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You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On a XXXX balance credit card which I do not monitor I was charged incorrectly for a recurring yearly charge by XXXX XXXX, which I had cancelled in XX/XX/XXXX. However, without my knowledge or consent XXXX XXXX charged me yet again the following two charges the following XX/XX/XXXX XX/XX/XXXX {$99.00} XX/XX/XXXX {$140.00} Only when the credit reporting agency alerted me of the 30 day late payment notice did I even realize that I was incorrectly charged, I called XXXX XXXX and they admitted their error and reversed the charges immediately. I complained to all 3 credit reporting agencies of this error and they sided with Capital One. This was inconceivable as the agent on the phone assured me that they would explain the mishap to the credit agencies and that the error would be corrected. This however was not the case, which is why I have escalated my claim to the CFPB."
}
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": "Credit card debt",
"Consumer complaint narrative": "XXXX / XXXX / {$6000.00} XXXX XXXX / XXXX / {$1800.00} XXXX XXXX XXXX XXXX / {$980.00} XXXX XXXX / XXXX XXXX {$1300.00} XXXX CARD / XXXX / {$4200.00} XXXXXXXX XXXX XXXX XXXX / {$0.00} XXXX / XXXX / {$0.00} XXXX XXXX/ XXXX / {$0.00} XXXX / XXXX XXXX {$0.00} XXXXXXXX XXXX / XXXX XXXX {$4400.00} 15 U.S. Code 1681i - Procedure in case of disputed accuracy ( 2 ) PROMPT NOTICE OF DISPUTE TO FURNISHER OF INFORMATION ( A ) In general Before the expiration of the 5-business-day period beginning on the date on which a consumer reporting agency receives notice of a dispute from any consumer or a reseller in accordance with paragraph ( 1 ), the agency shall provide notification of the dispute to any person who provided any item of information in dispute, at the address and in the manner established with the person. The notice shall include all relevant information regarding the dispute that the agency has received from the consumer or reseller.
( B ) Provision of other information The consumer reporting agency shall promptly provide to the person who provided the information in dispute all relevant information regarding the dispute that is received by the agency from the consumer or the reseller after the period referred to in subparagraph ( A ) and before the end of the period referred to in paragraph ( 1 ) ( A )."
}
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": "In accordance with the Fair Credit Reporting Act. The list of account below has violated my federally protected privacy and confidentiality under 15 USC 1681.
XXXX ; account number 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 an account without my written instructions.
15 U.S.C 1681c ( a ) ( 5 ) Section states no consumer reporting agency may make any consumer report containing any of the following items of information. Any other adverse items of information, other than records of convictions of crime which antedates the report by more than 7 years.
15 U.S.C 1681s-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or have reasonable cause to believe 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": "Since XXXX of XXXX, the CARES Act has given forbearance to millions of homeowners. I filled out and faxed in my mortgage forbearance form in XX/XX/XXXX, listed on the myloancare.com website, after getting laid off due to the COVID-19 pandemic. At the beginning of XX/XX/XXXX, I received a notification that LoanCare marked my account as 30 days late. I called and spoke with a representative that informed me that LoanCare had not yet processed my paperwork and assured me it would be resolved. I then received paperwork via mail telling me that my 90-day forbearance period had started in XXXX and would be valid through XXXX. However, my XXXX payment was still reported as 30 days late.
I called LoanCares customer service multiple times until I was finally connected with a supervisor who instructed me to send in my fax cover sheet, proving that I submitted the mortgage forbearance form in XXXX and that LoanCare would correct the report to show the correct forbearance start date. This process turned into a monthly task. My XXXX payment continued to be reported as 30 days late. I would call and speak with supervisors, each of whom instructed me to send in the fax cover sheet and follow their instructions. Yet every month, the account was still reported as 30 days late. By XXXX of XXXX, I was fed up and frustrated with LoanCares terrible customer service. Finally, a manager instructed me to make the XXXX payment which initially triggered the 30-day late reporting. He assured me that making the payment would stop the late reporting going forward. Following his advice, I ended up taking a loan from a family member to make the XX/XX/XXXX payment in XX/XX/XXXX.
My second forbearance period was 180 days, from XXXX through XX/XX/XXXX. By the time I received my second forbearance, I had already made the original XX/XX/XXXX payment. Several LoanCare customer support supervisors I spoke with confirmed that since I had made the XX/XX/XXXX payment before the start of the second forbearance period in XXXX, my account would be considered current. Yet again, I continued to receive notifications that my account was reported as 30 days late.
I had another forbearance through the end of XX/XX/XXXX, when I finally went back to work. I went back and made all the payments missed in XXXX and am now considered to be current on my loan payments. However, this hasnt solved the issue. This inaccurate reporting has destroyed my credit score and caused undue emotional stress. Multiply credit lines have been decreased to LoanCares reporting error. I am unable to refinance my loan due to this error, which has cost me thousands of dollars."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I am being held responsible for a damaged item that was returned from XXXX for {$100.00}. I was promised a refund and never received it and XXXX mastercard is basically telling me to deal with them for my refund and merchant is telling me to deal with credit card .. I dont feel protected at all by the credit card company as a consumer.."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "Currently I am in the process of buying a new home. So once my credit report was pulled, it was revealed to me that I had XXXX medical bills in my name stemming from XXXX of XXXX. I never went to the Hospital or the Doctor in XXXX. The name of the company is Commonweatlh Financial Systems, located in XXXX, PA. I have requested that they validate this debt, and I have disputed this with the credit bureaus. From my understanding XXXX XXXX sells your information to Commonwealth Financial and they report bogus information to your credit report. I do have XXXX XXXX XXXX cards. So this all makes sense. The amounts are for XXXX, and XXXX."
}
Output:
{
"Issue": "False statements or representation",
"Sub-issue": "Attempted to collect wrong amount"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "LITERALLY every time i try to file a credit report issue with Equifax they ask me security questions and when i answer them correctly they claim i need to call a 1 800 #. Im then connected to the XXXX and told i can give my complaint to them. ( They barely speak english. ) I ask who i can speak with in order to get back online. They tell me its not possible. This only happens when trying to dispute something. Any other time i use the website i get right through."
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Other problem getting your report or credit score"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Equifax put a fraud alert on my credit for what reason, I do n't know. The problem is that I am not able to get credit because they did not include my phone number on the Fraud alert. I called and wrote Equifax asking them to add my phone number to the alert or to remove the fraud alert. Each time I do that Equifax put a brand new fraud alert on my credit. I was denied credit for the fourth time with XXXX XXXX XXXX because of the fraud alert. This has been going on for a year"
}
Output:
{
"Issue": "Credit monitoring or identity protection",
"Sub-issue": "Problem with fraud alerts"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Capital One is limiting the amount of banking data they share with third-party applications. As this is my personal data, they should not be allowed to dictate the terms of that data sharing. I should be able to use applications and tools that enable me to live a healthy financial life -- and this shouldnt be limited to the services provided by Capital One. As such, I am submitting a complaint against Capital One because no consumer should be locked out of their own data."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Other problem"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/XXXX, I sent a certified dispute letter to Experian stating the following : These items are inaccurate because student loans were consolidated XX/XX/XXXX, these are duplicate accounts. The consolidated loan shows up on page 5 of 10, XXXX/XXXX XXXX XXXX Partial Acct # XXXX. I am requesting that the items be removed to correct the information.
Page 4 of 10 : 1 ) DEPT OF ED/XXXX Partial Acct # XXXX 2 ) DEPT OF ED/XXXX Partial Acct # XXXX Page 7 of 10 : 1 ) U.S DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$4600.00} 2 ) U.S DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$3600.00} 3 ) U.S. DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$6000.00} 4 ) U.S. DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$6000.00} 5 ) U.S. DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$4500.00} Page 8 of 10 : 1 ) U.S. DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$4500.00} 2 ) U.S. DEPARTMENT OF EDUCATION Partial Acct # XXXX - {$4500.00} On XX/XX/XXXX, I received a letter from Experian stating the following : " Outcome : Remains - the company that reported the information has certified to Experian that the information is accurate. This items was not changed as a result of our processing of your dispute. Please review your report for details. '' This response was the same for each of the duplicate items I disputed."
}
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": "Credit Karma and XXXX has as XXXX security question they asked in which were answered correctly and via Annually Free Credit report I have been refused to be sent a copy of my credit report. I them called XXXX for a copy and they said one is being mailed. The mail came today and it was and application to provide them with other documents. I have submitted all my information on the website and I will not be denied a free credit report by being forced to submit documents by mail. If they want further information that can be sent via fax. I move to Indiana in XXXX of 2018 and have lived my entire life in XXXX, Washington. They have been given both address and my current one here in XXXX, Indiana. My name, date of birth and Social Security and they refuse to send me a copy of my report"
}
Output:
{
"Issue": "Unable to get your credit report or credit score",
"Sub-issue": "Problem getting your free annual credit report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Federal student loan debt",
"Consumer complaint narrative": "I graduated from college in XXXX. Several years ago I consolidated my student loans with Navient. Since then I have improved my credit rating tremendously, I now have a credit score close to 800. I received a letter from Pioneer Credit Recovery in XXXX of XXXX about a student loan that I did not pay and did not know about as I thought all my federal student loans were included in my consolidation with Navient. I sent a letter to the US Department of Education in XX/XX/XXXX along with supporting documentation ( it is attached ) and I didn't receive a response. What's interesting about the collection agency being Pioneer Credit Recovery is that ( according to a XXXX search ) they are a division of Navient, that has my SSN, DOB, address ( where I have lived for over 5 years ) and my phone number that has not changed in well over a decade. Why would I just now be contacted about this loan? My XXXX, {$1800.00} tax return was off set this year. And I still owe over {$2700.00} due to fees and interest. On XX/XX/XXXX at XXXX I spoke to employee ID XXXX and requested a validation of debt. To date, it has not been received. In an attempt to move on with my life and keep my excellent credit rating, I have offered a settlement of {$1000.00} and it is being rejected. I think {$2800.00} for sending ONE letter a year ago is a pretty good deal, wouldn't you agree? My complaint is that the federal government nor Pioneer Credit Recovery have done their due diligence to collect on this loan and I should not have to pay the inflated fees and interest. Thank you for your time."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "The problem is still not fixed. The mortgage company does not seem to understand how to apply payments and continues to put my weekly payments into suspense and charging late fees, when payments are received well before their deadline of the XXXX monthly. I have been charged bogus late fees and have had to submit many complaints and PHH is still not acknowledging their errors. PLEASE HELP.
Previous complaint numbers XXXX & XXXX Lets start with XXXX payment.
XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX {$1100.00} = XXXX paid in full by XX/XX/XXXX XXXX payments XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX XXXX XXXX=XXXX paid in full by XX/XX/XXXX XXXX payments XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX {$430.00} = XXXX paid in full by XX/XX/XXXX Financial institutions were closed on XX/XX/XXXX due to XXXX day - so if the payment was delayed in being processed by PHH, it was not my fault, as it was there on the XXXX.
