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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": "Debt collection", "Sub-product": "I do not know", "Consumer complaint narrative": "This following scam information was sent to me by text ( XXXX ) XXXX again on my phone in this order and reported previously to FTC : PLEASE REVIEW THE BELOW FOR INSTRUCTIONS TO ACCESS YOUR SECURE COMMUNICATION, XXXX : XXXX? XXXX. REPLY STOP TO END************************************** This private message is from State Collection Service , Inc. ( " SCSI '' ) a debt collector. Please review the contents of this message and any attachment ( s ) in private so that other people may not view it. The attached message is encrypted. To open your encrypted message : 1 ) Double click on the secure PDF attachment. 2 ) When prompted for a password, please enter your current 5-digit zip code to open your encrypted message. This is a post-only communication from SCSI. Please only respond to this communication with " STOP '' if you do not wish to receive communication via text message from SCSI in the future. If you have questions or concerns regarding this communication or if you experience trouble opening the attached document, please call ( XXXX XXXX XXXX. This is an attempt to collect a debt. Any information obtained will be used for that purpose. This communication is from a debt collector. Sincerely, State Collection Service , Inc . I do not know who this scam company is. I do not have a bill with them. I have received this message 3 times before and sent them a text message to stop sending me texts and they refused. I blocked this number in the past and they got back in." } Output: { "Issue": "False statements or representation", "Sub-issue": "Impersonated attorney, law enforcement, or government official" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account 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 appears that my credit file has been compromised. Again, I was going through my records & noticed many items which 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 items which are found to be opened due to identity theft. The dispute items do not belong to me. Im attaching the required FTC Report for you and the bank 's records ( learned through more research both parties require ). Please block/remove these files. XXXX XXXX XXXX XXXX XXXX XXXX Balance : {$0.00} XXXX XXXX XXXX Balance : {$0.00} XXXX XXXX Balance : {$0.00} XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX ) XXXX XXXX : {$940.00} XXXX XXXX XX/XX/XXXX XXXX XXXX FI XX/XX/XXXX XXXX XX/XX/XXXX XXXX XXXX XX/XX/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": "Student loan", "Sub-product": "Federal student loan servicing", "Consumer complaint narrative": "XXXX XXXX XXXX in conjunction with certain lenders have used predatory lending practices, overcharging people for the education- being unclear on the nature of these loans- and the end result are degrees with no value as there is no accrediation and the degrees are bascically worthless.No ability to transfer any credits to further education, impossible loan amounts with high interest, We feel we have been ripped off and left with nothing but a huge amount of debt." } Output: { "Issue": "Dealing with your lender or servicer", "Sub-issue": "Trouble with how payments are being handled" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "In accordance with the fair credit Reporting actXXXX XXXX XXXX bank, account # XXXX has violated my rights. 15 USC 1681 Section 602 States I have the right to privacy. 15 USC 1681 Section 604 A Section 2 : It also states a consumer reporting agency can not furnish a account without my written instructions. 15 USC 1666B : A creditor may not treat a payment on a credit card account under an open end consumer credit plan as late for any purpose." } Output: { "Issue": "Improper use of your report", "Sub-issue": "Reporting company used your report improperly" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Conventional home mortgage", "Consumer complaint narrative": "on XXXX my mortgage company NewRez LLC paid insurance premiums to XXXX XXXX which is not my insurance for my house. NewRez said i must have authorized it, which was a lie. I called XXXX XXXX and found out it was not even my address or name on the account that was paid. They have returned the money to NewRez and both are saying it is the other ones mistake of course. The money has been cashed by NewRez but still waiting for them to put the money back in my escrow account. I do not want this to happen again or to some one else. I have been told it is my fault figure it out. I have tried and just want some one to explain how it happened and do not let it happen again." } Output: { "Issue": "Trouble during payment process", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Bank account or service", "Sub-product": "Checking account", "Consumer complaint narrative": "Hi there, I 'd signed up for a Citibank Citigold account after confirming with a representative by phone that I was eligible to receive their promotional XXXX XXXX XXXX AAdvantage miles after, in the first 60 days, making {$1000.00} in debit card purchases and 1 or more bill payments for two consecutive months. After completing these requirements, I was told 3 months later that I was actually not eligible for the promotion. I have followed up numerous times, only to be told every time that the promised promotion would not be honored. In the meantime, I was also assessed ( 3 ) $ XXXX monthly fees just for having the account open. The promotion was what drew me to the account and I had no interest in it had I known it would not be honored. Thanks for your help, CPFB! XXXX" } Output: { "Issue": "Account opening, closing, or management", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Conventional adjustable mortgage (ARM)", "Consumer complaint narrative": "We have been denied a loan modification for the third time despite meeting all the guidelines. XXXX XXXX XXXX blames the Investor Deutsche Bank National Trust Company as Trustee on behalf of XXXX XXXX XXXX XXXX Mortgage. This Investor gave a sub prime mortgage and continues to be predatory in regards to how every six months our monthly payments doubles. In XXXX 2016 our payment increased by {$1000.00}. and in XXXX the payment doubles. This Investor gave us a four year trial modification which we paid on time for XXXX. This investor has refused to modified the loan permanently which is a violation of the law. The investor is hoping and praying that we stop making our payments so that they can foreclose on us. Deutshe Bank and XXXX placed a {$93000.00} junk fees and charges which continues to hurt us, because they gave us an interest only payment which does not exist anymore. This Investor does not want us to live their AMERICAN DREAM OF HOME OWNERSHIP." } Output: { "Issue": "Loan modification,collection,foreclosure", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I recently checked my credit report and noticed some accounts that I do not recognize and I have not authorized. I believe I have been a victim of Identity Theft. There are several mistakes and inaccuracies regarding the account status. I am demanding this account be immediately removed off my credit report. Bankruptcy XXXX XXXX I have contacted the District Court and they have confirmed that they have not given any private information in reference to this account. I also have attachments along with this complaint containing the correct information. 28 USC 1746 Unsworn declarations under penalty of perjury" } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information belongs to someone else" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "In accordance with the Fair Credit Reporting Act XXXX XXXX Account # XXXX has violated my rights 15 U.S.C 1681 section 602 A. states I have the right to privacy. 15 U.S.C 1681 section 604 A section 2 : it also states a consumer reporting agency can not furnish a account without my written instructions" } Output: { "Issue": "Improper use of your report", "Sub-issue": "Reporting company used your report improperly" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "In accordance with the Fair Credit Reporting Act XXXX XXXX Acct # XXXX, has violated my rights. 15 US C 1681 Section 602 A states I have a right to privacy. 