with the company. The CFPB is seeking compensation for harmed consumers, civil penalties, and an injunction against Freedom and XXXX to halt their unlawful conduct.
I have had to start negotiaons myself, most of the creditors indicate no contact was made with Freedom debt relief on my behalf. Because of this XXXX card a creditor sued me, I stepped in and started negotiations with the creditors attorney."
}
Output:
{
"Issue": "Confusing or misleading advertising or 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 card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "In XXXX 2015 the minimum payment on my credit card was {$270.00}. I paid {$300.00} and did not make any purchases, etc. on the card. As I understand it, {$270.00} should have gone toward the interest and the principal of the lower interest rate ( 9 % ) and at least {$24.00} should have gone to my highest interest rate of 16 %. When I look at my statement from XXXX and my statement from XXXX, though, the balance actually increased for the amount being charged 15.99 %. I called Bank of America XXXX times and spent more than an hour on the phone. The XXXX rep told me it was because I had underpaid XXXX month ( I did not ), the next call I got disconnected and the XXXX transferred me to a line that was supposed to have voicemail, but rang for more than 3 minutes without being picked up."
}
Output:
{
"Issue": "APR or interest rate",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Other banking product or service",
"Consumer complaint narrative": "It is a strange threat they made, vaguely that they would attack my account "... .Ending in XXXX. '' It sounds more like a marketing ploy, but they threaten to " Block '' my access to my account if they can't extort the new information they want. This is part of an increasing problem with Citibank engaging in coercive information phishing. They have previously demanded information on NONCitibank accounts and closed a credit card account refusing to make a refund for my overpayment. ( CFBP worked that out for me, thank you. ) Now they're at it again.
This account includes an unwanted checking account which they opened themselves, XXXX XXXX style, against my wishes and threatened to close the account I did want if they didn't get to keep it. I will now close that XXXX XXXX style account in as big a confrontation required to for them to crawl back under their rock."
}
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 card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "My understanding is that, per the new laws enacted shortly after President Obama took office, credit companies were no longer permitted to apply payments in excess of the minimum balance to the lowest interest balances in favor of those w/ higher balances. In my case, today I contacted Synchrony Bank ( XXXX ) re : my Gap Visa from which I took a cash advance of {$1000.00} on XXXX/XXXX/16 and learned the following : I now have an overall balance of {$4000.00}, purchases are subject to an int rate of 25.24 % ; cash advances are subject to an int rate of 27.24 % HOWEVER even if i pay more than the minimum monthly payment ( ~ {$80.00} ) NONE of that additional payment can or will be applied to the cash advance balance until I have paid off the lower interest purchase balance IN FULL. Is this still allowed??"
}
Output:
{
"Issue": "Cash advance",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XX/XX/1973 XXXX XXXX XXXX XXXX XXXX Ss # XXXX Credit Bureau XXXX Experian I need you to investigate the following accounts XXXX XXXX account # XXXX XXXX XXXX account # XXXX In accordance to the fair credit reporting act Experian has violated my rights by reporting my accounts as late.
15 USC 1681 Section 602 States I have the Right to privacy.
15 USC 1681 Section 604 A Section 2 : It alsc states a consumer reporting agency can not furnish a account without my written instructions.
15 USC 1666B : A creditor may not treat a payment on a credit card account under Any 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": "XX/XX/XXXX / XXXX XXXX XXXX Made a hard inquiry that brought my score down -4 pts im trying to build my credit here! .I dont understand why theres this hard inquiry And i have not applied for no kind of credit.
I did not Authorize to make this.
XX/XX/XXXX/Capital One Made a hard inquiry without consent of mines Ive tried disputing this multiple times already and still nothing."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Other banking product or service",
"Consumer complaint narrative": "I recieved an email on XX/XX/2021 from Wells Fargo Bank regarding suspected fraudulent activity on my account. There was a pending XXXX transaction of XXXX dollars to a XXXX XXXX in the XXXX. I immediately contacted Wells Fargo to confirm this was fraudulent. I requested that they stop the transaction as it was pending. I was told that the transaction can not be stopped, that after the transaction cleared i would need to file a claim. Wells Fargo is in control of the transaction and knowingly paid the fradulent transaction, which makes no sense at all. They are the bank and should be fully capable of preventing fraud., or at least should be. I began reviewing recent charges and realized that there were numerous XXXX charges all that i did not authorize and were made fradulently.On XXXX XXXX i noticed there was a pending charge of XXXX on my account which I did not authorize this time i did not take no for an answer, I called at least four times before somehow the transaction seemed to disappear as if it had never even happened. My claim of XXXX was covered under the Wells Fargo Zero Liability Protection as specified on their website.
" Wells Fargo Debit and Credit Cards come with Zero Liability protection1 at no extra cost. You wont be held responsible for unauthorized transactions, so long as you report them promptly ''."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX and Experian are guilty of violating my consumer rights and violating F.C.R.A. rules and regulations on several counts. I did file for BK back in XX/XX/2009 which was a chapter XXXX.. However these 2 bureaus are reporting a chapter XXXX and stating they will report it for 10 years ... this is a violation.
I disputed with them only to receive a computer generated statement saying they verified with the provider the information is correct. I called both bureaus who told me to contact court. COURT CONFIRMED IN WRITING THEY DO NO REPORT, VERIFY OR VALIDATE ANYTHING WITH CREDIT REPORTING AGENCIES ( see attached ) so it is LIE!!!! Then I disputed and called again and was told they get this info from XXXX XXXX. I contacted XXXX and received in writing statement that they did NOT get any response from the bureaus and did NOT get any requests for verification and validation ... SO XXXX AND EXP LIED ON ALL DISPUTE RESPONSES SEVERAL TIMES ... THIS IS PUNITABLE BEHAVIOR AND I WILL COMPLAIN TO FTC AND ATTORNEY GENERAL 'S OFFICE IF THESE ARE NOT RESOLVED AND REMOVED"
}
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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "On XX/XX/18 I called XXXX to have my student loans reinstated ( they were paid off during the interest rate pause ). They told they transferred my account to AidVantage.
I call AidVantage on XX/XX/18 and they tell me they don't have my account information.
I call XXXX again and ask for the date of when my account was transferred. They told me they transferred it on XX/XX/18 ( suspicious, but fine ). XXXX is somewhat helpful and at least sends me a letter with all my loan information ( of the loans paid off during the interest rate pause ) so AidVantage knows exactly what to reinstate.
I call AidVantage again and ask when my account will be transferred and they tell me it'll take XXXX weeks... This is absolutely a ridiculous amount of time for someone to be able to simply create an account. I have my letter from XXXX on exactly what needs to be reinstated, but I can't send this to AidVantage without an account. The fact that I can't create an account today with AidVantage is extremely poor business practice and borderline criminal. They are essentially withholding my money.
I have already received my forgiveness email from the US Government ( of course it's on pause right now ), but I need AidVantage to open an account for me & reinstate my loans ASAP. They're making it extremely difficult and every time I call they give me conflicting information. One guy even told me I don't have to have my loans reinstated and that the US government will reimburse me directly ... AidVantage needs to be held accountable and fined for this behavior on a daily basis until they improve this experience."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Received bad information about your loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "I am a home owner, and rented part of my house to a tenant from XX/XX/XXXX to XX/XX/XXXX.
my tenant unable to pay the rent from XX/XX/XXXX to XX/XX/XXXX and because of federal government mandate I didn't evict my tenant. we both file for rent subsidy with broward emergency rental assistance. The tenant left after the lease expired and on XX/XX/XXXX I call the emergency rental assistance and they said my tenant didn't submit requested documents. I try to reach my tenant and unable to reach him. i apply to XXXX. and they said unable to help me.. they ask me to contact a councilor office, and i submit the form, unfortunately no response from them as well.