XXXX payments XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX $ XXXX XXXX paid in full by XX/XX/XXXX XXXX payments made as of today XX/XX/XXXX XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00}, XX/XX/XXXX {$700.00} = {$2100.00} paid toward the XXXX payment STOP PUTTING MY PAYMENTS INTO SUSPENSE AND CREDIT MY ACCOUNT AS PAYMENTS ARE RECEIVED. YOU CHARGED ME LATE EVEN WHEN PAYMENTS WERE PAID IN FULL PRIOR TO THE XXXX. REMOVE ALL LATE FEES CHARGED AND REIMBURSEMENT ME FOR UNJUSTIFIED LATE FEES PHH TOOK"
}
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": "In Accordance with the fair Credit reporting act. This List of account below has violated my federal privacy and confidentiality under 15 USC1681.
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 A section 2 : It also states a consumer reporting agency can not furnish a instructions.
15 U.S.C. 1681c. ( a ) ( 5 ) section states : no consumer reporting agency may make any consumer items of information Any other adverse item of information, other than records of convictions of more than seven years.
15 U.S.C. 1681s-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to a person knows or has reasonable cause to believe that the information is in accurate."
}
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": "Dear CFPB Staff I have an account with XXXX credit reporting company that updates me on my credit score monthly. XXXX also shows the credit score of the other two credit reporting companies such as XXXX and Transunion, but the information appearing on the XXXX website about my credit debt with XXXX and Transunion is incorrect. My total debt is {$8400.00} and NOT {$11000.00} which is old information shown on the XXXX website. I have tried to address this issue with XXXX before and nothing has changed. XXXX needs to update its website and communicate with both XXXX and Transunion to correct this error on its website. ( please see picture attached ) I have also filed a complaint with CFPB to XXXX Complaint # XXXX and again XXXX did not fix the issue with XXXX."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Old information reappears or never goes away"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "On XX/XX/2022, I was upgraded from a secured Discover Credit Card to an Unsecured card. I had initially put in a deposit of {$200.00} dollars was scheduled to be mailed the deposit via check within a month. It hadn't arrived the first time it was " allegedly '' sent out, or the XXXX. By that time I had asked for it to be sent electronically and Discover assured me they would in about 10 days time. A month has gone by since then I still haven't received anything."
}
Output:
{
"Issue": "Other features, terms, or problems",
"Sub-issue": "Other problem"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Experian removed information from my report in XX/XX/2019 causing my credit scrore to psotively increase, then added the information back to my credit report in XX/XX/XXXX that negatively impacted my credit score. See Attachment."
}
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": "Disputed fraudulent accounts with Equifax on X/X/17 and did not get results back until X/X/17"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Investigation took more than 30 days"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "NAVY FEDERAL XXXX XXXX XXXX XX/XX/15 appears on my credit as a negative item. Back at the time this account was opened, I do not recall ever being late and somehow I was pushed into paying the balance. It was paid and never was taken off of my credit or updated with positive status. This negative reporting is hurting my score and I know I wasn't late or owed .... Please have them remove this account."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I sent letters to all XXXX credit bureaus on ( date of XXXX letter ) regarding fraudulent, inaccurate and unverifiable items on my credit report. It has been over ( date since XXXX letter was sent ) and I have not received a response from anybody. This is a violation of my rights due to not providing a notice of dispute and for failure to communicate that my disputed debt has been disputed. I demand these accounts be blocked, deleted and removed from my consumer report immediately or I will file for litigation. I am trying to obtain credit and these inaccurate, fraudulent and unverifiable items are preventing me from doing so."
}
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 or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "My life has been affected by the pandemic. I enrolled in hardship programs with the company that weren't explained well and the letter they sent out was misleading. I was given the wrong amounts for minimum payments which caused further confusion. I was also told once my account was paid they would file a credit correction but that never happened. If this is not resolved I will have to take legal action against the company because what they're doing is unfair to consumers who already suffered immensely during the pandemic. They reported my account 30 days late three times in a row which was not correct and also the program stated that you would not fall further behind. I was not 30 days late when I enrolled in the program. Please fix my credit report 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": "Other debt",
"Consumer complaint narrative": "On XX/XX/18 this company signed for a certified return mail receipt for a dispute against this company. Tracking number is XXXX XXXX XXXX. Per The Fair Debt Collection and The Fair Credit Reporting Acts ( Section 807 ( 8 ) and 623 ( a ) ( 3 ) ; XXXX XXXX had 30 days to mark my account disputed, which they failed to do and it is a FEDERAL OFFENSE"
}
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": "I do not know",
"Consumer complaint narrative": "My name is XXXX XXXX, and I am sending this request myself to address the inaccuracies on my Credit Reports.
This account is inaccurately and erroneously reporting. Their intentional and willful furnishing of inaccurate information is a violation of FCRA Section 623 and according to FCRA Section 611. I have done many attempts to reach out to XXXX and XXXX Credit Bureaus but they failed to give me any results or feedbacks about this concern. Please investigate and remove it from my personal credit file. Thank you.
I.C. SYSTEM, INC XXXX Balance {$360.00}"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was result of identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have an account with XXXX XXXX XXXX XXXX that was awarded to my wife in our divorce on XX/XX/2014. I notified the bank and they were suppose to stop reporting on my credit report as I was no longer responsible for the account. They continue to report this account as a charge off since my ex wife let the account go. She original owed {$7200.00} she paid about {$770.00} because now the balance is {$6400.00}. I have n idea if this balance is accurate as I have no contact with my ex wife.I have written to all three bureau several times asking for the removal of this account and have even provided a copy of my divorce decree for them."
}
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": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, has violated my rights.
15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions 15 U.S.C 1681c. ( a ) ( 5 ) Section States : no consumer reporting agency may make any consumer report containing any of the following items of information Any other adverse item of information, other than records of convictions of crimes which antedates the report by more than seven years.
15 U.S.C. 1681s-2 ( A ) ( 1 ) A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or has reasonable cause to believe that the information is inaccurate."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card",
"Consumer complaint narrative": "My household is continuing to receive calls from this company via an automated dialer. We answer the call, but no one is there.
After the second call failure, my husband, XXXX XXXX, called back the number on the caller ID. XXXX - Operator ID XXXX was very confrontational and rude. He had no desire to try and help with this situation."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Frequent or repeated calls"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "EQUIFAX has continued to verify the Allegacy FCU account as accurate, I have attached the result of EQUIFAX Dispute dated XX/XX/XXXX. You can clearly see from original creditors payment history that the balance reported as of XX/XX/XXXX is ( XXXX ) however EQUIFAX continues to report a past due balance and the account as charged off. Remove XXXX XXXX account attached to this email because you are reporting a balance and a charge off that are not accurate based upon your own investigation. Furnishing incorrect information willfully on my report is a Violation of FCRA."
}
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": "Student loan",
"Sub-product": "Private student loan",
"Consumer complaint narrative": "On XX/XX/ 2019 I contacted Navient regarding private student loans and being placed in a 100 % discretionary repayment plan called an " interest rate reduction payment plan '' due to being recently unemployed. I had gone through this before with the company in which they lowered the interest rates from 12 % to 2 % for 12 months if i set-up auto pay. This time they would only offer to extend the terms of my payments ( currently paid for 7 years, have 15 years of payments remaining ( initially entered into a 15yr loan ). Then they came back after another 20 minutes on hold and offered me 7 % for 9 months and 3 more scheduled payments at 12 % in which they need bank account information/ debit card information. In the future they try to auto debit from bank accounts without permission. I refused and was denied any sort of repayment plan."
}
Output:
{
"Issue": "Struggling to repay your loan",
"Sub-issue": "Problem lowering your monthly payments"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "It appeared that my credit report has been compromised. Again, I was going through my files & noticed these items that do not belong to me. Since Im a stickler for research, I found that under section 605b of the FCRA you are required by law to remove & block any information or items which were found to be opened due to identity theft. The disputed items do not belong to my file. Im attaching the required FTC Report for you and the bank 's records ( learned through more research both parties require ). Please remove these items : XXXX XXXX XXXX XXXX Balance : {$10000.00} ; XXXX XXXX Balance : {$0.00} ; XXXX XXXXXXXX Balance : {$6000.00} ; XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX ) XXXX XXXX XXXX {$550.00} ; XXXX XXXX XXXX XXXX Balance : {$120.00} XXXX XXXX Date of inquiry : XX/XX/2021 If you feel there is a possibility these items belong to me I will require all documentation which bear my signature ( another research item I found that requires you to verify with 100 % accuracy that these items are 100 % true, accurate, correct, complete & VERIFIABLE ). If you do not provide any records & claim the items are verified please understand I will seek legal action. I do not take kindly to games nor does my attorney. SO please comply with the law so this matter can be handled immediately."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "I am furious that this second mortgage was not forgiven after the whole predatory lending fiasco in the early to mid XXXX era. Most second motgages were forgiven as part of a settlement however this loan was sold and improperly and fraudulently switched to seem as if it were a first loan, avoiding the forgiveness process.
I have paid over {$100000.00} on a {$65000.00} second mortgage and now they want the entire balance of {$51000.00} paid by XX/XX/XXXX. This has to be illegal!
I don't want to pay them another dime and want this loan forgiven and all that I have paid to them refunded to me.
The current company, which is probably the fifth, is XXXX XXXX ( PHH ) and the account number is XXXX. XXXX XXXX XXXX, XXXX. XXXX, NJ XXXX. Tel - XXXX.
I have made numerous attempts to deal directly with the bank however with all of the changes in the companies, who can keep explaining this to non caring individuals."
}
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": "This is my second attempt to have this matter resolved with both the CRA and the Collections Agency neither has been in correspondence with me regarding the issue and it has been over 30+ days. I have asked for account verification of the account that's being reported negatively on my credit report. Please respond with verification. As I know.. According to the Fair Credit Reporting Act, Section 609 ( a ) ( 1 ) ( A ), you are required by federal law to verify -through the physical verification of the original signed consumer contract -any and all accounts you post on a credit report. Otherwise, anyone paying for your reporting services could fax, mail or email in a fraudulent account. I demand to see Verifiable Proof ( an original Consumer Contract with my Signature on it ) you have on file of the accounts listed below. Your failure to positively verify these accounts has hurt my ability to obtain credit. Under the FCRA, unverified accounts must be removed and if you are unable to provide me a copy of verifiable proof, you must remove the accounts listed below."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Prepaid card",
"Sub-product": "Payroll card",
"Consumer complaint narrative": "Rushcard system is down my balance is not accurate they keep telling me it will be fixed soon. I ca n't even pay my bills because my funds are n't available due to their system outage. They keep putting me on hold and transferring me. The longest I 've been on hold was 50 minutes. Never had any issues before with them but now this is ridiculous"
}
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 reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "There has been news that the responses are randomly generated, and the report they gave me makes me unsure whether it 's accurate, or, as inaccurate as the news say."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I was shocked when I reviewed my credit report and found 30 days late payment on the dates XX/XX/2018, XX/XX/2018 - XX/XX/2018. I am not sure how this happened, I believe that I had made my payments to you when I received my statements. My only thought is that my monthly statement did not get to me."