15 US 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 received a statement of credit denial from XXXX XXXX XXXX XXXX Ohio office stating I was denied a pay day loan for which I never applied for. The statement also says that I was denied due to " credit product not available '' I called the phone XXXX. They said they would forward my name, phone, and email address and check into it. I get an email back stating that the account??? Was opened with your personal information. They also want me to fill out a more detailed report with even more personal information and have it witnessed by law enforcement. Of course this is a voluntary report. I used to work at XXXX XXXX XXXX corporate office in the accounting Dept. Years ago. I know what a pay day loan is., I also know scams but the paperwork is very professional. Should I be worried, also someone could be pretending to be from XXXX XXXX XXXX to gain information. 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": "Payday loan, title loan, or personal loan", "Sub-product": "Installment loan", "Consumer complaint narrative": "Contacted XXXX/moneylion about paying off full balance on my account. Made a call to them Monday XX/XX/XXXX. At that time I made a debit card payment of my full balance amount. Was told by XXXX that the payment would go through in three hours. I called today Friday XXXX because I had not received any email correspondence. Was told by XXXX that the payment was not processed due to their system was down. They were notified about their system being down on Wednesday the XXXX. I should have gotten a courtesy email or call, but didnt. Now Im forced to pay extra four days worth of interest on an issue that was not my problem in the first place." } Output: { "Issue": "Problem with the payoff process at the end of the loan", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Conventional home mortgage", "Consumer complaint narrative": "During Covid19 I was unable to keep up with my mortgage payments. I went online and requested assistance in XX/XX/XXXX. I never heard back and in XX/XX/XXXX I called. I spoke with a guy by the name of XXXX XXXX and was advised that he would wait until I hear back from them about the repayment plan.. I was heading into another month of late if I waited so I asked what amount could I pay until then to avoid foreclosure and he advised me XXXX I paid that amount with him over the phone which a few months of payments. I still had not heard back from the company so I called them and was advised on XX/XX/XXXX by XXXX XXXX that my account was in Foreclosure status and should not be! he called and emailed a few people and advised me to wait to hear back.. I then called back a few days later and got another rep that offered a repayment plan of $ XXXX month.. I agreed and paid a early.. I continued the payment repayment plan and they never updated my credit report to reflect the plan.. It continued to reflect past due months.. I then emailed XXXX XXXX my POC and still have not received a email back yet. I received an alert on my account reflecting it may be marked as foreclosed and I have no idea what's happening. Please Help" } Output: { "Issue": "Trouble during payment process", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "On XX/XX/2021 received a text from they said Equifax XXXX XXXX XXXX saying my credit score had dropped 46 points on XX/XX/XXXX and to click a link today to confirm or dispute.. Called Equifax and they said did not come from them." } Output: { "Issue": "Problem with fraud alerts or security freezes", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Credit card debt", "Consumer complaint narrative": "A company claiming to represent our original creditor called and threatened legal action if not settled that day. The call came on XX/XX/XXXX at XXXX. The phone number on my phone records shows XXXX. They had personal information and balance of debt owed. A settlement was agreed upon and automatic payments were set up. {$120.00} to be withdrawn every two weeks starting XX/XX/XXXX until {$970.00} was paid. The last payment of {$100.00} was withdrawn on XX/XX/XXXX. We received no final paperwork. On XX/XX/XXXX we received a summons from the court that the actual debt collector was suing us for our full debt amount. We learned the company withdrawing the money, Absolute financial services LLC, was unreachable and in no connection with our original creditors, the debt collector they sold our debt to, or their law offices. We have since entered a case through the courts who in XX/XX/XXXX ruled in favor of the plaintiff, the debt collector and still owe the full amount of our debt. The judge sent us the fair trade commission information of the ongoing case against absolute financial services in hopes we can recover our money lost." } Output: { "Issue": "Took or threatened to take negative or legal action", "Sub-issue": "Threatened to arrest you or take you to jail if you do not pay" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Conventional fixed mortgage", "Consumer complaint narrative": "Good morning, Acct # XXXX name is XXXX XXXX and I reside at XXXX XXXX XXXX XXXX , XXXX , Pa XXXX. My mortgage is with Nation Star Mortgage. The facts of my complaint are as follows : under RESPA a mortgage company is required to remit overage paid in escrow upon notification and proper due diligence. I have such an overage and I notified Nation Star Mortgage as soon as I received a letter from the county clerks office. Nation Star instead of following standard procedure botched the process and the county office did not remit my payment as required. Additionally, I have a cease and desist order on, my account. This means all communication must be done via mail ; Nation Star continues to harass me with solicitations and on XXXX Nation Star representatives called me XXXX. I need them to fix their incompetents and correct my escrow as soon as possible. Please have them stop call me. Thank youXXXX XXXX" } 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": "Mortgage", "Sub-product": "Conventional home mortgage", "Consumer complaint narrative": "I applied for a " streamline XXXX XXXX refinance '' on XX/XX/XXXX with CASHCALL MORTGAGE company located at XXXX XXXX XXXX, XXXX, CA XXXX with XXXX XXXX Loan Agent - Team 7 | NMLS # XXXX, with the knowledge provided to me that I would get a loan between 3.750 to 3.875. They pulled a bait and switch on XX/XX/XXXX ; XXXX XXXX had another person XXXX XXXX speak with me trying to put me in a hire loan rate, telling me the new loan rate would be 5.1 and an investor loan for an owner-occupied property. I asked why when I live in the property. I spoke with XXXX XXXX and asked for XXXX XXXX because I wanted to see if they would remove the hard inquiry on our credit reports since the loan could not be made as informed. XXXX XXXX informed me that XXXX XXXX was gone for the day." } Output: { "Issue": "Applying for a mortgage or refinancing an existing mortgage", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit card or prepaid card", "Sub-product": "General-purpose credit card or charge card", "Consumer complaint narrative": "I need help in getting the written dispute policy and procedure guidelines for the American Express Business Card. I continue to struggle getting a consistent and complete action when I inquire or dispute a merchant charge. There are times when I inquire OR dispute a charge and nothing is do ne. I continu e to have to make follow up calls to Customer Service after I see NO action is being done on their behalf. I feel they are giving me answers to simply get me off the phone as they are NOT following through on their words. This type of business practice has continued for years. I have asked American Express Customer Service many times for a copy of their written dispute policy and procedure. They will NOT provide me with this information. Is there also a 3rd part y representative that can assist me in making sure I am treated fairly and make sure their policies are followed." } Output: { "Issue": "Other features, terms, or problems", "Sub-issue": "Problem with customer service" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "on the XX/XX/2018. I deposited a check from my employer " XXXX XXXX XXXX XXXX '' for the amount of {$700.00}. there was a hold placed on my check for a period that exceeded 5 business days. i was negative in my account when i initialize the deposited in my account. so i had no funds to continue this new job i started. and the end result to