I just follow the federal government mandate, and now I am in financial trouble.
I would appreciate if your office could guide me whom I should reach out to submit my claim."
}
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": "On XX/XX/2018 I received another alert from XXXX XXXX pulled from TransUnion with derogatory information on a closed account reducing my fico score 30+ points. XXXX XXXX XXXX XXXX. The creditor has been submitting intentional erroneous information in result of each and every dispute damaging my consumer file.
The creditor has been monitoring my credit to the point any time their is a positive change since XX/XX/2017 they will update to pull score down through TransUnion. My TransUnion consumer file was 620+ before disputing erroneous information and fraudulent accounts"
}
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": "Other personal consumer report",
"Consumer complaint narrative": "There are multiple version of my name that is not legal me! I go ONLY by XXXX XXXX XXXX. This is a complete violation and inaccurate reporting and need this fixed asap!"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Since Im a XXXX for research, I found that under section 605b of the FCRA you are required by law to eliminate & block any accounts or information which is found to be opened due to identity theft. The disputed items do not belong to me. Im attaching the required FTC Report for you ( which complies with FCRA Section 603 ( q ) ( 4 ) which states ( 4 ) The term " identity theft report '' has the meaning given that term by rule of the Bureau, and means, at a minimum, a report ( A ) that alleges an identity theft ; ) B ) that is a copy of an official, valid report filed by a consumer with an appropriate Federal, State, or local law enforcement agency, including the United States Postal Inspection Service, or such other government agency deemed appropriate by the Bureau ; and ( C ) the filing of which subjects the person filing the report to criminal penalties relating to the filing of false information if, in fact, the information in the report is false. ) The following report included this language but Equifax is neglecting to process it to the letter of the law. Please see they are held accountable for as this is impacting my life in emotionally, physically & financially... and I would truly not what to pursue a legal remedy... Please have these accounts deleted from my file : XXXX XXXX XXXXXXXX XXXX XXXX XXXXXXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XX/XX/2018 open"
}
Output:
{
"Issue": "Credit monitoring or identity theft protection services",
"Sub-issue": "Problem canceling credit monitoring or identify theft protection service"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "During the start of XXXX Wellsfargo had XXXX payment assistance plan for their customers. I participated in the assistance plan for 1 month, and I decided that I was able to continue to pay my mortgage regularly the next month. I called their customer service reps and I was told that the unpaid amount under the CARES ACT would be just added to the end of my loan. I never signed or received any documents.
On XX/XX/22, I decided to apply with wellsfargo to refinance my home. I provided them with over 60 documents, everyday seemed like a hurdle and they continued to ask for documents that seemed almost impossible to obtain. I continued to provide them with the impossible within a few hours of them requesting documents. In this process, I learned that the 1 month of XXXX relief automatically flagged my account as in " forbearance. '' This was never explained to me and obviously will affect any other mortgage company I try to contact. After almost 2 weeks of underwriting -- they call me to say that " things had changed and how much money I had in my checking account because to make the refinancing work I needed to provide cash '' this was today XX/XX/22. I told the mortage advisor to close my application since it has been a hurdle each time. He said " OK '' and hung up on me. They closed out the application. As I am working with a different bank, they notice that wellsfargo has listed my account to be in " forbearance. '' I asked them for payments made for the last 12 months to prove that I have made them, and documentation that explains I " exited forbearance '' they sent me the document -- it was never signed by me because I never was notified that they were doing this. Please let me know if you can help. This is unfair. It is almost like wellsfargo knew I wouldn't be able to provide the cash since they could see my bank account so I could close out the application. This avoids them from " having to reject a minority. ''"
}
Output:
{
"Issue": "Applying for a mortgage or refinancing an existing mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/2021, XXXX charged my XXXX credit card by {$41.00} automatically for subscription. I dont want this item so I returned it once I receive the item and expected a refund like usual. However, XXXX failed to process the refund. The balance stayed on my XXXX account. I didnt receive any notice from XXXX about this balance that I dont realize at all. I got the late payment reported to the credit bureaus by XXXX.
Once I found out my credit score drop dramatically I realized the amount and paid immediately. And I reached out XXXX they tried again and refunded me on XXXX.
I tried to work with XXXX to revert the miss payment report. But they refused.
There are two reasons that I believe they should help to remove this record : 1. I received no notice of the amount from XXXX 2. The delay of refund from XXXX failed to process in XXXX and I am not responsible for the balance Thank you for helping to investigating the issue!
I am very appreciated!"
}
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": "I do not know",
"Consumer complaint narrative": "I am XXXX XXXX and I'm submitting this complaint myself and there is NO Third Party Involved.
THE BUREAUS INC is violating my consumer rights under 15 U.S. Code Chapter 41 - SUBCHAPTER VDEBT COLLECTION PRACTICES.
THE BUREAUS INC have used abusive, deceptive, and unfair debt collection practices.
THE BUREAUS INC have violated my consumer right to privacy, I NEVER gave them any my information directly and THE BUREAUS INC has committed the crime of IDENTITY THEFT.
I am a consumer and natural person of the United States of America and my consumer rights have been violated and I demand a quick and proper resolution.
I am holding the THE BUREAUS INC civilly liable for their gross negligence. Attached is an affidavit of fact that includes the FDCPA violations and resolution."
}
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": "As a consumer I am highly disappointed in this Bureau! I have sent several letters to them about several accounts on my report that were deleted and re-added for instance account XXXX and XXXX these are the same account that were delated from the original creditor XXXX XXXX because they were disputed. I asked them to verify this account with the original creditor and they couldn't so it was deleted. However it was readded to my credit report under XXXX and another name. As a consumer I have the right to a credit report that is 100 % accurate. What makes matter worse is that the bureaus isn't supplying me any information on the dispute process. I have sent several letters and have gotten no responses from this Bureau. This isn't the only account that this has happened with ; there was another, account Account number XXXX creditor XXXX XXXX XXXX which I disputed this account with the Bureau back XXXX XXXX resulting in, apparently a deletion which I also didn't receive a letter about, this was deleted due to them not being able to verify the account and then a month later was readded under account number XXXX and XXXX. I will be including copies of my credit report from XXXX and my most Recent credit report here in XXXX. However the Bureaus have a responsibility to make sure all information is accurate and correct before entering it into my credit report! This didn't happen. I am writing this complaint because I am expecting them to correct not only these two files but all the others on my credit report!"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Old information reappears or never goes away"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hello my name is XXXX XXXX and writing in for the second time. I would like all of my unauthorized Inquiries, my derogatory remarks, late payments, public records, closed accounts, and student loan accounts removed. Many of these accounts fall under the statute of limitations or are misreporting on my credit report.
Please apply these laws to my accounts : The Fair Debt Collection Practices Act Law XXXX, title XXXX, XXXX Stat. XXXX XXXX XXXX ) FairCredit Reporting Act ( FCRA ) 15 U.S.C. 1681 ( FCRA XXXX Account Name : XXXX XXXX Account Number : XXXX Account was opened : XX/XX/XXXX Account Balance : {$5700.00} Account Name : XXXX XXXX XXXX Account Number : XXXX Account was opened : XX/XX/XXXX Account Balance : {$260.00}"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "HSBC has reported late payments on this account to my credit report WRONGFULLY, for 8 months in a row when I paid off the balance on the card every month!