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "You never received your bill or did not know a payment was due"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Ive submitted numerous disputes and the credit bureaus haven't sent me any correspondences at all regarding them."
}
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": "Vehicle loan or lease",
"Sub-product": "Loan",
"Consumer complaint narrative": "Bridgecrest XXXX 3rd Party Allegedly states I the consumer natural person owes an alleged debt of {$19000.00} with their company on XX/XX/ I the consumer extended credit to the company XXXX XXXX for a consumer transaction in exchange a used vehicle. These companies violated Federal Laws, TILA, FDCPA, & FCRA. I the consumer delivered to the company legal documents and evidence of their violations and my non obligation to such debts. They also used abuse and misleading information to force I the consumer natural person into complying with their demands and regulations. A breached of my privacy has been initiated on their behalf as well."
}
Output:
{
"Issue": "Getting a loan or lease",
"Sub-issue": "Fraudulent 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": "Conventional home mortgage",
"Consumer complaint narrative": "on XXXX revived a letter by mail where they inform me that I'm behind on my mortgage. I call and after 5 hours of waiting I showed them that they were wrong in XXXX XXXX XXXX my payment was XXXX.
on XXXX we send payment and to the end of same month we send payment for XXXX the PHH company when they receive the payment and find out was short XXXX they decide to apply my payment to my principal with out authorization of of me or my wife. no call no email from PHH Mortgage and now they want late fees they report to the credit company 's. I spoke with the scalation team name XXXX employee # XXXX they told me they going to try to fixed. and after got another letter on XXXX where the try to charge me another XXXX for escrow same thing call and to today no respond from them.
the problem with the company all the call go to XXXX over there no idea how or who can fixed.
My NamXXXX XXXX XXXX XXXX # XXXX. I need help they need to they need to return the charges and fix my credit"
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "I received an email on XX/XX/22 from a company I have never heard of called XXXX XXXXXXXX. I was sick to my stomach, then I called immediately to report fraud, as I have never heard of this company and did not and would not apply for this type of loan. They assured me they would alert fraud when I called that night- they clearly did not flag the account. I receive a call today- which shows up as spam risk - so I didnt answer - They left a voicemail - that I have not paid the loan - and I am late on payments. The number I called back today XX/XX/22 was XXXX and told them again I NEVER opened a loan with them. I was given information that this person who stole my identity used my old mailing address XXXX XXXX XXXX XXXX - zip XXXX with my cell number and they were somehow authorized to send money to wells fargo which I have never had a checking or savings account with. These funds apparently were released to account number ending in XXXX- on XXXX XXXX. I received a call today my account was past due - which now I am very worried as someone not only stole my identity, my credit has been impacted. There is a late payment with XXXX knows how high the interest is on this type of personal loan. This same person - tried to open another personal account with XXXX XXXX XXXX the same day and got denied. I have filed a complaint with the FTC, I have called Wells fargo and I have called XXXX loans- I am filing a police report next as well as calling XXXX XXXX, XXXX, XXXX and XXXX and am prepared to get professional legal authorities involved."
}
Output:
{
"Issue": "Problem with a lender or other company charging your account",
"Sub-issue": "Transaction was not authorized"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "Im traveling abroad and I don't have access and time to call my bank to open a fraud dispute.
On XX/XX/XXXX I receive a message on my phone saying that my debit card was locked due suspicious activity.
Then I checked my bank statement and there are several transactions that I never authorized, and checking my wallet I noticed my debit cars was missing.
I need to report fraudulent transactions starting on XX/XX/XXXX with XXXX XXXX XXXX XXXX XXXX XXXX XXXX CA XXXX until the last transactions posted on my account.
Before that one the transactions posted are authorized. Attached goes each transactions marked with a circle, those are not authorized."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem using a debit or ATM card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Private student loan",
"Consumer complaint narrative": "XXXX is suggesting I owe student debt. The debt is from a private loan from Union Bank. This loan dates back to XXXX. I have not been a student in college since XXXX. When I attended college, it was a Junior College and I paid out of pocket. I explained to the customer service representative that these two accounts are fraud and I was a victim of identity theft as early as XXXX. Under the FCRA & Statute of Limitations, these fraudulent accounts should have been removed from record years ago. Instead, XXXX bought the fraudulent accounts and are harassing me in an effort to collect on bogus accounts. In explaining the situation to the customer service representative, I was clear that my police reports and FTC reports would be strong enough to show how my ID was stolen and personal information compromised. XXXX is wrong on so many levels with this matter. I will be speaking to a lawyer to have them outline my rights and recourse."
}
Output:
{
"Issue": "Getting a loan",
"Sub-issue": "Fraudulent loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XX/XX/2017 chase bank {$150.00} delinquency"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, MA XXXX Social Security # XXXX DOB : XX/XX/XXXX I hereby make a ghost-letter attempt to a resolution on various credit items and/or items of public record that currently show on XXXX, XXXX, XXXX XXXX and XXXX respectively. My position is legal based upon sections of the Fair Credit Reporting Act as well as the Fair Debt Collection Practices Act. These laws are to make sure that a level and fair playing field exist to protect American consumers from abusive reporting procedures on credit reports, as well as illegal and deceptive collection practices. I stipulate at this time that are no condition am U making an identity theft claim or accusation, so I demand that policy be adhered to by all creditors and respective credit reporting agencies.
Amended consumer rights Fair Credit Reporting Act XXXX XXXX XXXX XXXX. XXXX : XXXX : XXXX Fair Debt Collection Practices Act : XXXX XXXX XXXX On these subscribers that are reporting the affixed information, I am requesting full validation such as, original transfer rights to collect, confirmation of legal timeline to report on credit and/or collection procedures, validation of licensing requirements, posted bonding requirements, oversight on monthly payment histories, proper subscriber agreement compliance with credit reporting agencies, and overall compliance with all Fair Credit Reporting Act and Fair Debt Collection Practices Act bylaws as mandated by federal laws. If all of these requirements are not in order, then I have the legal right to request the deletion of this tradeline accordingly. I especially desire compliance under the 84-month timeline for deletion of negative information, with the deletion of the account details altogether. My dispute is quite specific in the exception area posted below : 605. Requirements relating to information contained in consumer reports [ 15 U.S.C. 1681c ], Section 609 of the Fair Credit Reporting Act ( FCRA ), Disclosures to consumers [ 15 U.S.C. 1681g ] ; Section 610 ( a ) ( 1 ) [ 1681h ], and 611.
CREDITOR CONTACT INFORMATION : XXXX XXXX ( XXXX ) XXXX ACCOUNT # XXXX ACCOUNT # XXXX ACCOUNT # XXXX ACCOUNT # XXXX ACCOUNT # XXXX ACCOUNT # XXXX On these subscribers that are reporting the affixed information, I am requesting full validation such as, original transfer rights to collect, confirmation of legal timeline to report on credit and/or collection procedures, validation of licensing requirements, posted bonding requirements, oversight on monthly payment histories, proper subscriber agreement compliance with credit reporting agencies, and overall compliance with all Fair Credit Reporting Act and Fair Debt Collection Practices Act bylaws as mandated by federal laws. If all of these requirements are not in order, then I have the legal right to request the deletion of this tradeline accordingly.
I am posting this dispute through the Consumer Financial Protection Bureau, the governing body of the Fair Credit Reporting Act and the Fair Debt Collection Practices Act. Please respond to this dispute within 30-days as mandated within federal guidelines.
Sincerely, XXXX XXXX"
}
Output:
{
"Issue": "Written notification about debt",
"Sub-issue": "Didn't receive enough information to verify debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "Beginning last XX/XX/XXXX, I began the process of looking into either a standard refinance or a partial cash out refinance with Navy Federal Credit Union.
I used their portal, and after receiving the estimates of what payments and APR would be authorized and paid for a credit report.
That began a nightmare odyssey for me and my wife. They have now had our credit reports done 4 separate times by them, the MLS agent responsible for us dissappeared for weeks or months with no returned phone calls.
I had authorized them to have access to my accounts for verification purposes, but after over a month of no contact I wrote them and revoked in writing that access. Yet every day I am notified of them accessing my accounts including my government pension account from when I was a prosecutor.
About a month ago I received a call from them, ( new agent ) saying that they had messed up one our social security numbers and needed to run credit again. I asked for what, he told he it was for a product that we were not even applying for.
They have damaged my credit, they have and are continuing to, in violation of state law, invading my privacy by accessing my accounts. Then today I received a note from them that we were being denied credit from them for not providing information, yet we WERE NOT applying for a product and had told them multiple times to shut it down.
There was a brief moment, after 7 documented un returned phone calls, 3 electronic reach outs, where we were able to connect with XXXX XXXX, NMLS # XXXX. She was supposed to righting the ship. However it nearly 7 weeks later when another agent called to offer us another product. XXXX then failed to return multiple phone calls.
So to receive this letter and to continually see my accounts being accessed, feels very wrong and I am asking for help.
Navy Federal 's response to me has been they are inidated with a large amount of applicants. That's not an excuse.
Please help.
Thank you-"
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I have repeatedly tried to fix this problem with Ability Recovery Service. I know my rights and this account has not been investigated nor validated. I asked for cease and desist also, I asked for an investigation for this account requesting : To Whom It May Concern : Be advised this is not a refusal to pay, but a notice that your claim is disputed and validation is requested. Under the Fair Debt collection Practices Act ( FDCPA ), I have the right to request validation of the debt you say I owe you. I am requesting proof that I am indeed the party you are asking to pay this debt, and there is some contractual obligation that is binding on me to pay this debt.
This is NOT a request for verification or proof of my mailing address, but a request for VALIDATION made pursuant to 15 USC 1692g Sec. 809 ( b ) of the FDCPA. I respectfully request that your offices provide me with competent evidence that I have any legal obligation to pay you.
At this time I will also inform you that if your offices have or continue to report invalidated information to any of the three major credit bureaus ( XXXX, XXXX, XXXX XXXX ), this action might constitute fraud under both federal and state laws. Due to this fact, if any negative mark is found or continues to report on any of my credit reports by your company or the company you represent, I will not hesitate in bringing legal action against you and your client for the following : Violation of the Fair Debt Collection Practices Act and Defamation of Character.
I am sure your legal staff will agree that non-compliance with this request could put your company in serious legal trouble with the FTC and other state or federal agencies.
If your offices are able to provide the proper documentation as requested in the following declaration, I will require 30 days to investigate this information and during such time all collection activity must cease and desist. Also, during this validation period, if any action is taken which could be considered detrimental to any of my credit reports, I will consult with legal counsel for suit. This includes any listing of any information to a credit-reporting repository that could be inaccurate or invalidated. If your offices fail to respond to this validation request within 30 days from the date of your receipt, all references to this account must be deleted and completely removed from my credit file and a copy of such deletion request shall be sent to me immediately.