that check was a much greater negative balance, and loosing my employer due to funds for transportation. so since i lost my employer and cant pay my bills an they added fees for insufficient funds i want to file a complaint." } Output: { "Issue": "Managing an account", "Sub-issue": "Deposits and withdrawals" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit card or prepaid card", "Sub-product": "Store credit card", "Consumer complaint narrative": "I contacted XXXX XXXX XXXX regarding making payment arrangments in XX/XX/2018, they gave me an arrangment of {$66.00} my normal due date is the XXXX of every month, they tried to pull the {$66.00} out on XX/XX/XXXX and that put my account into overdraft, i called the company and a supervisor told me that when the processed cleared on there end, to call back and they would reimburse me the fee, so i called back on XX/XX/XXXX to see if everything was ok on there end, and a supervisor told me that the other supervisor shouldn't have told me that and they can't honor it, so i asked her that since they won't honor it could i pay the {$66.00} as agreed by the representative that gave me that figure in the first place, she went on to say she dont see any notes as to where i was given that figure, she just flat out lied because why would they try to draft {$66.00} on the second so i just hung up." } Output: { "Issue": "Problem with a purchase shown on your statement", "Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "XX/XX/19 XXXX XXXX XXXX XXXX XXXX XXXX, CA XXXX SSN : XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX TRANSUNION and XXXX Account # XXXX XXXX XXXX XXXX XXXX, XXXX ( XXXX ) XXXX To Whom It May Concern : I AM RESPONDING TO A COLLECTIONS ACCOUNT ON MY 3 CREDIT REPORTS. This letter is regarding a negative inaccurate account which you claim I owe your company {$650.00}. I have never entered into any legally binding payment obligation with XXXX XXXX XXXX. The negative remark is not coming from XXXX XXXX, but from your company. I also have no contractual obligation to XXXX XXXX XXXX either. The original charge off from XXXX XXXX, was deleted from my reports and marked as inaccurate ( I am attaching the proof of this with this letter ) and was removed from my credit reports in XX/XX/2019. 1. This account was sold to a collections company who has never sent me any notice they acquired it or how they did so. The charge off date on this account is listed as XX/XX/2019, while in XX/XX/2019 XXXX XXXX was reporting this account incorrectly to the bureaus. The debt cant be owned by both companies. 2. This 3rd party collection company is claiming they are a factoring company as stated on my report. This is a false statement. Charged off accounts are not suitable to be factored and dont meet the requirements to call them guaranteed future income. 3. The reason XXXX XXXX XXXX called themselves a factoring company is so they could lie about the date of the charge off/last payment to make an old debt look new and have it been the most harmful to my credit score as possible. The date of purchase/ownership listed by this fraudulent company is XX/XX/2019. When I disputed this account while it was under XXXX XXXX, I received a letter from XXXX XXXX stating they had erroneously sold the debt to XXXX XXXX XXXX Please note, that your account was sold on XX/XX/2019, to XXXX XXXX XXXX Sincerely, XXXX XXXX, on behalf of XXXX XXXX. This is reason enough to remove this account from all 3 of my reports immediately. 4. This account was sold to a collections company who has never sent me any notice they acquired it or how they did so. This is a formal notice that your erroneous claim IS now disputed. I am requesting validation, made pursuant to the Fair Debt Collection Practices Act and the Fair Credit Reporting Act, along with the corresponding local state laws. Please provide me with the following : 1. PROOF OF PAYMENT AGREEMENT I MADE WITH XXXX XXXX XXXX NOT XXXX XXXX, which I have no knowledge of anyways. I want a wet ink signature between myself and your company proving I owe YOU this or any debt you have in my name. 2. What the money you say I owe is for? 3. Explain and show me how you calculated what you say I owe? 4. Provide me with copies of any papers that show I agreed to pay what you say I owe : 5. Provide a verification or copy of any judgment if applicable : 6. Identify the original creditor : 7. Prove the statute of Limitations has not expired on this account 8. Show me that you are licensed to collect in my state 9. Provide me with you license numbers and Registered Agent 10. Please note that I am requesting validation that is competent evidence bearing my signature, showing that I have ( or ever had ) some contractual obligation to pay you 11. Provide Me The names, addresses, and telephone numbers of each person who personally verified this alleged account, so that I can inquire about how they verified without providing any proof TO ME. 12. Provide the documented proof that you ever gave me notice of the charge off per FCRA LAW 623 ( A ) ( 4 ), 13. Provide the actual charge off date and as stated you must in FCRA LAW 623 ( A ) ( 5 ) A person who furnishes information to a consumer reporting agency regarding a delinquent account being placed for collection, charged to profit or loss, or subjected to any similar action shall notify the agency of the date of delinquency on the account, which shall be the month and year of the commencement of the delinquency on the account that immediately preceded the action 14. Be advised that the method of validation is hereby requested along with the procedure used to determine the accuracy and completenesss of the information is hereby requested. As per FTC opinion letter from Attorney General XXXX XXXX XXXX, you should be aware that a printout of a bill or itemized document does not constitute verification. Additionally, please provide the names, addresses, and telephone numbers of each person who personally verified this alleged account, so that I can inquire about how they verified without providing any proof, bearing my signature? Please also be aware that any negative mark found on my credit reports including ( XXXX, Transunion, XXXX ) from your company or any company that you represent, for a debt that I dont owe, is a violation of the FCRA & FDCPA ; therefore, if you can not validate the debt, you must request that all credit reporting agencies delete the entry. Pending the outcome of my investigation of any evidence that you submit, you are instructed to take no action that could be detrimental to any of my credit reports. Failure to respond within 5 days of receipt of this certified letter may result in small claims legal action against your company at my local venue. I would be seeking a minimum of {$1000.00} in damages per violation : Defamation Negligent Enablement of Identity Fraud Violation of Fair Debt Collection Practices Act ( including but not limiting to Section 807-8 ) Violation of the Fair Credit Reporting Act ( including but not limited to Section 623-b ) Please Note : This notice is an attempt to correct your records, and any information received from you will be collected as evidence should any further action be necessary. This is a request for information only, and is not a statement, election, or waiver of status. Thank You, XXXX XXXX" } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Account information incorrect" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I received a letter stating the information sent to the credit bureaus was correct, but they did not provide me with the documentation to verify the late payments. According to the FCRA as a consumer it is my right to receive supporting documentation if the late payments were in fact verified which has not been provided. Provide me with documentation of these late payments to address provided. I have also provided proof of FCRA violations if a resolutions in not agreed upon." } Output: { "Issue": "Problem with a credit reporting company's investigation into an existing problem", "Sub-issue": "Problem with personal statement of dispute" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "My balance was a positive and then they charged an overdraft fee" } Output: { "Issue": "Problem caused by your funds being low", "Sub-issue": "Overdrafts and overdraft fees" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Money transfers", "Sub-product": "Domestic (US) money