From XX/XX/XXXX till XX/XX/XXXX HSBC BANK reported lates to my credit report erroneously. I have paid off the balance in full every month, have bank statements to prove but HSBC misapplied these payments thus causing lates reported on my credit report .... I have no derogatory credit, ever! This is the only item damaging my credit history and credit worthiness and it is NOT correct. please help resolve this as HSBC is being ignored for a whole year that I wasn't able to reach resolution with them, going in circles from one representative to another with no luck on correcting this mistake of theirs ..."
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "Problem during payment process"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "The company has placed a debt on my credit report. I have twice verbally requested tht they provide me with validation and verification. I also feel that the seven year statute of limitations for this debt has passed from the date it was incurred. They are engaging in deceptive nd unfair collection prctices by continuing to attempt debt collection fter refusing to validate and verify the debt."
}
Output:
{
"Issue": "Written notification about debt",
"Sub-issue": "Didn't receive notice of right to dispute"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "This is not a duplicate nor is this complaint being filed by a third party, I am filing this complaint myself. Please see this complaint is processed to the letter of the law.
I've talked to the bank & they've agreed to remove the account. BUT TransUnion has some internal block which they're claiming they can use to not block the account because they feel they're above the law. When questioned why they're not accepting the Identiy Theft report they claim they have an internal policy ( which is OUTSIDE FCRA laws ) & they're unwilling to explain why they won't accept the Identiy Theft report ( which is a FEDERAL LAW ). So TransUnion feels they're above the letter of the law & will not explain their policy...
Seems like a lot of passing the buck or no accountability. Whatever the reason, I'm beyond annoyed. The account listed below were opened due to identity theft. I've provided you ( CFPB ) & TRANSUNION with an affidavit & FTC fraudulent report to support my position. It seems TRANSUNION is violating FCRA sections 611, 616 & 617. They have provided XXXX VERIFIABLE PROOF & I feel are willful non complying and being truly negligent. I'd like this account blocked or removed based on 605b. This is my final request before I get my attorney involved because this matter has been creating an insane amount of emotional & physical distress. It's caused me restless nights & has caused me to miss work. My attorney told me I should just sue for violations in FCRA laws. I'd prefer to NOT get litigious. Please see this matter is handled. I'd like the account removed/blocked per 605b with the attached documents again... Thanks XXXX XXXX XXXX XXXX Date Opened : XX/XX/2015 Balance : {$1000.00}"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have reached out to XXXX by online and mail. They have an account from XXXX XXXX XXXX that has been closed for 3 years now. They are updating balances on the account every few months. This account is closed. They are opening up the account then closing it. Ive asked for it to be removed they have refused to do their duty as a credit reporting agency. There is no reason that it has been closed but continue to report"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Other banking product or service",
"Consumer complaint narrative": "I have opened an XXXX account ( XXXX XXXX XXXX ) in Bank of America in the late XX/XX/2022, for the opening account bonus ( XXXX dollars deposit for the XXXX dollars incentive with 90 days maintenance ). To meet the requirements, I used ACH transfer to deposit XXXX dollars. I did not receive the bonus after that long period ( from XXXX to late XXXX ) and I called the stuff for my case. Then they told me that my transfer is not a kind of deposit ( where they think I need to deposit money to the account from ATM ), so I do not meet the requirements at all. I did not receive any notification for it during such long time. I checked the offer carefully, and it only showed " deposit a new money ''. From the definition of deposit : " One kind of deposit involves a transfer of funds to another party for safekeeping. Using this definition, deposit refers to the money an investor transfers into a savings or checking account held at a bank or credit union '', I do believe my ACH transfer belongs to a deposit and meets the " deposit '' requirement. But BOFA stuffs refused to pay me the incentive and told me it is just because the ACH transfer is not a deposit. I think it is too mean for the explanation after I put such amount of money in their bank for a long time. Nowadays, many people have money in different accounts/banks, so it is normal for people to move money from different places with ACH transfer as a deposit method to meet some opening account requirements ( like XXXX bank XXXX XXXX, they account ACH transfer is a kind of deposit when I finish the similar deposit offers ).
I have never seen such harsh conditions. Please help me for this case. Thanks"
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "After months of inactivity, Barclay card sent me a letter on XX/XX/2019 closing my account and imploring me to contact them to inquire on rewards redemption. After being unable to resolve my issue a case was made to resolve my rewards issue and on XX/XX/XXXX a letter was sent out informing me they were unable to assist me. an inquiry call on XX/XX/XXXX informed me that my rewards had been forfeited, my accounts closed, and that i would still receive another card statement on XX/XX/XXXX despite the account closure. yet the XX/XX/XXXX account statement still shows the XXXX $ worth of travel rewards that had yet to be used and no notice of the account closure.
Prior to the closure of this account, no warning or impending notice of an upcoming closure was sent."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Other problem"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "My NFL Extra Points Visa credit card with Comenity Capital Bank features an advertised benefit of 0 % interest of purchases over {$250.00} to pay for XXXX tickets, which is clearly stated in the terms and conditions of the cardmember agreement.
My season tickets with the XXXX XXXX are eligible transactions under those terms and conditions and were charged to my card in 4 equal monthly installments ( @ {$2500.00} each in XXXX, XXXX, XXXX, and XXXX. ) Only the first transaction ( in XXXX ) was placed in the 0 % promotion correctly. The subsequent transactions were treated as normal transactions and have been accruing regular interest.
I reported this issue after receiving my XXXX statement when the 2nd of the 4 transactions ( made on XX/XX/22 ) was not reflected on my statement as being in the promotion. I was told a dispute would be opened and resolved within 90 days. I never received any acknowledgment the dispute was opened. On XX/XX/22 ( beyond the 90 timeframe ) I called again to check the status as I received no communication as to the resolution of the dispute. I was told it was " never worked '' and that a new dispute would need to be opened. I again have received no acknowledgment of a dispute or been contacted about it, to date.
For the last 6 months I have had to manually calculate my true balance owed and pay that amount each month by paying my entire monthly statement balance minus the transactions that should be on the promotion. Each month the interest is continuing to accrue and compound. This has also resulted in Comenity mis-reporting my account status to the Credit Bureaus with a monthly revolving balance when in actuality I have been paying off every transaction made that month in full. This is now negatively affecting my credit score.
Transaction Details : XX/XX/22 - XXXX ( XXXX XXXX XXXX ) [ this transaction was correctly treated as a promotional purchase ] XX/XX/22 - XXXX ( XXXX XXXX XXXX ) [ this transaction was NOT CORRECTLY treated as a promotional purchase ] XX/XX/22 - XXXX ( XXXX XXXX XXXX ) [ this transaction was NOT CORRECTLY treated as a promotional purchase ] XX/XX/22 - XXXX ( XXXX XXXX XXXX ) [ this transaction was NOT CORRECTLY treated as a promotional purchase ] As of today, after my most recent statement closing date of XX/XX/22, I have been incorrectly charged {$1200.00} in interest.
In addition, those same 3 transactions that were not correctly treated as promotional balance transactions also were not given the advertised 3x rewards points that XXXX team purchases are eligible for, as outlined in the terms and conditions of the cardmember agreement. They each received only XXXX in rewards points instead of the correct XXXX they each should receive. That is a difference of XXXX reward points that are due."
}
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": "I found wrong personal information in my annual credit report, they are : one wrong telephone number and 4 wrong addresses under my personal information Tel # XXXX Address 1 : XXXX XXXX XXXX. XXXX XXXX XXXX , MO XXXX Address2 : XXXX XXXX XXXX XXXX , WV XXXX Address 3 : XXXX XXXX XXXX XXXX XXXX , AR XXXX Address 4 : XXXX XXXX XXXX XXXXXXXX , IN XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "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 an alleged fraud or extortion.
XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, GA XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX, GA XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX, GA XXXXXXXX XXXX XXXX XXXXXXXX Balance : {$0.00} XXXX XXXX XXXX XXXX XXXX Balance : {$0.00} XXXX XXXX XXXXXXXX Balance : {$7800.00} XXXX XXXX XXXXXXXXBalance : {$3200.00} XXXX XXXXXXXX Balance : {$4100.00} XXXXXXXX XXXX XXXXXXXX Balance : {$1100.00} ( Original Creditor : XXXX XXXX XXXX XXXX XXXX Balance : {$440.00}"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have XXXX XXXX ACCOUNT / XXXX ACCOUNT closed on my XXXX / XXXX and Experian, I already disputed with the company and they said they would remove it and would contact the credit bureaus, and the credit bureaus have not helped or removed it, even though XXXX / XXXX / EXPERIAN agreed they would remove within 48 hours because they were unverifiable. it has been months now and it hasn't been removed and it is affecting my credit score and way of living."
}
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": "Equifax letter dated XX/XX/2022 confirmation # XXXX. Results - XXXXXXXX XXXX account was updated to - PAID IN FULL, account balance {$0.00} and an update freeze was placed on the account. This was done by Equifax per my dispute and CFPB complaint XXXX regarding XXXXXXXX XXXX issuance of a XXXX which cancelled and discharged the debt.
Just 5 days later, XX/XX/2022 Equifax reinserted a balance of {$22000.00}.
XXXX account accurate Balance is {$0.00} with reporting date of XXXX per the XXXX issued. Equifax knows the accurate balance is {$0.00} WHICH IS WHY EQUIFAX UPDATED THE BALANCE TO {$0.00} ON XXXX XXXX. EQUIFAX SHOULD NOT HAVE REINSERTED A BALANCE JUST 5 DAYS LATER.
Equifax dispute results letter dated XX/XX/2022 with pages XXXX, XXXX and XXXX are attached to this complaint for proof of letter received from Equifax. There are yellow highlights on the pages noting Equifax results of {$0.00} balance and paid in full. I am also attaching another copy of the XXXX.
This seems to be a FRCA violation. Permanently delete this account due to the inability to freeze the updating of a balance on this account. The accurate balance is {$0.00}. The updating from {$0.00} to {$22000.00} then back to {$0.00} then back to {$22000.00} can not keep happening."
}
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": "I do not know",
"Consumer complaint narrative": "Hello I recently received an alert from the credit bureaus and notice a collection account on my credit reports. I have no knowledge of any collection or ever had an account with CACI owing {$2200.00}. I have contact the creditor, serval times and has not heard back from them. Also, I have reached out to all three credit bureaus requesting verification of this collection account ; to send me verification that this account belongs to me and any documents that is bearing my signature. As of today ( XX/XX/2021 ), I have not heard anything from them. I have maintain my credit in good standards for years. It issue is affecting my credit and I need someone to help me correct this problem."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "i have sent multiple letters to experian to remove inaccurate addressed and accounts from my credit report and each time they refuse and make it extremely difficult to speak to anyone or address the matter. they stated i had more then one credit report which made no sense and that some of my information was split. after they merged reports i noticed information both personal and accounts that were not mine and they refuse to remove the false accounts let alone match my information to these accounts to make sure they are 100 % accurate. iv stated to them if its not 100 % they can not report it and have spoken to and written to them to remove such accounts as it violates the FCRA. they even want as far to put my name wrong on my report even after sending them my license, social card and birth certificate."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Information is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Equifax is taking too long to conduct my investigation and respond. I requested an investigation XX/XX/XXXX, but they have not yet responded to me with the results."
}
Output:
{
"Issue": "Credit reporting company's investigation",
"Sub-issue": "Investigation took too long"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "XX/XX/XXXX my mortgage was involuntarily sold to Caliber Home Loans.I find Calibers monthly statements to be confusing, and phone support to be virtually non-existent. XX/XX/XXXX, Caliber made statements on phone that were not reflected on following statements.They finally stopped taking my calls.As a result, I filed a formal complaint with XXXX on XX/XX/XXXX XXXX attached ). After several tries, XXXX got the issues resolved. However, at no time, did Caliber communicate with me, nor offer an apology. A year later, XX/XX/XXXX, basically the same issues happened again XXXX perhaps retaliation ), resulting in a just submitted second complaint to XXXX XXXX attached XXXX.
Unfortunately, the XXXX has also began ignoring my calls, necessitating this complaint to the CFPB. Can you help me with my current complaint, as well as continuing problems with this horrible mortgage company I am stuck with, whose statements are always confusing, sometimes inaccurate, and whose customer service is non-existent and getting worse!"
}
Output:
{
"Issue": "Loan servicing, payments, escrow account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have sent XXXX letters to remove personal information from my credit report to Experian.
XX/XX/XXXX tracking ID XXXX XX/XX/XXXX tracking ID XXXX According to the fair credit reporting act 15 UCS 1681. I have the right to privacy. Also according to 15 USC 6802 No financial institution is not supposed to share my information to a 3rd party people These are a clear violation of my federally protected rights."
}
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": "On XX/XX/2018 XXXX and Equifax has my credit score dropping over 38 points for transunion and 48 points from Equifax There is no reason for these scores to drop that much within a short period of time Both companies can't give me a reason as to why. My on time payments is excellent my credit card usage is within the guidelines. I have had credit checks done because I bought and new car and I am in the process of buying a new home. I went on the XXXX web site and they wanted me to sign up for a XXXX dollar a month monitoring plan. Now this company drops my credit score by 38 points for no apparent reason and then they want me to sign up for their credit monitoring plan please look in to this there is no reason why my credit scores drop that much. My credit score has always been in the mid 800 's now they dropped me from excellent to good and they can ; t explain it. These people have the sole power to impact your buying power XXXX Good XXXX XXXX XXXX XXXX XXXX XXXX XXXX Excellent EQUIFAX Updated XX/XX/2018 Calculated using XXXX 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 reached out to Truist XXXX XXXX numerous times and nothing has changed regarding the negative account on my credit. I filed a police report stating that my identity had been being used to secure accounts that I had no knowledge of with Truist being one of them however the company is still reporting a charge off and bad debt. Each credit bureau is reporting a different amount and different history. I have never bought a car with this company ever."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "According to XXXX XXXX, there is an open collection account from First Credit Services on my credit report. I XXXX the number and called First Credit Services to find out more information about this {$92.00} debt. I spoke to XXXX XXXX on XX/XX/21 at XXXX and provided her with my date of birth, social security number, and address but I was told there is nothing in the system for me. I listened on the phone while XXXX XXXX called her boss over to look at the computer and figure out where my account was. They asked who the original contractor was and I provided them with the information shown on XXXX XXXX. They said " We don't see anything for you. We can't find it. We no longer have this account. We have exhausted all efforts to collect on this and we no longer have the account. '' If this is the case, why is this collection agency on my credit report in the first place?
Please remove it immediately from all reports."