It would be advisable that you and your client assure that your records are in order before I am forced to take legal action.
CREDITOR/DEBT COLLECTOR DECLARATION Please provide the following : Agreement with your client that grants you the authority to collect on this alleged debt.
Agreement that bears the signature of the alleged debtor wherein he/she agreed to pay the creditor.
Any insurance claims been made by any creditor regarding this account.
Any Judgments obtained by any creditor regarding this account.
Name and address of alleged creditor.
Name on file of alleged debtor.
Alleged account number.
Address on file for alleged debtor.
Amount of alleged debt.
Date this alleged debt became payable.
Date of original charge off or delinquency.
Verification that this debt was assigned or sold to collector.
Complete accounting of alleged debt.
Commission for debt collector if collection efforts are successful.
Your claim can not and WILL NOT be considered if any portion of the above is not completed and returned with copies of all requested documents. This is a request for validation made pursuant to the Fair Debt Collection Practices Act. Please allow 30 days for processing after I receive this information back.
cc Federal Trade Commission I have not received none of the information I requested."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "My name is XXXX XXXX. The credit bureau is refusing to remove two bankruptcies from my credit report that are not my accounts. I have never submitted nor filed for bankruptcy in my life. I am XXXX years old and I've been dealing with credit for a very long time. A recurring issue I've had to fight with the credit bureau about is that they will report other persons' accounts on my credit that are not mine. This has happened to me before and is the exact same situation that is happening with these two bankruptcy accounts. The first one has a filing date of XX/XX/XXXX with reference #XXXX. The second one has a filing date of XX/XX/XXXX with reference #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": "Money transfer, virtual currency, or money service",
"Sub-product": "International money transfer",
"Consumer complaint narrative": "Dear Sir, I am XXXX XXXX from XXXX, recently I have been in contact with a seller of a car on XXXX, I use to be very carefull when it comes to buy but this time the seller I think is a scammer.
I have bought many cars in US and this is the very first time I've got trapped.
Before paying I got the seller passport, spoke to hime over the phone, asked for many pictures even some hard to find details for a non knowledgeable average connoisseur to make sure I was buying from someone honest.
Then I paid the car to his bank account XXXX XXXX asked a shipping company to pick up and the seller wasn't answering anymore.
The advert also from XXXX.
And somehow!! the advert is again on.
https : XXXX I was wondering if it's the same scammer, probably yes.
You will find all the attached details, link, bank account below : Name of the seller and bank details where I have wired the money to ( probably fake ) XXXX XXXX XXXX is : XXXX XXXX XXXX XXXX XXXX XXXX, FL XXXX Cell Phone : XXXX XXXX XXXX Email address : XXXX Wire Instruction : Account Name : XXXX XXXX Bank Name XXXX TD Bank USA Checking Account # XXXX Best regardsSwift code : XXXX Routing # XXXX Bank address : XXXX XXXX XXXX XXXX XXXX, NJ XXXX First he has sent me the saving account!! But the money has bounced Saving account : XXXX Now with the checking the money has been cleared on his account.
Hopefully you can do something about that, I have been trying to find a number to contact the bank where the money has been sent but no success, may be the police will certainly have the power to get that."
}
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 have been fighting with transunion for a while because they continue to keep removing my XXXX XXXX XXXX XXXX Account a good standing account that is XXXX XXXX XXXX, which is also my oldest account on my credit report, for no apparent reason!! This is so very frustrating and i dont understand why they are being aloud to constantly do this. They remove and i file a complaint and then they reinsert it!! Why must a consumer continue to go thru continuous problems with this company. The language barrier is already frustrating within it self and then i have to deal with them deleting my accurate information. PLEASE reinsert. I even have a letter from transunion telling me the account was reinserted and will stay."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Difficulty submitting a dispute or getting information about a dispute over the phone"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "On XXXX XXXX, I attempted to use my Capital One Card to purchase an XXXX Computer for approximately {$3000.00}. it was immediately rejected, and immediately I had a text message and email asking me to call immediately. When I called I gave them sufficient information that they knew I was the card holder and I explained what I was doing and that I was at the XXXX Store. I was told there had been suspicious activity on my account and the account was frozen. I could unfreeze the account if I sent them documentation to a secure portal they would send me in an email. The documents they wanted included both sides of my drivers license, both sides of my social security card, and a full copy of a utility bill. These 3 documents together amount to PERSONAL IDENTIFIABLE INFORMATION which is all that would be needed to steal my identity. I reached out to the number on the back of my card ( XXXX ) a total of 4 times. Each time hoping I would find someone that would help me. Everyone stated this was the safest way to protect both customers and the company. Yet, in XX/XX/2019 approx. 140,000 social security numbers were compromised when they had a breach. When they refused, I asked if they would cash in my rewards miles ( currently valued at approx. {$550.00} ) and send it to the name and address on the card. They refused, and said as long as the account was frozen they couldnt do that. I finally found the name and email and phone number of the President of Capital One, and reached out to him twice through email ( XXXX ; ph. XXXX ) Still no response. I posted my issue on XXXX, only to find that another person had a similar situation and is out over {$4000.00} so far. ( You may be hearing from that person, since my Attorney Generals office suggested contacting you ( & I promised that person I would share helpful information if I got any ). At this point, if you can at least help me get Capital One to release my rewards miles, which I have earned, I will be done with Capital One forever. But I cant imagine that what they are doing is okay. I did learn from one of the persons on the 4th phone call I made to Capital One that what caused them to freeze the account was the purchase I was making at the XXXX Store. It was approximately {$3000.00}. Yet, I had made a purchase a few months earlier for approx. {$2800.00} and that didnt trigger anything. I have an EXCELLENT CREDIT RATING and I will not put myself in a situation of handing off sufficient information that someone could easily steal my identity."
}
Output:
{
"Issue": "Trouble using your card",
"Sub-issue": "Can't use card to make purchases"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/19 I paid my XXXX XXXX credit card bill with a XXXX ACH for {$700.00}. My Accountant informed me that I should cancel the ACH because it was a Corporate Account. I cancelled the XXXX ACH and remitted a XXXX ACH on XXXX {$620.00} which was my new Balance. XXXX XXXX XXXX sent both ACHs to XXXX which would only authorize the ACH for {$620.00} because that was the only one authorized by me. XXXX XXXX repeatedly asked me for a letter from XXXX stating I authorized both ACHs. I refused because that was not right or authorized. My Credit limit was {$950.00} and the total of the ACHs was {$1300.00} which was almost double of what I owed. They closed my account which was ok with me and I spoke with many employees of XXXX XXXX and I asked them how to take care of the balance and they informed me to pay as agreed and no problem. XXXX payment was made on time as usual as well as XXXX payment. XX/XX/XXXX XXXX XXXX reported to Experian that I was late and they charged off my account. I have had this card for over 2 years and have never been Late. This is reflected in all my documentation I have to support this. Experian still show a charge off even though I have all the supporting documents proving I have paid as the cardholder agreement and XXXX XXXX is abusing their power to report a charge off even though I have paid as agreed. Experian has removed the late payments and the past due amount XXXX XXXX was reporting through 2 different open disputes I have filed. This XXXX XXXX Charge off should be removed and shown paid as agreed. This has lowered my credit score over 59 points. I havent contacted the other credit reporting agencies about this injustice but I will. I pride myself on making all my credit card payment as agreed. This is hurting me tremendously. Thank you!"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "AARGON Agency , Inc will not reach out to research this account not consider any of my documents from an ID theft I experienced while living in a XXXX XX/XX/XXXX. I have sent them my insurance card for services rendered in XXXX XXXX and a police report for the ID theft on the XXXX XXXX services while living at the shelter that is also attached to the dispute on my credit report - AARGON said they will NOT look at those documents ... It is real frustrating how these collection agencies can easily attach an unconfirmed account, especially since the new debt collection laws have taken place in the state of NEVADA. Please help me in the matter, I feel XXXX three times over and have the documents to prove that I do not owe this debt to XXXX. I have also sent these same documents to XXXX XXXX XXXX today ( XXXX/XXXX/XXXX ) to the attention of XXXX - whom is the PT Accounts Services Manager. Research and review of these documents you will notice that my name is partially my maiden name and married name ( XXXX ) on my supporting documents."
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt resulted from identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "I am filing this complaint about lendingpoint LLC regarding an alleged debt that is in dispute. On XXXX XXXX, I mailed a notarized affidavit certified mail to the company citing various violations of the FDCPA and requesting a response from the company as well as compensation within a months time. No response was received, and the company continues to violate my consumer rights as well as harass and defame my character. Their repeated phone calls during business hours interrupt my daily business as I am currently self-employed as well as their emails inundating my mailbox with correspondence including harassing language about reporting information to the credit reporting agencies and attempts to collect a debt. Lendingpoint has failed to rectify the situation and/or respond in any manner and continues to attempt communication when they were asked to cease their harassment. They were also invoiced for XXXX worth of violations. They are in violation of the following : 15 USC 1692b. ( XXXX ), 15 USC 1692b. ( XXXX ), 15 USC 1692f. ( XXXX ), 15 USC 1692d. ( XXXX ), 15 USC 1692c. ( XXXX ), 15 USC 1692c. ( XXXX ), 15 USC 1692c. ( XXXX ). They continue to violate my right to privacy and their reporting of negative information to all three credit agencies has resulted in emotional distress, loss of the ability to conduct proper business and is a direct defamation of character which includes my credit score and ability to obtain credit."
}
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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "I offered to pay the amount but they refused to remove it from my credit report. Theres no benefit to paying it if they do not remove it. They do not think they have to remove it if its Paid.
Account resolution Corp XXXX. XXXX XXXX XXXX mo XXXX XXXX"
}
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": "I discovered that some of the information on my credit report was incorrect when I reviewed it. Sections 609 ( a ) ( 1 ) ( A ) and 611 ( a ) ( 1 ) require the 3 credit bureaus to validate these accounts ( A ). It is not permissible to fail to validate these reporting accounts as unverified information without providing any proof within the time frame specified by law. My credit report includes the following erroneous information that needs to be investigated and corrected : XXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "There are unknown hard inquiries on my credit report. Im requested the verification of the inquiries and the method of verification.
On XXXX. XXXX, XXXX, MICROBILT CORPORATIO add a hard inquiry on my credit report per XXXX. No contact information has been reported by this creditor.
On XXXX. XXXX, XXXX, XXXX add a hard inquiry on my credit report per XXXX.
On XXXX. XXXX, XXXX, XXXX add a hard inquiry on my credit report per XXXX. No contact information has been reported by this creditor.XXXXBut companies did not provide the following proof : Permissible Purpose My written authorization Proof that I personally initiated these inquiries.