transfer", "Consumer complaint narrative": "I sent money to my cousin who is in prison, I do not have the reference no. of the first transaction but it was back in XXXX back then I had confirm all the information concerning my relationship with beneficiary. On XXXX XXXX and XXXX of XX/XX/XXXX I sent him money ref no. XXXX & XXXX my cousin was waiting for the funds and he had not received it ref no. XXXX, I contact the customer services which informed a representative informed me that the transaction was under review. She ask mw what was my relationship with beneficiary and then released the funds.after informing that beneficiary was my cousin. Now on XXXX XXXX, XX/XX/XXXX the same thing happen, the whole day pass and my cousin had not received the funds. I called customer services again and the representative informed that the received had to call them. I told him that if had receiver my money transfer and informed in that this was going to someone in prison. He asked me to be placed on hold and the informed that funds were released. I do n't know what money grams employees are doing there is no reason for my transaction to be on hold if they have confirm the information with me. this is a frustrating situation. I will NEVER use money gram again. bad services and made me waste my time." } Output: { "Issue": "Other service issues", "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 XXXX XXXX account XXXX and XXXX XXXX XXXXXXXX, have violated my rights. 15 U.S.C 1681 section 602 A, states I have the right to privacy. 15 U.S.C 1681 section 602 A section 2, also states a consumer reporting agency can not furnish an account without my written instructions." } Output: { "Issue": "Improper use of your report", "Sub-issue": "Reporting company used your report improperly" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Other debt", "Consumer complaint narrative": "A leasing company in XXXX Colorado forced me to sign in to a visitorlease that only lasted a month and all visitors were supposed to fill it out. While visiting a friend at his apartment complex. When I signed the paperwork so I could park my vehicle at the property without it being towed away. Come to find out the paperwork they MADE me sign was attaching me to the actual lease and now are charging me for 4 months of a lease. I was there for a week." } Output: { "Issue": "Attempts to collect debt not owed", "Sub-issue": "Debt is not yours" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "In my previous complaints, I submitted a request to block items on my consumer report as I am a victim of identity theft. This was not a dispute, however, the FCRA has chosen to forgo Federal regulation and apply their company policy to treat this as a dispute. In fact, when I called them, they stated that their policy supersedes Federal law ( which states ) ( a ) Block A consumer reporting agency should block the reporting of any information in the file of a consumer that the consumer identifies as information that resulted from alleged identity theft, not later than 4 business days after the date receipt by such agency of- ( XXXX ) appropriate proof of the identity of the consumer ; ( XXXX ) a copy of an identity theft report, ( XXXX ) the identification of such information by the consumer, and ( XXXX ) a statement by the consumer that the information is not information relating to any transaction by the consumer. I submitted proof that my social security number, fingerprints, birthdate, addresses, and other identifying information were affected by the XXXX XXXX data breach as well as XXXX 's XXXX data breach. I submitted all the required documents per 15usc1681c and yet the CRA has failed to block this information and it has been over 4 days. Again, this is NOT A DISPUTE. XXXX XXXX XXXX .... Balance : - XXXX XXXX XXXX. Balance : - XXXX XXXX XXXX XXXX .... Balance : - US BKPT CT CA XXXX XXXX Identification number XXXX XXXX filed XX/XX/XXXX Status XXXX XXXX bankruptcy discharged." } 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": "The Credit bureaus stated the accounts listed below was properly investigated but how is that possible if the open date is inaccurate, the date last active is inaccurate, date last reported is inaccurate, two-year payment history is inaccurate, account status is inaccurate, past due balance on a charge off account is inaccurate. This is grounds for removal. Account Name : XXXX XXXX XXXX # XXXX Account Name XXXX XXXX Account # XXXX Account Name : XXXX XXXX XXXX Account # XXXX Account Name : XXXX XXXX XXXX Account # XXXX Account Name : XXXX XXXX XXXX Account # XXXX They also violated my rights under 15 U.S.C 1681 section 604 A Section 2 : The law clearly states a consumer reporting agency can not furnish an account without my written instructions. They also violated my rights under 15 U.S.C 1681 Section 611 ( 2 ) ( a ) : The law clearly states the credit bureaus have 5 days to notify me from when they receive my disputes. They also violated my rights under 15 U.S.C 1681 Section 611 ( 5 ) ( a ) ( I ) ( ii ). The law clearly states if items are found inaccurate or can not be verified the consumer reporting shall promptly delete the information from the file of the consumer." } Output: { "Issue": "Problem with a credit reporting company's investigation into an existing problem", "Sub-issue": "Their investigation did not fix an error on your report" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "On or about XX/XX/2021, I spoke to an Equifax supervisor regarding a creditor not displaying on my credit file. The supervisor advised as a result of a complaint against Equifax, Equifax contacted XXXX XXXX XXXX XXXX to confirm whether or not the credit information was to appear from my file. The supervisor advised between XX/XX/XXXX and XX/XX/XXXX, XXXX XXXX stated to delete open and active accounts from my credit report. The supervisor stated he along with two additional departments he would work to have the accounts placed back on my credit report. He also stated he would investigate and contact me today. After, not hearing from the supervisor, I contacted Equifax, and requested to speak to a supervisor. XXXX, ID # : XXXX took the call. XXXX informed me the previous supervisor did not document the matter ; rather the notes, according to XXXX stated to escalate the matter. XXXX, refused to tell me the department in which the matter was escalated to. All XXXX continued to ask me for 40 minutes what did I want to do? I over and over again advise him to read the notes. XXXX continued to state " there were no notes ', 'and if I had a reference number ''. For 45 minutes, XXXX, Equifax Supervisor would not assist me in resolving the matter. I continued to tell him I wanted the accounts to be placed back onto my account ; he needed to take the dispute and contact XXXX XXXX to determine what reason they deleted my accounts. XXXX refused to take a dispute ; stating, " you need to contact the creditor ''. I advised I did. I ended up disconnecting from the call. I have never experienced the blatant disregard from a supervisor I did from XXXX. His refusal to gather information, to look into the matter, take a dispute if needed, or simple research the notes was unprofessional and disrespectful to me and his duty. I disconnected the call without the matter being resolved. To date, Equifax has not fixed the false information or investigated the false information reported to Equifax, as a result, the account is not displaying on my report. Their action has caused an injury to my credit score. The aforementioned is an ongoing matter with XXXX XXXX XXXX XXXX XXXX. It Equifax and XXXX XXXX are working together to dismantle all the hard work I have done to improve my credit score." } 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": "Store credit card", "Consumer complaint narrative": "Identity Fraud Home Depot XXXX, TN XX/XX/2022 {$5000.00} My identity was stolen. A cc card was issues to someone other than me- identify unknown. I have contacted Home Depot. They told me that the SS # was invalid but they still gave the credit. Home Depot case # XXXX. I have also filed a XXXX, TN Police report case # XXXX." } Output: { "Issue": "Problem with a purchase shown on your statement", "Sub-issue": "Card was charged for something you did not purchase with the card" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Other mortgage", "Consumer complaint narrative": "I am a short sale