}
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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "I was summoned to court on XX/XX/XXXX for an unpaid balance owed to Mariner Finance. XXXX XXXX had contacted me on XX/XX/XXXX at XXXX to see if I wanted to make payment arrangements after they had already filed court papers against me. I advised him that I was going to make a payment on XX/XX/XXXX and that was not enough to satisfy my debt. I had called in to speak to XXXX XXXX on XX/XX/XXXX wanting to make payment arrangements for today and on XX/XX/XXXX in the amount of XXXX but he quickly advised me that was n't acceptable. The conversation got heated due to not coming to an agreement on a suitable payment arrangement and XXXX XXXX continued to over talk me. I felt very intimidated when he advised me that I owe them money and I need to pay it. He continued to over talk me and he advised me that I do n't need to be rude to him nor have any kickback. I advised him about my situation and the reason I was past due. I was in a near XXXX XXXX XXXX on XX/XX/XXXX in which I almost got XXXX in a XXXX XXXX XXXX due to the other party XXXX XXXX XXXX the XXXX and nearly XXXX me, his wife and child and maybe himself. I advised XXXX XXXX when I got my settlement back that I would pay this debt off and he kept asking me when I was getting it. I advised him that I was n't for sure. I understand XXXX XXXX has a job to do but that 's not the way to do it. I understand I owe my debt and I own up to my debt that is owed but that 's not the correct way to treat a consumer if they are at least trying to pay. People run into hard times but it seems as if XXXX XXXX is only wanting a paycheck and not wanting to help the consumer."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Used obscene, profane, or other abusive language"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX XXXX XXXX XXXX XXXX, NY XXXX ( XXXX ) XXXX XXXX SS # XXXX Transunion Re : Us Bkpt Court NY Bankruptcy chapter XXXX To Whom It May Concern : I have previously submitted a certified letter which was received and signed for on XX/XX/XXXX2016 requesting that a dispute be initiated for a public record Bankruptcy chapter XXXX which is reporting information on my Credit Report."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "i hate to use this to write you. its not really a complaint i cant get thru by phone or by letter no response. Also your phone system needs improvement i have lost much time. sometimes it recognizes my card last 4 digits sometimes no it takes a lot of my time.
card eding XXXX i am out of work. please help me extend the 5 percent interest thru XXXX.also as a courtesy for my 30 year relationship if there is interest which i know is valid please credit it please dont throw it in my face i am a good customer vs those who dont pay at all who get all their fees waived. please credit it for my loyalty, longevity and all the time i lose when i call with that phone system this is for my At and T univ card. pelase dont put notes on my account either.
i am out of work. also system says they mailed me a card i have no new card.please help with these 3 items without making me feel bad about asking.
please XXXX XXXX a card not mail. no fees please."
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I did my tax return on XXXX and was approved for a {$250.00} loan basically where they would give it to me and then take it out of my tax return when it came but I had to do it through Credit Karma Credit Karma locked my account told me that Im not approved for it after They had already said I was and now my money is in the account and they wont give it to me. They will not email me back when I call they kept repeating the same thing saying we can not comment on this. You have to talk to our special investigations through email and they never emailed me back. I asked to have the money transferred into my bank or to mail me a check and they are refusing I told them I need to pay my rent with them my money and they wont give it to me and wont pay late fees due to the fact that its their fault they wont give me my money."
}
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": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I had two blue cards in my wallet. Used wrong card today thinking it was a debit card. Instead, it was XXXX 's XXXX credit card issued by XXXX XXXX. When I placed the card in self check out at Walmart, I asked for a {$40.00} cash back, thinking it was my debit card with XXXX. Instead, it kicked out {$100.00} cash to me and paid for the small grocery item.
When contacting XXXX XXXX today asking to reverse the pending charge on my credit card, by paying the {$100.00} back and manually replacing the mistaken withdrawn cash with an instant payback. They refused to do so and said the only way to pay any cash withdrawal was to first pay off the entire balance. Thus, this {$100.00} cash withdrawal will charge an additional 3 % interest until paid, based on their refusing to manually accept the mistaken cash back within the same hour the transaction took place. I then immediately closed my account with them.
This isn't my first problem with them. They mistakenly opened multiple accounts and closed accounts in my name over the years, failed to report actual payoff balances after numerous complaints that wrongly showed a balance over my credit limit which has never happened in the life of my account with them.
Their phone systems are also flawed. Both verbal and type in account information was never recognized. Their online chat also was worthless and misleading. No live person was there and she was in XXXX most likely not understanding a word if she was live.
I have had enough. I also closed my other account with XXXX XXXX. The abuse by this company has been long and against credit card/consumer laws. A full investigation of their flawed systems should be done immediately. I have lots of debit and credit cards. None abuse me or have abused me like XXXX XXXX. Perhaps the last part of their name tells it all ... .chroney ...."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Problem with cash advances"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account 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": "In XX/XX/XXXX, I opened a new credit card account with TD. The physical card for that account expired in XX/XX/XXXX. When I did not receive a replacement card by XXXX, I called TD to request a replacement card and was told that my card would not be reissued because the account had been inactive for 18 months at the time of card expiration. My account has remained open ( and reported as open to the credit bureaus ), and I have continued to be charged interest and to make payments. I requested a replacement card several times between XXXX and XXXX and was told nothing could be done. In XX/XX/XXXX, I again requested a replacement card for this account and TD continued to state that my card was denied re-issuance in XX/XX/XXXX because it had not been used for the previous 18 months, and stated they could not issue me a new card at that point ( XX/XX/XXXX ) because the account had now been inactive for over 60 months. When I pointed out that I 'd used my card at least as late as XXXX XXXX, TD then agreed that I had actually used my card as late as XXXX XXXX, only 6 months before XXXX XXXX. Then TD stated ( for the first time ever, despite my numerous written and oral communications with various TD Bank employees re : this issue between XXXX ), claimed that re-issuance was declined due to late payments. TD further stated that, " Regardless of the reason your credit card was not reissued in XX/XX/XXXX, we are not able to reinstate your account now. The law requires we assess your ability to repay before any credit card is issued. If you wish to have an active TD Bank credit card you would need to close this account ( you are require to continue making payments as indicated on your statement until the account is paid in full ) and reapply for a new credit card ( this will involve obtaining an updated credit report and determining if you currently meet our credit criteria ). '' However, my credit card agreement provides that TD may re-assess or review my credit history at any time, and I also informed TD that they could check my credit and I would give them any additional information needed to do so. Further, TD increased the credit limit on this account from {$5000.00} to {$5500.00} sometime between XXXX. This indicates that TD has the ability to review my credit without the need for me to apply for a whole new and separate credit card account. In addition to repeatedly providing me incorrect information re : transactions on my account, reason for declining to re-issue my card, and seemingly on the law related this this issue, during my interactions with TD in XXXX, I was referred to an individual named XXXX. During my conversation with XXXX after being asked specifically if I would like TD to close this account, I requested that the account not be closed. I also made this request to TD via writing. When I saw that my account was suddenly being reported as " account closed at consumer 's request '' in XXXX, I contacted TD to inquire and was told that my account was closed on XX/XX/XXXX based on a letter sent by XXXX XXXX XXXX on XX/XX/XXXX ( the same date I spoke with XXXX XXXX XXXX XXXX. I was also told that TD records show that the account was closed based on my request. After numerous requests via email and phone, my account was reopened within a few weeks, however, I was informed by TD that they would still never re-issue the card on my open, active and current account. Based on the foregoing, as well as other details not discussed above, I believe TD 's actions in refusing to re-issue my card ( initially and after subsequent requests ), and in reporting my account as closed in XXXX, were not based on legitimate reasons, and were instead due to either negligent mistakes and/or personal animus due to unpleasant interactions with me re : previous issues I had with the account."
}
Output:
{
"Issue": "Closing/Cancelling account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "My wife passed away on XXXX XX/XX/2021 on or about XXXX XX/XX/2021 I notified Lowes/Synchrony customer service number XXXX and was connected to Synchrony bank " OFF-SHORE '' call center that my wife had passed away as this was a joint account they did not ask for a copy of her death certificate but said they would verify independently through other means, On XXXX XX/XX/2021 I entered Lowes store in XXXX AZ and attempted to purchase some items I needed for some home repairs and my Lowes credit card was " DECLINED '', I went to the customer service desk and was told when they checked the account that it was closed with no mention of any thing else.