To my knowledge, I have not signed any documents authorizing your organization to view my credit history therefore, your inquiry into my credit report violates the Fair Credit Reporting Act, Section 1681b ( c ) : Transactions Not Initiated by Consumer."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "back in XX/XX/XXXX I trade in a XXXX XXXX XXXX for a XX/XX/XXXX XXXX XXXX to XXXX, TX XXXX and was told by the dealer not to make XX/XX/XXXX payment because it would mess up the pay off amount. I also at the time did have possession of the XXXX which was being financed through capital one. Needless to say I looked at my report and XXXX XXXX had me reported as delinquent for the month of XXXX despite the care not being in my possession. I disputed it twice with XXXX XXXX with no resolution. I also disputed it with XXXX and they came back saying XXXX XXXX is correct despite it showing on my credit report that I had another at that time frame. I call XXXX about this issue and they told me that XXXX XXXX was responsible and then I called XXXX XXXX and they placed the blame on the dealership. I feel that I have been the one did wrong during this situation. both companies were paid and I had something negative placed on my credit 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": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "Synergetic Communications , Inc. Say that they are trying to collect on an account from XXXX. The last time I have had an account with XXXX was almost 40 years ago."
}
Output:
{
"Issue": "False statements or representation",
"Sub-issue": "Indicated you were committing crime by not paying debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "Today XX/XX/XXXX I received a letter dated XX/XX/XXXX that my mortgage is increasing by {$74.00} per mom after an escrow analysis was done. The letter informed me that the increase is effective on XX/XX/XXXX. I immediately called XXXXhe servicing company XXXX number listed on the letter and was greeted by a young lady by the name of XXXX ID number XXXX who explained that since the letter was dated XX/XX/XXXX that there is nothing she could do to assist me in extending the effective date even though I was only notified 19 days prior. I asked her why nothing could be done, She told me that since I didnt seem to like her answer that I could with call Lakeview the Finance company or speak with her manager. She transferred me to a woman who claimed to be a manager by the name of XXXX ID number XXXX who stated that because the letter was dated XX/XX/XXXX theres nothing she could do to help me. She placed me on hold and came back and said that the letter was available to me online under my account on XX/XX/XXXX. I then asked her to confirm for me when they received my XXXX mortgage payment thats made online every month, she stated on XX/XX/XXXX. I asked her then how am I supposed to know as a first time homebuyer to look for such a letter AFTER Ive already made my XXXX payment, that I access my account when its time to make a payment and at that time there was no letter there. She then offered to transfer me to an escalation line. I asked if I would have to explain all this to yet another person, XXXX assured me that she would warm transfer me so that she could let the person who answered know what was going on. After being on hold a few minutes a person greeted me by the name of XXXX ID XXXX, she had no knowledge of what was going on and stated that no one told her that a call was being transferred to her so I had to explain all over again and to no avail I got nowhere with her. She stated that all she could do was waive a late fee if I needed to pay late. I explained to her how hard Ive worked on my credit, that my payments are always early for that very reason, that what Im asking is for a proper 30 day notice that a XX/XX/XXXX effective date would be fair ... she was no help AT ALL. I am now forced to try to figure out how I will be able to come up with the rest of the funds for this increase. The amount may not seem like much to most but to a single Mother, it means a great deal."
}
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": "n accordance with the Fair Credit Reporting act XXXX Account # XXXX, has violated my rights, XXXX XXXX XXXX # XXXXXXXX XXXXXXXX # XXXX XXXX XXXX XXXX XXXX # XXXX XXXX
15 U.S.C 1681 section 602 A. States I have the right to privacy.
15 U.S.C 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions"
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Reporting company used your report improperly"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have a loan with FEDLOAN. I have always made my payments on time. For some reason, I realized that there was a late payment on my credit report. As you can see, I have always had a stellar payment record with this company. I tried contacting both XXXX, XXXX and FEDLOAN with no successful resolution. XXXX and XXXX were reporting me late. There was definitely an error on their part. I was never 30 days late."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "Comenity Bank sets consumers up.
The company 's agent told me in the past that they already set up automatic payment for me in their system with my debit card. But they did not.
So this time, when I traveled internationally to a country, where the internet is of limited access, to handle a family health emergency in early XXXX, I was completely ruined by this vicious company.
To begin with, I was not informed of the card balance, which was only {$30.00}. This balance is completely within my payment ability. As long as they reached out and let me know, I could have made the payment right away.
Instead, this company intentionally kept me unaware of this balance while I was out of the country for over a month. This resulted in me paying {$66.00} of late fee and penalty charges. Moreover, this resulted in a significant hit on my credit, which is completely unacceptable.
When I reached out to the company in XXXX when I came back to the country, the company 's agent told me that they are going to charge me late fee no matter what - since this is the way they run their business. They told me that they are not going to include this incident in my credit report - my credit will not be negatively influenced, as I was traveling out of the country.
However, two months later, my credit was still hit by this. Comenity did not deliver their promises. When I confronted them about this, they now say that they can't do anything about it and I can only report to CFPB.
This company is really dishonest in the way they conduct business and should be shut down.
As such, as an innocent and uneducated American consumer, I ask for CFPB 's look into this case and help me fix the credit issue. Thank you."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Difficulty submitting a dispute or getting information about a dispute over the phone"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "The company Helvey & Associates Inc is reporting negative information illegally, unjustifiably, and refuses to acknowledge or respond to my request for retraction of their negative credit reporting. I have notified them initially by telephone, that a utility bill for property address XXXX XXXX XXXX XXXX, Florida must be removed from my credit report for there failure to properly notify me of this debt as required by both state and federal fair debt collection practices acts. It has been established by both myself and there customer service representatives that they sent the original dunning notice to the same service address that they provided the service too, at this property that I never lived in since it was a short term ownership for investment purposes. Since I did not receive the proper notification of this account being delinquent, this account ended up on my credit report without meeting the requirements under law for credit reporting. I followed up with them notifying them of this situation in writing on XX/XX/2019 ( Exhibit 1 attached ) - a correspondence to which I have yet to receive an acknowledgement or response from Helvey and Associates. This situation is extremely frustrating as I am being penalized on my credit report for an unfair, unjustified collection that I was never given the proper notification as mandated in FDCPA laws. This error, and Helvey Associates refusal to correct there mistake is causing me significant financial, emotional, and psychological damages due to my credit score dropping significantly- and not allowing me to refinance my investment properties or acquire new ones at the best terms I would be getting. These damages are in the tens of thousands of dollars and I will be pursuing a legal claim for them if this situation is not rectified immediately."
}
Output:
{
"Issue": "Written notification about debt",
"Sub-issue": "Didn't receive notice of right to dispute"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "i only gave them premission to run my credit one time not twice they have two different dates on my credit i will like the inquire for XXXX XXXX to be taking of my credit report i didnt give them premission this day"
}
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": "Decided to make a big move in buy my first home. I knew I wanted to go into a first time home owners program, due to them being able to help you financially. As I was sitting with my mentor she said my credit needed to be pulled. I knew I had good credit so I was not worried about that being the first step. Out of surprise a big list of accounts I have never seen or heard of in my life showed up. Those fraudulent accounts do not belong to me. Please remove these things from my report."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "I took a mortgage with Sterns Lending in XX/XX/XXXX for a condo in Florida. At the time of closing the state official who processed the closing documents notified me that my mortgage seems to have been sold already. The person at Sterns that I dealt with denied it. The next month I received a notice that XXXX XXXX was collecting my mortgage payment and I was given address to send payments. Since then I have been given a new address to send payments with no explanation. Upon calling XXXX to find out what was happening with my mortgage and to ask them if they owned or were selling my mortgage they told me my mortgage wasn't ever owned by them that they were only a collection agency. They told me my mortgage was owned by a company called XXXX XXXX XXXX - a name I have never heard of before. I have no information from or about XXXX XXXX XXXX and no way to contact them. It is now mid XX/XX/XXXX and I have no idea who I have a mortgage with or who to pay. I have two different XXXX locations telling me to pay them. According to my research I should be notified if my mortgage is sold. Further, looking on line I see that Sterns ( original mortgage agent ) is in bankruptcy, but when I call the agent there he claims nothing is wrong and the he still has control of the mortgage."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "Hi, I was checking my XXXX credit report tonight and noticed a collection account as shown as follows : MIDWEST RECOVERY SYSTEMS Address : XXXX XXXX XXXX XXXX XXXX XXXX XXXX, MO XXXX ( XXXX ) XXXX Account Number : XXXX Original Creditor : XXXX XXXX XXXX Address Identification Number : XXXX Status : Collection account. {$1200.00} past due as of XXXX 2015.
Date Opened : XXXX Date of Status : XXXX Reported Since : XXXX Last Reported Date : XXXX Type : Collection Terms : 1 Months Monthly Payment : {$0.00} Responsibility : Individual Credit Limit : {$760.00} High Balance : N/A Recent Balance : {$1200.00} Recent Payment : {$0.00} I would like to claim wisleblower status as I believe that this collection agency and/or collection company is possiblity making up fake collections in order to defraud me and possibly other individuals unknown to me at this time. I state that I do not owe XXXX, nor have i ever taken out a payday loan with XXXX due to no need for XXXX at this time. The weird thing is the credit limit is XXXX dollars which is above a payday loan and the last time i cashed a check at XXXX was months ago for like around XXXX dollars, I have a great record with that company XXXX in XXXX, FL. In closing, it seems to be some sort of debt collection companies creating false accounts or something to like bill myself and possibly other U.S. Citizen/Nationals.
Respectfully, XXXX XXXX XXXX"
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I am disputing hard inquires on my credit report because this is fraud and does not belong to me."
}
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, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have always made my payments on time. For some reason, I realized that there were 30 days late payments on my credit report. I have read enough to understand my rights. The Experian Credit Bureau are required to verify for themselves, but they failed to neither verify nor update me about these complaints. I believe the company is violating Sections 616 & 617 of the FCRA, as required under Section 609 ( a ) ( 1 ) ( A ) Disclosures to consumers [ 15 U.S.C. 1681g ] ( ( a ) Information on file ; sources ; report recipients. Experian shall, upon request, and subject to 610 ( a ) ( 1 ) [ 1681h ], clearly and accurately disclose to the consumer. If the consumer to whom the file relates requests that the first 5 digits of the social security number ( or similar identification number ) of the consumer not be included in the disclosure and the consumer reporting agency has received appropriate proof of the identity of the requester, the consumer reporting agency shall so truncate such number in such disclosure. & Section 611 ( a ) ( 1 ) ( A ) Procedure in case of disputed accuracy [ 15 U.S.C. 1681i ] ( a ) Reinvestigations of Disputed Information. If the completeness or accuracy of any items of information contained in a my file at Experian is disputed by the consumer and the consumer notifies the agency directly, or indirectly through a creditor, the Bureaus shall, free of charge, conduct a reasonable reinvestigation to determine whether the disputed information is inaccurate and record the current status of the disputed information, or delete the item from my file in accordance with paragraph ( 5 ), before the end of the 30-day period beginning on the date on which the agency receives the notice of the dispute from the consumer or creditor. I believe theyre unwilfully noncomplying with federal law & this issue is creating a ton of emotional physical stress. Id like this matter resolved ASAP as it is impacting my health & wellbeing. Please update or remove the late payment from this account. Below is the account with erroneous information and Id like remedied ASAP.