negotiator for this loan, which is in bankruptcy and they are required to only have that department speak with me. And so the problem is that vendor is not answering their phones or calling to give updates on the file. An appraisal was completed over 3 weeks ago and there has still has not been a response. I have also requested in writing for the name of the investor and they have not responded or provided me with that information as well. There could be foreclosure sale and we would have no idea." } Output: { "Issue": "Loan modification,collection,foreclosure", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Bank account or service", "Sub-product": "Checking account", "Consumer complaint narrative": "Wells Fargo refuses to increase my daily ATM withdrawal from {$310.00} to {$1000.00}. I am in XXXX for work and unable to pay bills in a timely fashion with cash. There is plenty of money available but they want their fees. Disgusting company." } Output: { "Issue": "Using a debit or ATM card", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "XXXX actions contravene the Federal Trade Commission Laws. The company has denied my dispute request for an account I have no knowledge of." } 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": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "e-mail toXXXX XXXX XXXX Ma'am, Did I or did I not walk into your branch about two months ago and sign up to have my account statements for XXXX delivered to me via slow mail on paper? This is the second month I have not gotten my paper statement for last month. Please fix it. I will not ask nicely again. Nor will I wait for years, as I did with your online mess. The next time I will take out an ad in the legal section of the XXXX XXXX, the month after that, I will take out a column ad, and the month after that a half-page ad, and the month after that a full page ad. In each one I will explain to the public that your bank is either incompetent or discriminating against me personally. That your bank has long refused to give me reliable online service, requiring me to repeatedly sign in while going around in circles on the login page - then lied about why, offering up ridiculous IT excuses that don't pass the smell test. Then when I sign up for paper statements, refuses to send them, making me ask every month. Eventually, I'll get inventive and start putting videos about it on XXXX. I'm going to ask in speculation if this is the XXXX XXXX XXXX XXXX XXXX XXXX XXXX messing with someone who is not XXXX. Or entirely XXXX. Yet. I shall also send repeated complaints to the CFPB, citing "your complaint XXXX about Arvest Bank." Then, when that's all said and done, I'm going to get a lawyer and sue your bank in court, for lawyer's fees and the sheer aggravation of being forced to move my accounts, savings and automatic deposit(s) to another bank. Because surely I could not trust your bank to facilitate that without causing me significant financial grief. It would seem to need a court order and sufficient financial penalties for fraud or failure. After all, you also promised me online service and paper statements. If you should think that your bank can mess with me like this and then expect me to be satisfied with "arbitration", please think again. Sincerely, XXXX XXXX XXXX" } Output: { "Issue": "Managing an account", "Sub-issue": "Deposits and withdrawals" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Money transfer, virtual currency, or money service", "Sub-product": "Mobile or digital wallet", "Consumer complaint narrative": "PayPal closed. Y disputed claim and stated they sent me an email on XX/XX/XXXX to escalate the complaint within 20 days. The e mail was not received in my inbox." } Output: { "Issue": "Unauthorized transactions or other transaction problem", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "On XX/XX/2019, I contacted Equifax because I'm applying for a mortgage but the current address has not been updated on my Equifax report. I have lived at this address for 1.5 years and it is correctly listed with the other credit reporting agencies. I was still that they were not able to help me and to fax a copy of my social security card along with my current driver 's license to them. I promptly faxed both along with a cover letter with the reason for the submission being that the address needs to be updated on my Equifax report. I was told that it would 5-7 business days for the information to be updated. On XX/XX/XXXX, I received a notice from Equifax dated XX/XX/XXXX that they received my documents but are not able to update my information because they can not find a current file for me. I called Equifax to try to correct the information but the customer service agent was not able to access the system due to a " system update '' and to call back in 30-60 minutes. I attempted to call again but the system was still unavailable. I tried calling again on XX/XX/XXXX and was told that they were still experiencing a system update. I attempted to call again on XX/XX/XXXX and spoke with a representative. She said that they received the fax but that my social security card is not valid and does not match their records. I requested to speak with a supervisor. The supervisor said that they are experiencing a system update and she can not access my account. I requested another supervisor and she retrieved the documents, attached them to my credit report, and updated my current address manually. She said that it will now be 30 days before the information is reflected on my Equifax report. This call took 45 minutes. Since beginning this process I have spent close to 40 hours on the telephone attempting to have this information updated. Just to speak with a representative each time it takes XXXX in the automated system and I'm often disconnected before speaking with anybody. The solutions offered are nonexistent, repetitive, and often incoherent. I've been told repeatedly that I just need to resubmit the documents or that the documents submitted were invalid. When I've asked to speak to a supervisor, multiple times it has been the same person using a different name. I would just like this information updated in a timely fashion so that I can attempt to buy a home." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information is missing that should be on the report" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "I do not know", "Consumer complaint narrative": "On XXXX XXXX 2015. the CFPB and Encore Capital Group , owners of Midland Funding LLC entered in a Consent Order. In XXXX XXXX not one judge is honoring this Consent Agreement and lawyers for Midland are still suing not only in the state courts but also in the federal district court in violation of the Agreement. What can be done to enforce this ORDER??" } Output: { "Issue": "Improper contact or sharing of info", "Sub-issue": "Contacted me instead of my attorney" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I sent a letter on XX/XX/XXXX to 3 credit bureaus to remove erroneous items that are UNKNOWN to me. I came across this law today and according to the Per FCRA 605B ( 15 U.S.C. 1681c-2 ) the credit bureaus are required to remove any accounts or information not later than 4 business days after the date of receipt. Ive already included this in my previous Complaint # XXXX, XXXX, and XXXX & I am including it again., XXXX XXXX XXXX Balance : {$1500.00} XXXX XXXX XXXX Balance : {$0.00} ; XXXX XXXX Balance XXXX {$0.00} XXXX XXXX XXXX XXXX Balance : {$990.00} XXXX XXXX XXXX Balance : {$220.00} ; XXXX XXXX Balance : {$110.00} ; XXXX XXXX XXXX Original Creditor : XXXX XXXX XXXX XXXX XXXX ) XXXX Balance : {$1100.00} ; XXXX XXXX XXXX Balance : {$770.00} ; XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX ) XXXX Balance : {$710.00} ; XXXX XXXX XXXX XXXX XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX, MO XXXX XX/XX/XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX, MO XXXX XX/XX/XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX, MO XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX, MO 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": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "In XX/XX/XXXX, I opened a BMO Harris checking and savings account. I received the bonus as advertised on the savings account, but did not receive the bonus on my checking account. The bonus was in the amount of {$300.00} and was to be paid within 120 days. It is now XX/XX/XXXX, and the bonus was not paid. XXXX XXXX XXXX via chat on XX/XX/XXXX verified that my account met the requirements. Shortly after, I called the number provided and was told to wait a little longer for the bonus to post. I have waited for the statement to close, and the bonus is still not posted. Chat attached." } Output: { "Issue": "Opening an account", "Sub-issue": "Didn't receive terms that were advertised" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "Please see the following accounts appearing on my credit file by Transunion, XXXX and XXXX : 1. XXXX XXXXXXXX - # XXXX 2. XXXX XXXX XXXX # XXXX 3. XXXX XXXX XXXX XXXX - # XXXX These accounts are a violation of my federally protected consumer rights. And In accordance with the Fair Credit Reporting Act under 15 U.S.C. 1681 section 602 A states : I have the right to privacy.15 U.S.C . 