I called Lowes/Synchrony customer service and requested to talk with a manager and then they finally transferred my call to another person in their " Probate '' center who told me yes they reported my wife 's passing and in addition they had reported my death also to all three credit reporting agencies without any evidence that what they did was in fact true. I contacted XXXX and XXXX and filed a dispute with both of them and was told that this could take up to 45 to 60 days to get corrected. My wife 's death certificate has not been issued by the XXXX county coroner as of XXXX today XX/XX/2021 also my wife 's obituary was not published in the XXXX XXXX XXXX weekly paper until XXXX XX/XX/2021. This will seriously impact on my ability to finnish all my wife 's final arrangement if I am listed as deceased by all three credit reporting agencies that have not conducted their due diligence and published totally false information on my credit report and want to drag their feet in correcting this. All the other creditors that my wife and I have asked for a copy of her death certificate or the name of the funeral home that was taking care of my wife 's funeral. I am possibly considering retaining an attorney to take them to task for their errors."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose prepaid card",
"Consumer complaint narrative": "I received a Vanilla Gift Card for {$300.00}. I did not use the card until on vacation in XXXX. I used very little of the balance and still have the card in my possession.
In XXXX, I checked the balance of the card to determine the remaining balance prior to using it again.
I found that 3 unauthorized transactions occurred on XX/XX/XXXX. All three were paypal transactions.
I called the 800 # on the back of the card and notified the company of the unauthorized transactions. They emailed me a claim form on XX/XX/XXXX, which I completed and returned by mail.
I called around 30 days later and they claimed to have not received my forms. I received another email and returned via email on XX/XX/XXXX
Vanilla Card declined my claim stating it was outside the 60 days allowed. As a gift card, I do not receive periodic statements alerting me to activity on the account.
I responded several times since then, quoting their own terms and conditions, which state that if a claim is filed outside of the 60 days, they can deny the claim if I was negligent.
Since I am in possession of the Card. I am not negligent. I did not authorize or perform those transactions.
Vanilla Card is bound by not only their own terms and conditions, but VISA 's Zero Liability rule.
I would like your assistance in obtaining the refund due to me on the claim.
Thank you."
}
Output:
{
"Issue": "Problem with a purchase or transfer",
"Sub-issue": "Card company isn't resolving a dispute about a purchase or transfer"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account 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 # 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."
}
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": "When I reviewed my credit report, I discovered that some of the information was erroneous. The following account need to investigate and correct for erroneous reporting in my file and are listed on my credit report The 3 credit bureaus must validate this account : XXXX XXXX XXXX : {$0.00}"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "The mortgage agent XXXX XXXX was helping me to get my home loan to refinance since XX/XX/XXXX. I listed her personal information as list below : Professional Information Broker Address : XXXX XXXX XXXX.
XXXX XXXX XXXX.
XXXX, CA XXXX Cell phone : ( XXXX ) XXXX Websites : Website Screenname : XXXX Member since : XX/XX/XXXX Real Estate Licenses : XXXX ( CA ) Other Licenses : XXXX ( Real estate and mortgage broker ) Languages : English, XXXX In the beginning, XXXX XXXX said she can get no point no fee for my home loan for XXXX with 2.75 % 30 years fixed. My loan process was almost closed and she said I can sign my paperwork on XX/XX/XXXX or XXXX, XXXX. However, on XX/XX/XXXX, she called me and asked me to pay her {$500.00} dollars cash for over 30 days loan process. If I don't pay for her she will have to terminate my loan process with Quiken Loans, LLC ( XXXX XXXX XXXX XXXX, XXXX, MI XXXX and my loan number XXXX ). If I don't pay her {$500.00} now, I have to wait for another 30 days later and restart my loan application again. I am not happy with that since she already took me over a month. I also gave her all my personal information and turn out to have to wait for another two months. She knew my current interest rate is 3.875 % which I will have to pay {$330.00} dollars more for each month. If I give her {$500.00} dollars, she can help me to close the case immediately. I search online that never and ever pay for the mortgage broker money because the lender will give her money when the loan closed. In order to avoid paying her, I have to restart the loan application with another new broker. My phone number is XXXX and my email is XXXX. If you have more questions welcome to call me."
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On ( XX/XX/2023 ) I sent a letter regarding inaccurate Late payments, inaccurate negative accounts, bankruptcies and unknown things on my credit report. To this day over XXXX days later I have not received a response yet. feel like i'm being taken advantage of and being ignored of my disputes. Section 611 ( a ) it is plainly stated that failure to investigate these items within 30 days gives a reason to immediately remove those negative items from my credit report as well as correct my late payments as paid on time. It has been over 60 days so they should be corrected promptly. I demand these negative accounts be deleted & correct late payments as paid on time immediately or I will file for litigation due to the stress you caused me. My information was also impacted by the XXXX data breach and may have got into the hands of the wrong person."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Investigation took more than 30 days"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "BB & T Servicing is not sending any mortgage statements. Their online portal also does not contain any mortgage statements. I emailed them through their portal to demand that they issue me my mortgage statements and they have not done so."
}
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": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "My credit report was involuntarily frozen by XXXX due to its security breach. I applied for a loan today with Ally Bank through XXXX XXXX, etc. in XXXX, AL ; however, Ally Bank wants to charge me 15 % interest with a XXXX credit score. Two years ago I was granted a 12 % interest auto loan with a credit score of around XXXX with XXXX XXXX XXXX. I was told by the finance manager of XXXX that the reason Ally Bank charged this high interest rate is that my credit report had to be unlocked and that some finance companies penalizes consumers because of this. This is totally unfair to consumers. I did not place this freeze. XXXX had to place this freeze due to a security breach. What is going on? Most auto interest rates in today 's market starts at or about 3.0 with an average credit score of 640-699."
}
Output:
{
"Issue": "Getting a loan or lease",
"Sub-issue": "Problem with signing the paperwork"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "Someone from XXXX and XXXX law firm text me me about my debt with speedy cash and this what they told me : Hello, this is the XXXX and XXXX LAW FIRM. This message is solely for, XXXX XXXX.
We are representing XXXX XXXX. Founded in 1997, XXXX XXXX XXXX XXXX is the country 's leading provider of non-bank financial services, with approximately 2,100 centers across the country. The company is the Mother Lending company for companies like XXXX XXXX XXXX, XXXX XXXX XXXX, Speedy Cash, XXXX, XXXX, XXXX XXXX, XXXX XXXX and many more. The Company is a founding member of the XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, whose mission is to promote laws that provide substantive consumer protections and to encourage responsible industry practices. XXXX XXXX is a wholly owned subsidiary of XXXX XXXX, one of the largest XXXX American providers of banking services, consumer finance and specialty retailing.
The loan you have taken out became delinquent and the company has full rights to press charges on you.
Details are below.
Total Outstanding Balance ( Due Amount ) : {$1100.00} ( Including Late Fee, Penalty, Affidavit Charges and Rate of Interest Charges. ) We have more evidence such as the Bank Account, Account & Routing Number, References ( With Phone Numbers ), Drivers License, Employer Name & Address, IP Address and many more.
One Time Settlement Offer : 1 : {$400.00} XXXX Has to be paid Today XXXX.
2 : {$650.00} ( {$200.00} Today and {$450.00} Paid Within This Week ) 3 : {$200.00} ( Bi-weekly starting from today until paid in full ) Or the Entire balance of {$1100.00} within the next 30 days.
Consequences ( Further Legal Steps ) : 1 : Lawsuit Filed Under Several Sections 2 : Your Employer Will Be Informed.
3 : Credit Bureaus Will Be Notified So That It Could Be Reported to Your Credit.
4 : References Will Be Informed.