XXXX XXXX Date Opened : XX/XX/06 Balance : {$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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "H & R block-Emerald Card was set to send me a new card because there was fraud committed on my account. There were some fraud internet charges that they thankfully managed to stop. The new card never arrived, and on Memorial Day I received text notifications that there were ATM withdrawals on my account. I tried calling but offices were closed due to the holiday. Later that same day, I received e-mail notifications that someone was trying to link my new card to another H & R block account. I called the number on the e-mail, and again no answer because offices were closed. The following day I called and informed them of the entire situation. They sent me forms to fill out to formally file the Fraud claim. I e-mailed the forms over and was told through the phone that someone will contact me about the " investigation ''. Today, XX/XX/XXXX, I called for an update and was told that the claim was denied and that the decision was final, and to wait for a letter in the mail about this. No further information was given. I called a total of five times to H & R block and the Emerald card people trying to find out anything about the reason and to reach the team that conducted the investigation. No one could tell me absolutely anything. Neither about my claim, the so-called investigation, nor the denial. They don't even have a direct number to the " investigation team '' or department. Almost {$2000.00} were taken from my account. 1. I was never contacted by the " investigation team ''. 2. They clearly have recordings of me calling to inform them that someone is stealing my money and trying to link my card to their account. 3. What did they investigate?! and why doesn't anyone in their team know the reason or can give me a number. If someone was trying to link their account to my card, who was it? If they used an ATM machine, request recordings! Why don't they prosecute THEM? My account is left with {$6.00}!!!! and I get my claim denied with no information but to " wait for a letter in the mail that will arrive in 5 business days ''. That doesn't help me at all."
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem using a debit or ATM card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I am consumer, fully insured individual, that has been harassed by XXXX and their abusive and unfair practices after extend my credit for in a consumer credit transaction for personal, family and household purposes.
XXXX has breach their fiduciary duty and misrepresented my interested as security for the account used for phone service by several interruptions allegedly for nonpayment and harassing text messages alleging consumer owes a debt.
I have sent dispute notices to XXXX regarding an alleged debt and XXXX has responded with generic letters as responses. XXXX sent a letter dated XX/XX/22 claiming to be entitled to report write-offs in effort to disgrace the consumer deliberate violation of consumer right of privacy in their refusal of exclusions of transactions or experiences via consumer reports furnished as defined under Fair Credit Reporting Act.
XXXX has misrepresented the term Bureau as defined under FCRA in their letter to deceptively assume to role as credit reporting agency themselves.
XXXX has in collusion with with Afni. Inc to represent their collection efforts.
XXXX has been instructed cease and desist direct and indirect communication with consumer as XXXX has refused to comply with Fair Debt Collection Practices Act Verification of an alleged debt in a positive balance indicating per Fair Credit Billing Act and credit balance demanded as refund."
}
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": "Mortgage",
"Sub-product": "Other type of mortgage",
"Consumer complaint narrative": "I did a loan modification with Chase Bank under the federal HAMP program on XXXX of XXXX, during that time, I had financial hardship and was approved for the loan modification. During the signing of the documents, I noticed a clause of a balloon amount of {$210000.00} at maturity date, I asked the processor about the clause and the answers was, not to worry, that was the accumulation of interest during the life of the loan. During the following years I called the bank to do the same questions about this balloon amount and the answer was always the same, not to worry this is the accumulated interest for the life of the loan.
Recently I received an offer from Chase Bank to recast the loan, the bank sent documents for me to sign, I noticed the same clause, but now with additional notes, ( attached the recast documents ). In my curiosity, I called the Chase Bank to ask about this clause, but this time they informed that this was an amount that I needed it to pay if I were to sell or pay the property off.
My Complaint is as follows, I looked to Chase Bank for help under the federal program HAMP to save my property, instead of helping me, they deceit me, with malice and purpose. From day one, their purpose was to defraud and gain from a home owner in hardship. I came in with a debt of {$360000.00} and came out with a debt of {$530000.00} just by signing this contract. Chase Bank did not help me at all, instead they defrauded me as they have done with thousands of customers.
I plead to the federal government to put these greedy thieves in jail and make them pay for all the suffering and stress they have caused to the honorable people of this great country."
}
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 card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "The issue is that on my XXXX Credit Report .My Date Opened is incorrect. I have disputed it with XXXX three times and filed a complaint and a dispute through you CFPB and it still is has n't been updated. Date Opened needs to be updated to XX/XX/2016. It 's reporting incorrectly as, XX/XX/2016."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have been asking TransUnion and XXXX XXXX TO REMOVE THIS CHARGE OFF MY CREDIT REPORT FOR SOME YEARS NOW AND NEITHER TRANSUNION OR XXXX XXXX HAS DONE SO. I HAVE SENT PROOF OF A COURT RULING IN MY FAVOR TO BOTH COMPANIES AND NOTHING HAS HAPPEND I THINK ITS REALLY SAD THAT THESE COMPANIES CAN JUST RUIN A PERSONS CREDIT AND NOTHING CAN BE DONE ABOUT IT THIS HAS CAUSED ME STRESS AND PROBLEMS WHEN NEEDING TO APLY THE USE OF CREDIT TO MY PERSONAL LIFE. THE RULING TOOK PLACE WAY BACK IN 2017 AND THIS COMPANY HAD THE NERVE TO TAKE ME BACK IN FRONT OF THE SAME JUDGE 2YRS LATER I WAS NOT HAPPY NEITHER WAS THE JUDGE THE RULING ENDED IN A JUSGEMENT FOR ME ( WITH PREJUDICE ) WHICH MEANS IT SHOULD NOT HAVE BEEN FILED AGAIN AFTER THIS JUDGEMENT WELL NOW IM HERE BECAUSE THEY STILL REFUSE TO REMOVE IT I NEED HELP AND ALSO WANT TO KNOW IF I CAN FILE A SUIT MY SELF AGAINST BOTH XXXX XXXX AND TRANSUNION 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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting Act. The List of accounts below have violated my federal protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX XXXX XXXX account # XXXX, Opened XXXX XXXX has violated my rights by reporting an inaccurate late payment. This was also during the COVID19 pandemic. And should be removed not only because of the violation of FCRA privacy and consumer laws, but also because of the CARES ACT provisions for consumers.
15 US.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 man 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.
15 U.S.C. 1681 section 611 ( 5 ) Treatment of Inaccurate or Unverifiable Information ( A ) In general. If, after any reinvestigation under paragraph ( 1 ) of any information disputed by a consumer, an item of the information is found to be INACCURATE or INCOMPLETE or can not be verified, the consumer reporting agency shall ( i ) promptly delete that item of information from the file of the consumer, or modify that item of information, as appropriate, based on the results of the reinvestigation; and ( ii ) promptly notify the furnisher of that information that the information has been modified or deleted from the file of the consumer."
}
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": "There is one account on my credit that is not mine I need this off my credit report since it does not belong to me in any shape or form I have not applied signed or used an account like this.
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": "XXXX XXXX XXXX # XXXX is reporting a collection to my credit report that is in violation of my 30 Day Right to Dispute, I never received any kind of notice for this collection and I settled it as soon as I found out about it. This should not be on my credit report and needs to be removed immediately."
}
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",
"Consumer complaint narrative": "I have disputed this charge. I have sent them request to verify this charge. They do not have any signed documentation on this charge and I want them removed. I believe it was filed incorrectly under medicare/medicaid and this item is not mine."
}
Output:
{
"Issue": "Disclosure verification of debt",
"Sub-issue": "Not given enough info to verify debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I received an unexpected public record on my TransUnion credit report on XXXX XXXX , XXXX for a circuit court # XXXX . It states that it was reported as of XXXX XXXX . This account has NEVER been on my credit report and I have attached proof. We are in XXXX XXXX , and I am concerned as to how TransUnion can report a negative public record from XXXX XXXX on my report causing my TransUnion score to drop DRAMATICALLY. This public record needs to be deleted from my credit report immediately. I have attached a copy of a recent TransUnion credit report showing that this public record was not showing up on my credit file under the public records section. Here we are a year later, and TransUnion is inaccurately and illegally reporting that this public record was reported as of XXXX XXXX ."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Public record"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to 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": "Original Creditor account is already 4-5 years old. A new company is showing on credit report as newly opened in 2021. It's stated NOT to communicate with creditor if it is an old account for that may, " reset '' the time clock of responsibility showing on credit report. I have had NO contact with new company, but it shows as fairly new account. This is not a fair practice as account is actually old and should be falling off my report. Original Creditor may have an account with newer company showing on my report, however, I DO NOT, and NEVER HAVE had an account with the new company, nor have I had ANY contact with them. PLEASE HELP, I've been dealing with this issue for far too long."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XXXX XXXX , 2017, cont acted Experian regarding dispute with XXXX , XXXX Experian did not properly investigating the validity of debt with XXXX XXXX XXXX XXXX . I notified Experian that I provided XXXX , XXXX , documentation that disputes the information XXXX XXXX XXXX XXXX initially provided and that information was not requested and inaccurate information was submitted to the credit bureaus."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hi I am submitting this XXXX XXXX this isn't any influence and this is not a third party. TransUnion has low and unfair credit number for me in their report. I have complained. The problem has not been resolved. my fico has me at a credit score over 719transunion has me at a score around 590. That is a huge difference. transunion paints me as a XXXX. my fico say I have good credit. What the heck is going on here. i have almost no debt and my identity was stolen causing my score to drop n i made this clear for 60 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 us rept but they refused they had me on mute for 4 hours which was hurtful I have a perfect repayment record. I have very low credit utilization. I have three negative credit items outstanding debt now. I have modest but ok income. Social Security. Something is wrong with Transunion. I do not understand why they are abusing consumers .This was a fist step towards attempting resolution. They kept lying telling me they disputed n its not reporting but it keep reporting this inaccurate information without my authorization. They refused or were unable to verify n remove the inquiries and its been 60days n they record the calls n admitted they had my police report n ftc and affidavit That was after attempting to contact Transunion more than 21 times. transunion is an abusive company. They are supposed to be protecting consumers. They need to be reigned in. they are causing me severe XXXX and stopping me from getting this job offer n now im homeless n cant provide to my XXXX XXXX XXXX daughter PLEASE HELP ME PLEASE im depressed now.with no help."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act. The List of accounts below has violated my federally protected consumer rights to privacy and confidentiality under 15 USC 1681.
XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX 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 or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Bank of American Credit Card Number : ending XXXX I would like to DISPUTE for the charge on XX/XX/2018 with XXXX XXXX in the amount of {$15000.00}. This letter is in regards to my DISPUTE for the charges listed below, for the services prepaid that I have not received : XX/XX/2018 XXXX XXXX XXXX NC {$15000.00} This transaction was used as partial payment to pay for the XXXX XXXX that will take place on XX/XX/2018 to XXXX, 2018 at XXXX.