1681 section 694 A Section 2 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": "Problem with a credit reporting company's investigation into an existing problem", "Sub-issue": "Their investigation did not fix an error on your report" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "I tried to call Wells Fargo on Saturday XX/XX/2021 to close my account. The representative who I spoke with would not close the account, and gave me a difficult time. He repeated a statement regarding a third party several times, and would not assist me with closing the account. My wife was in the background assisting me, but I did not give him permission to speak with her regarding the closure of the account. I feel like this is a delay tactic that this bank is using. They make it very difficult to close accounts. I want this account closed immediately." } Output: { "Issue": "Closing an account", "Sub-issue": "Can't close your account" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I want to state that this is complaint is from the Actual Consumer XXXX XXXX and not a third party of any kind. I looked at my TransUnion credit report recently and found an unauthorized inquiry reporting on my credit report. I wrote TransUnion a letter on XX/XX/XXXX to try to get this inaccurate information deleted from my credit report since it is hurting my credit profile and costing me money in higher interest rates. Much to my dismay TransUnion wrote me a letter back stating that " we wont process your request ''. ( because they think that I did not write the letter of dispute myself ). I am a XXXX XXXXXXXX with very XXXX XXXX and i am able to write my letters of dispute without need for a third party. TransUnion is not taking these inaccuracies on my credit report seriously and they are taking my rights for granted, Please assist. COMPANY : XXXX XXXX INQUIRY DATE : XX/XX/XXXX ADDRESS : XXXX XXXX XXXX XXXX XXXX XXXX XXXX CA XXXX Attached is the letter i wrote to TransUnion and the letter TransUnion wrote back to me refusing to process my dispute. i have attached my drivers license as proof of Identity and my phone and internet bill as proof of address so there are no questions about my identity." } Output: { "Issue": "Improper use of your report", "Sub-issue": "Credit inquiries on your report that you don't recognize" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I am writing to delete the following information on my file. The items I need deleted are listed on my report. I XXXX be a victim of identity theft and did not make the charge. I ask that the items be deleted to correct my credit report. I reported the theft of my identity to the Federal Trade Commission and I also have enclosed copies of the Federal Trade Commission 's Identity Theft Affidavit. Please delete the items as soon as possible." } 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": "XXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX I went to try to buy a car and the dealership kept pulling my credit even when I was not there." } 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 was in the process of applying for new employment and it was brought to my attention that there is some inaccurate information reporting on my credit report.I received a copy of my credit report and to my surprise I saw all these inaccurate items that are not correct. By the way I hope that this does not hinder my ability to obtain employment due to this inaccurate information showing. I was advised by a legal friend of mine that under : 15 US Code 1681a section 603 ( k ) ( b ) ( ii ) can be considered an adverse action. 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 1692C Without the prior consent of the consumer given directly to the debt collector or the express permission of a court of competent jurisdiction, a debt collector may not communicate with a consumer in connection with the collection of any debt. TransUnion XXXX : XXXX XXXX XXXXXXXX XXXX Addresses : XXXX XXXX XXXX XXXX, CA XXXX XXXXXXXX XXXX XXXX XXXX XXXX XXXXXXXX, AZ XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Name : XXXX XXXX XXXX XXXX Employer : XXXX XXXX XXXX XXXX XXXX XXXX XXXX, CA XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX AZ XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX, AZ XXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX Addresses : XXXX XXXX XXXX XXXX XXXX XXXX AZ XXXX XXXX XXXX XXXX XXXX XXXX, AZ XXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX, AZ XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX" } 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 filed a CFPB compliant against XXXX XXXX. They said it isn't them that is misreporting the data. Yes, a foreclosure was started by XXXX XXXX in XX/XX/XXXX but, the house was short-sold in XX/XX/XXXX and it never foreclosed. Experian is showing in the 24-month payment history a Charge off status XX/XX/XXXX. This is an error. You can see the other 2 bureaus do not report this information. This issue is causing me a mortgage decline because my lender says my house was foreclosed not short sold which is wrong." } 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": "This is my ANOTHER endeavor to tell CREDIT BUREAUS that I am a victim of identity theft and to complain specific records in my document because of their 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. Per Section 605b of the FCRA CREDIT BUREAUS are required by law to remove any ITEMS or information which is found to be opened due to identity theft. XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX {$5100.00} XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX" } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information belongs to someone else" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Conventional home mortgage", "Consumer complaint narrative": "We paid off our mortgage loan on XX/XX/2019. One day before closing, the mortgage holder, XXXX XXXX XXXX, withdrew {$9800.00} via automatic transfer from our checking account for the XXXX monthly payment. In the latest correspondence from SPS, dated XX/XX/XXXX, they refused to refund the unwarranted XXXX payment." } Output: { "Issue": "Trouble during payment process", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Money transfer, virtual currency, or money service", "Sub-product": "Virtual currency", "Consumer complaint narrative": "I have an account open with the company Coinbase where I bought {$300.00} of bitcoin at the price of {$9600.00} per coin, so I received XXXX bitcoins. I noticed that on XXXX XXXX, when I tried to log into my account it said " your account is temporarily disabled '' and I was unable to log in to my account. I contacted the Coinbase customer service line, XXXX ( XXXX ) XXXX and spoke with a representative who said " Some red flags were raised on your account, but I do not know what they are, I have forwarded your case on to a specialist who will work with you to reopen the account ''. Since then I have received no contact from the website that would indicate that they are working on my case. I called again today XXXX XXXX, and spoke with another representative " XXXX, '' he said that he raised the priority of my case and that I would receive some type of response before Monday. It is a bitcoin exchange, and due to the time sensitive nature of fluctuating bitcoin prices, I need to be able to log in if I want to sell my bitcoin. I have read on the internet that many people have this problem ( i.e. " account temporarily disabled '' and Coinbase taking multiple weeks or months to unlock their accounts ). I find