5 : A Day in Court.
6 : Garnishment of Your Paycheck.
Let us know whether you opt for a one-time settlement or payment arrangement for the whole amount, so that the account can be updated.
Since this loan account is very past due, we would appreciate if you give this matter your earliest attention, as this account has to be updated on an urgent basis to avoid legal action.
( Please not the legal affidavits have already been generated on your file, for filling it with the court house tomorrow first thing morning. As this is an urgent matter and needs your attention to it right away. You have time till close of business today ).
Please contact the petitioners office. It is your legal right to do so. That number is XXXX XXXX XXXX XXXX. Thank you. That day they told me to get a XXXX XXXX gift card for XXXX which I did and they want me to send a payment of XXXX a month from not to XXXX of next year for the total amount XXXX I called the website where I got the loan from and they said that it might be a fraud and for me not to send money but I still have the text where they message me and the picture of the receipt that they sent me."
}
Output:
{
"Issue": "Took or threatened to take negative or legal action",
"Sub-issue": "Threatened to arrest you or take you to jail if you do not pay"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "On XX/XX/XXXX, I received a call and a voicemail from a woman claiming to have issues with a Bank of America prepaid card in my name. Now, I bank with Bank of America, so immediately was concerned about the status of my account or if there was a problem. I called the number back and it was now a man asking for my first and last name, which at first I had no problem giving. However, then he asked for my social security number, which I thought was a part of the process so I gave it. He asked for my social again, and after hearing the shakiness in his voice, I realised that it was a scam. I am currently worried that he might've heard enough of my information to do something with it, and I was writing to see if there was any way of checking this or filing a bigger report."
}
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": "XX/XX/2021 Certified Letter Sent to Bureau Proof of Identity Theft and Demand for Removal of Bankruptcy XX/XX/2021 Requested Response Letter Sent to Bureau Follow Up on Demand for Removal XX/XX/2021 Affidavit Final Follow Up and Response Requested and Demand for Removal XX/XX/2021 Legal Action Notification and Demand for Response My name is XXXX XXXX. I am speaking on behalf of myself and no one is speaking for me. I have contacted via certified mail Equifax, XXXX, and XXXX on 5 separate occasions with no response or removal. This is a direct violation of 15 USC 1692 ( g ) Validation of Debts, FCRA 605B, 615 ( f ), and 623 ( a ) ( 6"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "a mortgage company called XXXX gave me a loan for an investment property. From XX/XX/XXXX until XX/XX/XXXX XXXX ( XXXX XXXX XXXX ) was paying escrow taxes to the wrong lot number for the condo I own. After 3 years I was notified by the town the condo is located in, that they put a lien on the property for non-payment of property tax. I called XXXX, they admitted fault and paid all of {$4000.00} owed to the town taxing authorities. XXXX then started to charge me thru additional mortgage charges to get the tax money they paid to the town. XXXX said that they would charge me until all of the {$4000.00} was paid to them and if I did not pay, it would affect my credit and they would repo the property. I complained and called them once a week. XXXX sold the loan to FLAGSTAR bank. I explained the problem with XXXX to the new holder of the loan FLAGSTAR, but they just kept charging me extra on my payment until my escrow is down to negative {$1200.00} Both XXXX and now FLAGSTAR owe me {$2800.00} that I was charged by extra payment on my loan by raising my mortgage payment. I am being robbed and threatened that if I don't pay, this bank will ruin my credit and foreclose on the property. I have called both banks NUMEROUS times and they say they will investigate and nothing happens."
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Other type of mortgage",
"Consumer complaint narrative": "I am not clear on the details of all transactions. I have been trying to reconstruct the timeline and have made calls to the companies numerous times inquiring about the changes to my mortgage, but they would not allow me to obtain them or outright lie to me. Someone refinances my house many times an it is not in foreclosure. Rather sold to my brother for a quarter of its worth with my signature giving him the gift of equity and purportedly giving me a right of refusal in two years.
Prior to this I paid all payments to XXXX XXXX, but apparently there were 2 separate mortgages on my house from two separate banks. Im still unclear exactly what occurred, but there were multiple transactions and I was unaware of them, nor did I benefit from them in any way.
XXXX, an mortgage co I only learned of around XX/XX/XXXX appears to have had a mortgage on my house since XX/XX/XXXX along with XXXX XXXX.
Payments appear to have been made to both banks, but Im unclear on how or what account they came out of.
Whoever refinanced appears to have stop making payments or a time period elapsed Around XX/XX/XXXX and foreclosure proceedings were sent to me for different dates, times and places.
In response, I used Keep Your Home Ca to save my house when it was in foreclosure and my mother as POA helped me with all the details. Im learning now that both XXXX, Wells Fargo, XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX, the mortgage broker and possibly someone very close to me all played a part in obtaining all such mortgage transactions as they appear to all be done online and use the same signature page of A GFE I initiated myself in XX/XX/XXXX.
I have documentation of inquiries and now subsequent to the sale to my brother Im left with nothing. Not even the right of 1st refusal which would have allowed me to either repurchase my home of 17 years for what I sold it to him for, or it be sold and equity split between us fairly according to my years in the house, my gift being repaid and market value according to its unique value.
Ive been given nothing, rendered homeless and have no recourse."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "RE : Report # XXXX I 'm complaining because Transunion have failed to comply with, furthermore wilfully ignored, my request to provide me with the documents that their company have on file that was used to verify the account that I disputed in writing back in XXXX. On XXXX XXXX of this year, I sent them a dispute letter requesting that they verify all data for the disputed account and 40+ days later I have yet to receive anything of the sort. Because it is well past the 30 days permitted by law, and they are unable to verify the account, the Fair Credit Reporting Act Section 611 ( 5 ) ( A ) explicitly states that they are required by federal law to " ... promptly delete all information which can not be verified. '' namely the accounts in my dispute. I ask that you resolve this matter with urgency as my character is being defamed by their wilful non-compliance with the FCRA. Thank you."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I have a credit card account with Navy Federal, I open this account XX/XX/XXXX. I opened the account because i was referred by a friend. When I tried to sign into my account to make my payment, they told me that I didnt have access to my account because I was not apart of XXXX. I let them know that I was referred, they told me that, since I was not directly in XXXX the locked the account. I told them my credit card was still working, and I just wanted to have access to my account so that I could make a payment and see whats going on with the account, charges and all that. I did not want to make payments blindly, not knowing the charges on my account, or if fraud or anything was going on and I didnt know. They would not give me access. I have called several times asking for access to my account so that I can know the charges on my account and to make payments. They have not given me access. I do not think its right to be restricted out of my account and expected to make payments, not know what Im paying for and the only way i can make a payment is calling customer service. My account is still open with Navy Federal, but they have locked me out, but still charging me late fees and etc, but not allowing me see whats going on with account. this negatively effecting my credit now. My account is now 90 past due, again i want to pay what I owe, but I would like to have access to my account to do so."
}
Output:
{
"Issue": "Billing disputes",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Discover is running a promotion where they are giving 10 % cashback for all purchases made via Apple Pay in store until XXXX/XXXX/15. I have made numerous purchases using Apple Pay but discover has not honored many of the purchases because they added a clause several days into the promotion that says " gift cards are excluded. " To enforce this clause, they are now blanket rejecting many purchases above a certain amount and requiring customers to provide receipts to show whether the purchase is or is not a gift card purchase. No where in their terms of service does it state that customers need to retain receipts and then send them to Discover.
This is a blatant attack on consumer privacy and has shifted the burden of proof to the consumer when it is the credit card company that should be the one verifying purchases.
If Discover wants to exclude gift cards, they need to do it accurately and without requiring customer receipts to be sent to Discover."