Please see my attached document for 1 ) my ticket to the XXXX XXXX XXXX 2 ) Receipt for the XXXX XXXX XXXX 3 ) Contract agreement for XXXX XXXX XXXX The dispute amount of {$15000.00} was used as partial payment to pay for the XXXX XXXX that will take place on XX/XX/2018. I was unable to attend because XXXX was shut down by the Federal Trade Commission ( FTC ) for being a scam and fraudulent company.
I did not receive XXXX XXXX service from XXXX. I did not get to attend the XXXX XXXX. I did not received the service indicated on the contract agreement for XXXX XXXX..
XXXX was shut down by the Federal Trade Commission on XX/XX/2018 and have been taken over by a Receiver named XXXX XXXX XXXX, please check the FTC website and the Receivers website for the latest updates.
thank you very much"
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Checking or savings account",
"Sub-product": "Other banking product or service",
"Consumer complaint narrative": "The following is being forward to Inspector General, F.B.I., Florida CFO, XXXX Police, IRS, SEC, CFPB, and FINRA. To make sure that this matter is properly handled you can close the transaction or the professionals can instruct you to close it but I have a right to the offering and I am willing to enforce that right to the fullest unless you comply with services advertised, offered, and that as escrow agent you are contractual obligated to provide. The only role you had in this transaction was to make sure that I could secure/insure my interest in the offering I provided that. You then come to hinder that with intrusive inquire into my trust only to deny establishes malice when you role is insurance verification.
On XXXX XXXX, 2017 I went to the escrow agent for a closing on the acquiring of a digital asset ( ICO ) XXXX XXXX. To qualify for this closing I was required to provide an insurance product to secure insurable interest in the goods being sold. Suntrust Wealth Management Team/Escrow Agent XXXX XXXX, XXXX XXXX, XXXX XXXX, XXXX XXXX, and XXXX XXXX sent me to a few departments When I only needed them to verify my insurance product was valid, issue the letter of credit, and place the asset in a trust account. They first lead me to believe that as escrow agent they would handle the closing. I have approximately 60 emails covering this correspondence arranging closing getting financials over to wealth management team and insurance product so that my trust interest would be protected. As an insurers agent the parties involved are aware that to provide false information regarding business related to the policy is insurance fraud. But because they feel that the can hide under the color of florida state law F.S. 672.722 to deprive me of my right to private trust property. The state was aware that an organization could use legislation that was passed like F.S. 672.772 to deprive individuals of their right to private property, so Florida legislatures enacted F.S. 70.001 to protect those rights. Because of the wealth management teams willful denial to close out and lawfully closed transaction means they are attempting to use F.S. 672.722 to deny me a right to insuring interest in a transaction that I executed lawful subscription contracts to XXXX USD in XXXX ICO. The wealth managment team are depriving me of a right to F.S. 70.001 under the color of the law F.S. 672.722.
Title 18 U.S.C. 241 and 242 states that if a person or organization does this that it is criminal. I am willing to hand over all emails and financials to prove the merits of this criminal claim.
Title 18 U.S.C. 241 states : If two or more persons conspire to injure, oppress, threaten, or intimidate any person in any State, Territory, Commonwealth, Possession, or District in the free exercise or enjoyment of any right or privilege secured to him by the Constitution or laws of the United States, or because of his having so exercised the same ; or If two or more persons go in disguise on the highway, or on the premises of another, with intent to prevent or hinder his free exercise or enjoyment of any right or privilege so secured They shall be fined under this title or imprisoned not more than ten years, or both ; and if XXXX results from the acts committed in violation of this section or if such acts include XXXXXXXX or an attempt to XXXX XXXX XXXX XXXX or an attempt to commit XXXX XXXX XXXX or an attempt to XXXX, they shall be fined under this title or imprisoned for any term of years or for life, or both, or may be sentenced to XXXX.
Title 18 U.S.C. 241 states : Whoever, under color of any law, statute, ordinance, regulation, or custom, willfully subjects any person in any State, Territory, Commonwealth, Possession, or District to the deprivation of any rights, privileges, or immunities secured or protected by the Constitution or laws of the United States, or to different punishments, pains, or penalties, on account of such person being an alien, or by reason of his color, or race, than are prescribed for the punishment of citizens, shall be fined under this title or imprisoned not more than one year, or both ; and if bodily injury results from the acts committed in violation of this section or if such acts include the use, attempted use, or threatened use of a dangerous weapon, explosives, or fire, shall be fined under this title or imprisoned not more than ten years, or both ; and if XXXX results from the acts committed in violation of this section or if such acts include XXXX or an attempt to XXXX XXXX XXXX XXXX or an attempt to commit XXXX XXXX XXXX or an attempt to XXXX, shall be fined under this title, or imprisoned for any term of years or for life, or both, or may be sentenced to XXXX."
}
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": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "Following my previous CFPB complaint ID : XXXX.
XXXX has remained unresponsive. Direct calls to this collection agency are unsuccessful. After submitting another complaint with the CFPB, XXXX, the organization replied just introduce another service provider.
It is unfair for XXXX to damage a consumer 's report without following the fair debt collection practices act. XXXX claims to have the reported account since XX/XX/XXXX. However, there was no contact with the consumer before making a report to the credit bureaus.
See below for a follow up complaint against XXXX : XXXX XXXX XXXX XXXXXXXX reported a debt to my credit report in XX/XX/XXXX in the amount of {$4500.00}. XXXX claims that they hold this account since XXXX of XXXX. However, the company did not call, mail, or email me about this debt to allow me the opportunity to dispute it or resolve it before reporting it to the credit bureaus. After several calls to XXXX, I finally spoke with an agent who was not willing to provide much information over the phone and advised that a verification notice would be mailed to me. It was not until I contacted this organization that they finally communicated with me. Since I received the notice I have called XXXX at least ten more times at XXXX, and I am continuously directed to their voicemail. There is no live agent to speak with.
Please note the account was reported to the credit bureaus before my first and only conversation with XXXX. The impact on my credit report triggered my research about the organization and my contacting them.
According to CFPBs debt collection rules, the debt collector is required to communicate with a consumer in person, via phone, email, or via mail, then wait a reasonable amount of time before reporting to credit bureaus. XXXX did not communicate with me before reporting this account to the bureaus thus XXXX failed to follow appropriate CFPB guidelines.
I previously submitted complaint ID : XXXX with the CFPB to follow up on this collection account and XXXX failed to provide more context on their collection practice and why I was not contacted since XXXX. Instead, XXXX advised that a new service provider : XXXX XXXX XXXX, is to be contacted about this debt. XXXX also claimed that I provided a cease-and-desist to the new service provider when they contacted me.
Again, I could not even communicate with XXXX, how am I expected to know that another collection agency is attempting to collect on this debt? I have no documentation pointing to a new service provider. I learned about this because I initiated a dispute process with CFPB. Otherwise, I would have never known that a new agency is now involved.
I plead with CFPB to look further into this issue as XXXX failed to follow fair debt collection practices.
-Why was the consumer not contacted since XX/XX/XXXX?
-Why was the account suddenly reported to the consumer 's credit report in XX/XX/XXXX without calling, emailing, or mailing any letters or invoices since XX/XX/XXXX?
-Why does the consumer report state that the account is opened in XX/XX/XXXX, when in reality it started to be reflected in the consumer 's report in XX/XX/XXXX?
-Why is a new service provider being introduced after XXXX failed to communicate with the consumer since XX/XX/XXXX? Did XXXX receive authorization from the consumer to transfer this account to another service provider?
-Why does the consumer report still name XXXX however, the organization claims a new collection agency is managing the debt?
-Is it an acceptable practice to damage a consumers report by reporting a debt for which the consumer was not timely notified about nor allowed to pay before being forwarded to the credit bureaus?
All these questions need answering as these are the gaps XXXX practices created."
}
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": "There is an account reporting with XXXX XXXX XXXX for some type of payday loan. This is not mine it's showing that it opened in XX/XX/2015 and I can tell you I never opened an account with any type of payday loan in 2015. This needs to be deleted. I was notified through my XXXX XXXX account that it was reporting on Equifax."
}
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": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "This is a monthly rental we signed contact for 1 year. When the contract was over I called the owner first time 35 days before end of contract and then twice 30 days which is required per terms of contract. I spoke with the owner and he was/is still very mad because my truck left ruts in his freshly paved parking lot. And he told me where to park. The owner of course said I never called him about leaving when I called 3 times o my to get his voicemail every time he also claims we left stain on concrete floor? We did no such thing"
}
Output:
{
"Issue": "Taking/threatening an illegal action",
"Sub-issue": "Attempted to/Collected exempt funds"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Payday loan",
"Consumer complaint narrative": "I owe cash call a debt, I have paid them regularly but have had to make arrangements to make payment later than usual or to skip a payment due to financial hardship. Although they set up plans with me to stop an automatic payment they continue auto draw my acct. I have an agreement with them now thru a supervisor to pay tomorrow XXXX/XXXX/15, today XXXX/XXXX/15 they have called XXXX times, using unk caller so I ca n't call back. I believe this is a violation of the law and when I asked them that they said they can call as often as they want"
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "trying to connect my bank account for installment loans and managing my monthly bills"
}
Output:
{
"Issue": "Managing an account",
"Sub-issue": "Problem accessing account"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "IC System Inc ( on behalf of XXXX XXXX ) posted a derogatory collection notice on my credit report in the amount of {$59.00}. No one has tried to contact me about this previously and I have no accounts with either IC System or XXXX XXXX, so this fraudulent claim must be removed immediately."
}
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": "I do not know",
"Consumer complaint narrative": "A garnishment suit and was taken against me about a debt from XX/XX/XXXX to XXXX that I never knew anything about. An email do the suit was sent to me from my employer In early XX/XX/XXXX about wages being garnished on next pay check. Claim was taken out from XXXX XXXX XXXX XXXX who claim that they couldnt give me much info because I obtained counsel from my employer which I nor my employer knew nothing about. To gain anymore info about the debt, I reached out to Unifund CCR Partners who gave me as much info as they could. Firm representative XXXX told me the debt was from XX/XX/XXXX and payments were made up until XXXX. The debt is almost 20 years old and I dont believe it to be mine."
}
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": "Credit card debt",
"Consumer complaint narrative": "Has contacted family members outside of immediate family. I also believe that wages have been garnished outside of the limitations of when they can be as I also just received another letter and another court date for Midland financial."
}
Output:
{
"Issue": "Threatened to contact someone or share information improperly",
"Sub-issue": "Talked to a third-party about your debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Mortgage debt",
"Consumer complaint narrative": "This is a second request to Bank of America, N.A. but this time to XXXX XXXX XXXX XXXX XXXX XXXX XXXX has failed to answered my requests ). I've sent 2 letters in regard to Tax Forms I need to file my taxes.