it highly distressing that Coinbase is not working to resolve this issue expeditiously. And I stand to lose money if the price of bitcoin goes down and I am not able to sell my units." } Output: { "Issue": "Other service problem", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Bank account or service", "Sub-product": "Checking account", "Consumer complaint narrative": "DISCOVER BANK IS UNFAIR! THEY PROMISED TO SEND ME FORMS TO DISPUTE A FRAUD THAT XXXX DID ON MY BANK ACCOUNT! I NEVER RECIEVED THE AFFIDAVIT AND DISCOVER IS CLAIMING THE CASE WILL BE CLOSED IN THE FAVOR OF THE MERCHANT BECAUSE I DID NOT SEND THE FORM! I NEVER RECIEVED THE FORM TO BEGIN WITH!" } Output: { "Issue": "Using a debit or ATM card", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "TODAYS DATE:XX/XX/2020 XXXX, XXXX XXXX SOC SEC # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX XXXX, XXXX XXXX, FL XXXX Equifax XXXX. XXXX XXXX XXXX XXXX, GA XXXX XXXX XXXX XXXX XXXX. XXXX XXXX XXXX, Texas XXXX XXXX XXXX XXXX, XXXX. XXXX XXXX XXXX, PA XXXX XXXX. I recently submitted a request for investigation of an Acct Number : XXXX XXXX Reported : XX/XX/2020 XXXX/XXXX Reported : XX/XX/2020 Ive submitted enough information regarding the fraudulent account thats open in my name I have provided information for your company to have carried out a reasonable investigation of this dispute. If you had investigated properly rather then using your e-Oscar system you would have noticed that this account is not mine which your companies have claimed to Verified. Since youve obviously neglected to investigate this account thoroughly I am demanding you remove this account off my profile. It is at this time that I will point out the in Cushman v TransUnion, Stevenson v. TRW ( Experian ), and Richardson v. Fleet, Equifax et, the courts ruled each and every time that the CRA couldnt merely Parrot information from the creditors and collection agencies that they have conduct an independent REASONABLE investigation to ensure the validity of the debt and honesty/integrity of the creditors/CA in question. Sending out a generic form through the e-Oscar system that doesnt even contain my reasons for the dispute is not reasonable. If you dont initiate an investigation regarding my dispute, as it is my right under the Fair Credit Reporting Act, I will have to take legal action to protect my credit rating and myself. Which Im sure you are aware each violation of the Fair Credit Reporting Act allows damages of {$1000.00} should this matter ends up in court. I look forward to an expedite resolution of this matter Thank you. XXXX, XXXX XXXX SOC SEC # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX XXXX, XXXX XXXX, FL XXXX" } Output: { "Issue": "Problem with a credit reporting company's investigation into an existing problem", "Sub-issue": "Their investigation did not fix an error on your report" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Medical debt", "Consumer complaint narrative": "I have 3 collections accounts being reported to my bureau : Lockhart Morris and Mont XXXX XXXX XXXX XX/XX/2020 XXXX Lockhart Morris and Mont XXXX XXXX XXXX XX/XX/2020 XXXX Lockhart Morris and Mont XXXX XXXX XXXX XX/XX/2020 XXXX I have asked numerous times for validation of debt and proof this is owed by me, as I believe it is an error. I have yet to receive any information- each time I dispute thru the bureau 's the debt is transferred to a different company with still no validation of debt." } 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": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "XXXX XXXX XXXX certified to experian a collection account that was not mine. this is fraud. they collected my personal insurance information at the time of service, refused to bill them and then sent me to collections with no notification. This is a direct violation of the FCRA. XXXX and Experian are allowing them to do it." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Account information incorrect" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit card", "Sub-product": "Not Available", "Consumer complaint narrative": "I 've deposited {$1800.00} in order to have a secured credit card with First Progress Card. After few years I decided to close the account, I 've called XX/XX/2017 to close and ask for the deposit refund, at that time I was informed it should take up to 7 weeks which is ridiculous. It 's Seven weeks already and I did n't receive the refund, now I 've told that is up to 10 weeks!!!" } 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": "XXXX refuse to remove accounts from my report that was not authorized by me. The XXXX XXXX XXXX and XXXX XXXX. XXXX XXXX XXXX Has been on my report for over 7 years and they still refuse to remove it off my credit report. Also there are several inquiries which I did not give consent to fringe upon my credit report those being XXXX XXXX, XXXX XXXX, XXXX, XXXX, XXXX, and XXXX" } Output: { "Issue": "Improper use of your report", "Sub-issue": "Reporting company used your report improperly" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "XXXX XXXX XXXX XXXX is reporting collection account for {$11000.00} past due as of XXXX XXXX. I disputed with reporting agencys this is not my account. I was not in Country during this time. They are reporting joint with XXXX XXXX. I have filed disputes requesting documentation this is my account, I have not received. I have sent documentation showing such. Reporting agencys continue to mark as remain." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Account status incorrect" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Vehicle loan or lease", "Sub-product": "Loan", "Consumer complaint narrative": "Purchased a new vehicle and was financed through BBVA Compass in XX/XX/2015. Ran into a hardship a little bit after purchasing the vehicle. I was denied help for the longest due to it being a new loan. This loan has damaged my credit tremendously. I have almost close to {$1200.00} dollars in late fees which should be against the law. Just recently they did grant me an extension due to me owing the IRS." } Output: { "Issue": "Struggling to pay your loan", "Sub-issue": "Denied request to lower 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", "Sub-product": "Not Available", "Consumer complaint narrative": "Equifax is not allowing me to see my credit file and my credit score because they put my credit file in the consumer affairs department." } Output: { "Issue": "Unable to get credit report/credit score", "Sub-issue": "Problem getting 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": "Debt collection", "Sub-product": "Other debt", "Consumer complaint narrative": "I was fraudulently sold an XXXX XXXX XXXX phone at a local XXXX on XXXXXXXX XXXX. I only use the inexpensive XXXX XXXX XXXX XXXX mobile service. I wanted the {$190.00} XXXX under this plan. I made many attempts to resolve this with XXXX XXXX XXXX customer service representatives. Finally, on XXXX XX/XX/2022 account, XXXX, associated with XXXX cell number XXXX, was cancelled by an XXXX XXXX XXXX fraud representative. A prepaid mailing card was sent to me st the same time. The tracking label shows XXXX XXXX XXXX received the XXXX on XXXX XX/XX/2022. XXXX XXXX XXXX still illegally accessed my bank account several times after XXXX XXXX taking {$94.00} out twice and {$120.00} one occasion. My banks fraud department recovered all the unauthorized XXXX XXXX XXXX debits to my account. In early XXXX, I received a notice from the CREDENCE RESOURCE MANAGEMENT LLC, a debt collection agency. They are trying to collect {$910.00} from me for an XXXX XXXX XXXX XXXX account ( # XXXX ) that was closed on XXXX XX/XX/2022. I called Credence after receiving their letter to dispute the debt for an account that had been closed and a {$910.00} phone that had already been returned on XXXX XXXX. These facts were ignored, and their spokesman told me that if I did not pay, it would ruin my credit. That I had to pay them the money. A second call was a repeat of the first. I was given 30 days to send them money I did not owe XXXX XXXX XXXX. XXXX XXXX XXXX continued to harass me with calls from " The President of XXXX XXXX XXXX XXXX Office. Voicemail messages sounded like it was a teenager calling. XXXX XXXX XXXX also has deliberately made it difficult for me, still threatening me to pay {$30.00} a month, for 5 years, until they receive complete payment for a phone