}
Output:
{
"Issue": "Rewards",
"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 attempted to correct the first date of delinquency with all of the credit reporting agencies, including XXXX XXXX and have provided the corroborating documentation as evidence.
I spoke with XXXX XXXX and requested the first date of delinquency and payment history because the credit reporting agencies are reporting that the first date of delinquency is reporting correctly.
I spoke with an Equifax representative and had him review the date of first delinquency as reported on the current report and he observed the date reported in the payment history but also observed a different date in the section that would have it removed in the 7 year history for removal. The representative informed me that it appears that the system is picking up the latest date of first delinquency not the actual date of first delinquency.
I am providing a copy of my Equifax credit report dated XX/XX/XXXX showing the date of first delinquency for the XXXX XXXX Home Mortgage Acct : XXXX as XX/XX/XXXX and the XXXX-month payment history which shows XX/XX/XXXX as 30 days late.
I am providing a copy of my Equifax credit report dated XX/XX/XXXX showing the date of first delinquency for the XXXX XXXX Home Equity Acct : XXXX as XX/XX/XXXX and the XXXX payment history which shows XX/XX/XXXX as 30 days late.
Since I have filed my complaint with XXXX XXXX, the representative and the company provided me with the Discharge of Mortgage ( see copy ) on XX/XX/XXXX which was submitted on XX/XX/XXXX.
I believe that the computer programs that Equifax, XXXX and XXXX utilize is only picking up the date of the latest first delinquency not the actual date of first delinquency. As you can see, I attempted to save this property and made payments which is why all of these dates start over.
I have spoken with representatives from Equifax, XXXX and XXXX in an attempt to have this problem corrected and properly reported so that these negative accounts will be removed in accordance with the 7 year timeframe."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I had a car loan with XXXX XXXX that was paid off in XXXX 2012. I had a very good professional relationship with the company and a excellent payment history. I recently checked my credit reports online and I was very surprised to see the credit bureaus are reporting this account with a lot of negative history."
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I filed a complaint against this company for harassment of my daughter on her cell phone and the company used that opportunity and the CFPB response system inn order to attempt to verify this debt instead of using proper practices to do so. Using the complaint system as a way to verify possible debts with consumers."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "You told them to stop contacting you, but they keep trying"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account 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": "IDENTITY THEFT NOT RESOLVED Source XXXX Alert Date XX/XX/2018 Balance Date XX/XX/2018 Balance Amount {$54000.00} Open Date XX/XX/2018 Status Date XX/XX/2018 Name XXXX XXXX XXXX XXXX XXXX Phone Street Address XXXX XXXX CREDIT BUREAU DISPUTES City XXXX XXXX NC Zip XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX reported to the consumers reporting agencies for an unpaid account three years ago and that account is now paid in the full amount.
XXXX XXXX credit company, XXXX, and Transunion credit reporting agencies are refused to remove/delete the fully paid & closed account from my credit report history.
Paid Date : XX/XX/2021 Amount Paid : {$69.00} Payment Confirmation Number : XXXX Paid Date : XX/XX/2021 Amount Paid : {$99.00} Payment Confirmation Number : XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "On XX/XX/2022 at XXXX XXXX I received a email from a debt collector Amer Assist on behalf of XXXX XXXX XXXX saying that I owed a debt of {$120.00} and it was sent to collections. This is not accurate. I would like someone to please look into the false claim of a debt and harassing emails I am receiving from Amer Assist at work. It is not fair to be harassed by this company for a false debt. It is a violation of the fair credit reporting act to to report an inaccurate statement of balance due. As per FCRA Violations when an agency, business or individual fails to exercise proper care or takes action that a reasonable person would not with regard to your credit information, that is negligent behavior as XXXX XXXX XXXX XXXX has done. I would like XXXX XXXX to correct this false debt allegation and have Amer Assit stop harassing me via email and phone calls."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The list of accounts below has violated my federally protected consumer rights to privacy and confidentiality 15 USC 1681.
DEPT OF EDXXXX XXXX # XXXX, U S DEPT OF EDXXXX XXXX XXXX XXXX XXXX XXXX DEPT OF ED/XXXX XXXX XXXX XXXX DPT EDXXXX XXXX XXXX, XXXX XXXX # XXXX XXXX & XXXX # XXXX, Has violated my rights.
15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than seven years.
15 U.S.C 1681s-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or has reasonable cause to believe that the information is inaccurate."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I was just recently notified by my credit bureau that my score dropped over XXXX points. I immediately went online to check it out to find that CapitalOne was reporting me over 30 days delinquent. I knew I had not used the card so I immediately thought fraud. However, after calling CapitalOne, I was informed it was from the annual credit card fee and by now received XXXX late fees. I did not receive any email notification or any statement in the mail. I checked everywhere. CapitalOne agreed to waive the late fees and update my credit bureau. However, they are unable to expedite the request and force report to the credit bureaus. I am in the process of purchasing a mortgage and as a result I will be charged a higher interest rate, my mortgage payment will be higher, and delay the process of purchasing our home. This is XX/XX/2017 and a credit card company can not expedite a credit bureau update per their error? They stated I would have to wait till the account cycles. That is three weeks from now. This needs to be done now!"
}
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": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "I have contacted with no results by phone and letter."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Equifax is reporting collection activity on an account that is past its statue of limitations and is time barred from credit reporting. This is a violation of my rights under the FCRA. XXXX and XXXX XXXX both have already removed this account. Equifax needs to remove this account from my credit file immediately."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card",
"Consumer complaint narrative": "This company XXXX XXXX XXXX called and left a automated voicemail on my machine they say someone has filed a complaint against me call to find out who it is. I call they say Bank of America is going to sue me. And I have to pay this debt. I say I have not had a bank of america card in 30 years. They say they will subpeona me and have a nice day. Please look into this. They call from XXXX number XXXX and XXXX both call back numbers left the same reference number. The scary thing is they have my social security number and current employer. You have to do something to stop these crooks. They violated all kinds of FDCPA"
}
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": "These accounts don't belong to me. Please remove them form all XXXX credit reporting agencies. please see below the list of accounts that are reporting derogatory on all XXXX credit reporting agencies : 1. Identity Theft CBNA/BBY Date of inquiry : XX/XX/2019 This is not mine.
2. Identity Theft XXXX XXXX Account Number : XXXX This is not mine.
3. Identity Theft CITICARDS CBNA Account Number : XXXX This is not mine."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Have sent a list of deputies to Experian, over 18 past and present dating from XXXX I did not authorize them and they failed to provide accurate information, and a removal of XXXX XXXXXXXX truck status is not accurate.
No action was taken by company and has impacted my financial stable ability to get a home for my family. I am seeking compensation."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I have been bombarded with phone calls for someone I don't know. Numerous times per day EVERY DAY!! I have told them several times I don't know the person they are looking for and to stop calling my number. Only to have them say ok and call a few hours later from a different number. The name of the company still shows up but the number is different. I am seeking a work from home job and these continuous phone calls all day impedes on my ability to provide for my children."
}
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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "Joint Consolidation Loan Act ( XXXX ) was passed 71 days ago, which means there is a law put in place to separate Joint Spousal Consolidation Loans. However, there is no plan to date to implement this separation. I have never been able to receive a pause in payment even now that I have a law passed to support me and my family. There is no equity in loan holders when my interest just grows on an administrative forbearance taken in XX/XX/XXXX and other loan holders enjoy the continued pause since COVID ( XXXX ). This is very frustrating and I would like to see something put in place to help those of us who have the support of a law that was passed unanimously through the Senate, then the House, and signed into law by the President."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Don't agree with the fees charged"
} |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.