Note to CFPB : This is not a duplicate request. This is another set of Tax Forms I need."
}
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": "This is my 5th 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, CA XXXX XX/XX/XXXX, XXXX XXXX XXXX XXXX XXXX CA XXXX XX/XX/XXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX CA XXXX, XXXX XXXX XXXX XXXX XXXX XXXX CA XXXX, XXXX XXXX BV XXXX XXXX CA XXXX, XXXX XXXX XXXX Balance : {$1000.00}, XXXX XXXX XXXX Balance : {$42000.00}, XXXX XXXX Bankruptcy Reference # : XXXX Date Filed/Reported : XX/XX/XXXX, XXXX XXXX XXXX Balance : {$0.00}"
}
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": "I would like to receive {$150.00} refund from XXXX XXXX XXXX XXXX XXXX.
Bank of America Dispute Department Representative filed claim with incorrect reason ( {$150.00} charge, for charging card without providing service instead of Suspicious or Fraudulent charge ). Now, BOA argued that they could not open new or reopen old claim for the same charge amount, even so reason for claim is Refundable Security Deposit of {$150.00} was not refunded as per company policy.
Bank of America made mistake and want cardholder to pay for it.
On XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX company charged {$140.00} on my credit card Visa XXXX from Bank of America for car rental during vacation on XX/XX/XXXX XX/XX/XXXX.
Base on rental policy : 1. Upon receipt of billing information, the appropriate deposit amount ( s ) will be charged to the credit card or debit card provided.
2. SECURITY DEPOSIT - Normally from {$100.00} to {$350.00} depending on the vehicle. This deposit is normally collected within 24 to 48hrs of picking up the vehicle and is refundable.
3. Incidentals Deposit - An Incidentals Deposit is required on all vehicles and is automatically calculated at the estimated amount of the rental plus an additional ( 1 Hundred to 3 Hundred and 50 dollars ) depending on the vehicle and vehicle pickup location. This deposit is normally collected within 1 to 30 WORKING DAYS of picking up the vehicle and is refundable.
XXXX. All Pre Paid Rentals are Non Refundable.
5. Signature is not required to complete a reservation 6. Giving verbal, written, or online reservation information with credit card or debit card information acknowledges renter agrees that any and all charges placed on their credit card are legitimate and authorized charges for the rental transaction they are completing, and hereby agree to not dispute these charges.
On XX/XX/XXXX I received email from XXXX with {$150.00} Security Deposit charge on the same card. It state this is refundable deposit which will be refunded 5-15 working days after car returned.
If you have any questions, visit our support site at http : //www.rentacaroftexas.com, contact us at XXXX, or call at XXXX XXXX.
I called there and company representative told that he just provide cars to renters and name of company XXXX XXXX XXXX. He see my reservation, but has nothing to do with charges.
So, I sent email to XXXX. No reply at all.
On XX/XX/XXXX Ive got credit card bill with {$150.00} charge from XX/XX/XXXX.
I called Bank of America and told them about Suspicious or Fraudulent charge. Credit card representative from Dispute Department after looking on term and condition determined that rental could make charge of an refundable Incidentals Deposit, but after XX/XX/XXXX, NOT before ( 30 working days and even so, they work every day ) or SECURITY DEPOSIT - Normally from {$100.00} to {$350.00} depending on the vehicle. This deposit is normally collected within 24 to 48hrs of picking up the vehicle and is refundable.
Since none of deposit meet term and condition, she recommended to file dispute ( Case # XXXX ).
On XX/XX/XXXX I received letter from Bank of America about my claim denial and reinstatement of {$150.00}.
The reason for denial is : Credit Card Company can not intervene in merchants contract.
They recommended dealing with merchant on my own, because XXXX provided information that deposit could be charged best on policy.
I contacted Rental company on XX/XX/XXXX, but got the same reply as before : nobody dealing with credit card charges and nobody replied to e-mail.
I paid {$150.00} to credit card, but 2 month after car was retured and one month after money were released to car rental company, I got no Refund."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "Recently in doing some research on the student loans in my name that are being serviced by Fed Loan Servicing. I learned that the loans are for a school by the name of XXXX University XXXX. I never signed an enrollment agreement with XXXX University XXXX nor did I ever sign or agree to become a former student of XXXX University. This seems like fraud to me. I do not wish to be associated with XXXX University and I never agreed to it. I do not understand how this is possible and how this legally has taken place. I do not understand why there are student loans listed under XXXX University in my name. I never agreed to this. I am requesting XXXX University XXXX to provide information stating that I agreed to be associated as a former or current student with this school. Before today I had no knowledge of any of this. I am requesting documentation with signatures that Fed Loan Servicing has the right to pursue loan payment from me for the school XXXX University XXXX. I do not wish to be affiliated with XXXX University XXXX. I never agreed to any enrollment or loans with XXXX University. Therefore Fed Loan Servicing must substantiate seeking loans associated with XXXX from me."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Don't agree with the fees charged"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "My husband and I both have, and have had loans through XXXX XX/XX/XXXX. We have both on several occasions set up, online, and over the phone automatic payments. 3 times. Once I tried. Twice he has tried. All three times we receive late notices through the mail with late fines attached. I am convinced this in on purpose so they can collect the late fines. The last time we called, they refused to answer their phones, and put us on a call back list. Four days later they have still not called us back. The last letter was XX/XX/XXXX. It says we have 15 days to pay. The last, of many, phone call we made was on the XX/XX/XXXX. XXXX XX/XX/XXXX says my husband owes {$85.00}. The letter also says we are fourth-five days past due. But we were living under the assumption our automatic payment had been set up, and that the bill was coming out of our bank accout. No email, to my knowledge was sent, and no phone call to notify us of any card number discrepancy or name misspell. Just a letter, already late, telling us we did not even try to pay. I am very frusterated and I would not wish this lack of customer service on anyone. It feels as though there are no humans running their business, only faulty computer systems."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Problem with customer service"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have been a victim of Identity Theft and the items I am disputing do not narrate to any business that I have made or approved. Neither did I obtain any ownership of goods, money, services, nor profits from the Theft of my Identity. 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 Transunion 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, physical & financially... and I would truly not what to pursue a legal remedy... I need the account removed now!
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": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "XX/XX/XXXX I requested a another escrow analysis of my loan payment. I have recieved a new payment statement of {$1900.00} with an increased escrow monthly payment of $ XXXX. I spoke with XXXX and she was able to waive the $ XXXX " escrow reserve '' payment per my request. She advised me that I could pay the $ XXXX payment to be paid over 12 months for the escrow shortage, which added up to $ XXXX to remove from monthly payment sum. So, I made electronic payment of {$1700.00} toward escrow the same day to have this removed from my monthly payments. In addition, I notified her that I had applied for my county tax exemption and it was adjusted in a reduction of {$940.00}, which should reduce my monthly payments as well. I submitted the letter to them via PennyMac message center. I recieved emails stating that it was under review on the dates of XX/XX/XXXX and XX/XX/XXXX and should get a response and note an adjustment in premium within 14 business days.
I have submitted two additional emails on yesterday to follow up as another payment of {$1900.00} is due. Please note, despite my above payments I paid {$1900.00} for XXXX to allow time for review and adjustments and now today is XX/XX/XXXX and PennyMac is still requesting a payment of {$1900.00}. This continuation of this payment is cause for financial hardship for me and they show their indifference in acknowledging my repeated calls and emails.
I did call on XX/XX/XXXX after being on hold for nearly XXXX minutes, I spoke with XXXX, who said she would resolve it and put me on hold only to disconnect the call ; and no one called me back to resolve it or responded to my emails from yesterday.
I have attached all the emails and the tax form for your review and a resolution."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional fixed mortgage",
"Consumer complaint narrative": "Ocwen purchased the mortgage on my XXXX condominium a number of years ago. They have been repeatedly sending me letters insisting that my homeowners insurance has lapsed and that I must obtain coverage or they will obtain it for me at my expense. Our homeowners association maintains this insurance, it has not lapsed for the 18 years I have had this mortgage, and I have faxed a copy of the policy to Ocwen three times. They continue to request the proof of insurance, even though I have called to tell them that I have faxed it to them, and now have sent me a letter on XX/XX/XXXX telling me that they have purchased insurance for me from a company named XXXX XXXX XXXX for {$390.00} annually and have added this cost to my mortgage without my consent. I also have individual homeowners insurance which also has not lapsed in 18 years. This is a scam which I have read all about them doing to other homeowners on the internet. I have always paid my mortgage on time and am also buying another home in XXXX with XXXX with no problems with that mortgage. This company is corrupt and needs to be put out of business."
}
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": "Debt collection",
"Sub-product": "Auto debt",
"Consumer complaint narrative": "In XXXX I bought a vehicle from XXXX of XXXX, it was a XXXX XXXX XXXX, the dealership signed me up for automatic payments through a company called XXXX XXXX, before any payments were ever made through that company I read up on them, saw nothing but bad customer service and support reviews ad well as lots of people claiming fraud and such, so I called them had eye witnesses in the vehicle when I called stating I was canceling the account with them and paying my finance company directly, they gave me attitude but still agreed to close it, since my very first payment was due I always paid my finance company directly, then this month XX/XX/XXXX I get a collection on my credit report from security credit systems on my report stating it was opened on XX/XX/XXXX which makes no sense when especially never received any form of notice from anyone about this which is a violation of the fair credit reporting act and fair debt collecting practices, the collection states its for {$590.00} well for starters my monthly payment for my vehicle through my finance company was only {$580.00} a month not {$590.00}, upon me calling the collection company known as security credit systems about violating the law and the fair debt collecting practices and the fair credit reporting act they continued hung up on me, I kept calling back getting angrier and angrier as they continued to hang up me, finally the XXXX who answered the phone and kept hanging up on me said she would get someone else to talk to me, she put the phone down on her desk and I could hear her talking to other people in the office, I could hear all of them XXXX and complaining how they didnt wan na talk to me so she came back over didnt say a word and hung up the phone again, they refused any commit about no 30 day notice, no validating of the debt as well as all refusing to talk to me about it and just hanging up on me, the account in question is fraudulent cause it was closed before ever used, also upon this being done found evidence of attempted theft by autopay plus trying to take 2 different charges of two different amounts from my bank account, 1. For {$600.00} on XX/XX/XXXX then another 2nd. Attempted charge of {$630.00} on XX/XX/XXXX, both were attempted fraudulent charges of theft seeing as neither of those 2 charges were my monthly also the collection wasnt even my monthly car payment, also account was closed before they attempted these charges that they sent to a collection company called security credit systems who are violating the fair credit reporting act and fair debt collecting practices by refusing to comment, talk to me, validate the debt, or even send me any form of notice, and the fact that the original creditor tried charging multiple fraudulent attempted charges of theft when account with them was closed and was paying my finance company directly and at a different price than any of these charges or anything were."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
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