already in their possession. CREDENCE has sent me two forms with a deadline, or it will ruin my credit. This is complete fraud. Texas Attorney 's Office has sent me a form to report CREDENCE. As well as XXXX XXXX XXXX. I do not need the intimidation that is being used to take money from me due to a fraudulent sale by an XXXX XXXX XXXX employee. Essentially he tried to take {$1300.00} without authorization. I never signed anything, it was purely bait and switch. I need help with CREDENCE. There is no reason for them to try to intimidate me into paying." } Output: { "Issue": "Took or threatened to take negative or legal action", "Sub-issue": "Threatened or suggested your credit would be damaged" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "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 1681XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, and 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 reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "Colony/Midnight XXXX XXXX ( XXXX XXXX XXXX XXXX, WI XXXX ) opened an account in my name for someone who fraudulently obtained my information on XX/XX/2015. Account charged off. {$990.00} written off. {$240.00} past due as of XXXX XXXX was never aware of this company before seeing derogatory information from them in my credit report. I have disputed this multiple times with XXXX with no results. If this is my account I want to know what was ordered, and where it was shipped as it appears this card is specifically for online shopping stores." } 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": "Several months ago I received a solicitation call from Amex with an offer of XXXX membership rewards points if I upgraded one of my Amex business cards to Amex Plat. That salesperson explicitly and directly told me that I could " downgrade back down to a lesser Amex card once the annual fee hit. I questioned that and they confirmed -- there's not the slightest doubt in my mind I was told that. Another Amex rep just under a month ago, said I " should wait till the account renews in XX/XX/XXXX '' before downgrading. They now have pulled the XXXX and said I needed to stay w/ the Plat for 1 year ( and, hey, not really all that unreasonable, ) but the sales team ( or whatever they are called ) definitely, definitely, definitely lied to me when " selling '' the upgraded card back in XX/XX/XXXX or whenever that was." } Output: { "Issue": "Advertising and marketing, including promotional offers", "Sub-issue": "Didn't receive advertised or promotional terms" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "i contacted a phone number because i wanted to get the first time home buyer grant they told me to call another number i did and they asked me for my id and ssn after that i received an email saying that they approved me for a home loan but nothing about the first time home buyer program they checked my credit score and my wife credit score without our authorization we are allready qualified with a lender i dont understand why this people pulled my credit if we didnt ask for the we wanted to get the grant for the money for first time home buyer they lie to us so i want to make a complaint if you can contact me in XXXX will be great" } Output: { "Issue": "Improper use of your report", "Sub-issue": "Reporting company used your report improperly" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting", "Sub-product": "Not Available", "Consumer complaint narrative": "I understand that TransUnion 's systems experienced and system glitch and old inquiries were re-added to credit reports. I 'd like to have those inquiries removed, I can not get through on their toll free number. I held for almost 2 hours." } Output: { "Issue": "Incorrect information on credit report", "Sub-issue": "Reinserted previously deleted info" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I was informed by my banker ( 20 years ) that I was not approved for a personal loan because of derogatory items on my credit. After I pulled my credit for myself, I notice suspicious accounts. Please remove these fraudulent accounts from my report as soon as possible. XXXX XXXX XXXX XXXX {$20000.00} XXXX XXXX XXXX XXXX XXXX {$1100.00} XXXX XXXX {$31000.00} XXXX XXXX {$0.00} XXXX/XXXX XXXX {$400.00} XXXX/XXXX XXXX {$240.00}" } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information belongs to someone else" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Credit card debt", "Consumer complaint narrative": "RE : XXXX XXXX XXXX XXXX XXXX Corporate Headquarters : XXXX XXXX XXXX XXXX XXXX XXXX, XXXX XXXX XXXX, Utah XXXX ( XXXX ) XXXX XXXX, XXXX. Global Headquarters XXXX XXXX XXXX XXXX XXXX, PA XXXX XXXX. XXXX : Date Approved : XX/XX/XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, SC XXXX ( XXXX ) XXXX On XX/XX/XXXX I opened a XXXX XXXX credit card online and purchased a XXXX XXXX XXXX toothbrush for {$79.00} and another item that was returned. I received a credit card number online at point of purchase with a credit limit of {$1900.00}. I never received an actual credit card, however. In XXXX, I received an initial bill with {$6.00} due on the first payment as a result of the return. At this time, I attempted to establish an online account to make payments, as I had with all my other recurring credit card, utility, mortgage, and other accounts. However, contacting and working with XXXX was an unusually confusing and unprofessional experience where I was referred to various departments and back and forth between XXXX and XXXX. Finally, I was told that neither XXXX nor XXXX could validate my account or even who I was, and had no record of my credit card account number. Frustrated with this ridiculous process and waste of time, I set the bill and issue aside thinking that it would be resolved at the next billing cycle or when I received my credit card. Unfortunately, as far as I recall, I never received another hardcopy billing statement from XXXX and promptly forgot about the account and the small payment due. I never heard from XXXX again, nor XXXX XXXX. However, on XX/XX/XXXX of XXXX, while reviewing one of my credit reports, I noticed a fraudulent address on the report : XXXX XXXX XXXX, # XXXX, XXXX, MI XXXX. This was listed as my residence since XX/XX/XXXX ( The proximate time when the XXXX account was opened by me and the first bill received ). I live in Nebraska and have no other residences or addresses. I notified XXXX of this and they promptly investigated and removed the address from my credit report as FRAUD. XXXX EXHIBIT D ) On this same credit report, there was also posted notice of a collections account for {$290.00} from a debt buyer, LVNV Funding LLC/Resurgent Capital Services at XXXX. XXXX XXXX XXXX, SC XXXX XXXX XXXX ). Along with this account was posted notice that the account had been charged off by XXXX XXXX on XX/XX/XXXX. This was all news to me since I had not received billing statements from XXXX XXXX or XXXX, had received no correspondence or calls about a late payment or that the account was in danger of being charged off, and did not receive the legally required Notice of Assignment ( NOA ) under Section 136 of the Law of Property Act from XXXX XXXX to inform me that the account had been assigned to the debt buyer LVNV. On XX/XX/XXXX, I contacted LVNV at the number on the report. I spoke with XXXX. I explained to her that I had no idea who LVNV was or why they had put a collections notice on my credit reports, stating that I owed {$290.00}. The only charge I had made on XXXX was for a toothbrush at {$79.00}. XXXX said that the balance was probably from late fees and interest, since the card appeared to still be open. I explained that I had received no bills or notice of this, so would not be paying any such fees. I also explained that under FDCPA, LVNV is required to provide written notice of the debt and collection action, and of my right to obtain validation of the debt ( 15 U.S.C. 1692 ( g ) ( a ) ; 209 C.M.R. 18.16 ), and can not under law, without incurring liability, place a collections notice on a debtors credit reports. This was clearly illegal and damaging. I never received any communications from LVNV, no communications or billing from XXXX or XXXX XXXX, no opportunity for debt validation, and did not know who you were until I saw your company listed on my XXXX report on XX/XX/XXXX. XXXX reviewed my account and at first thought that my address was the fraudulent address in Michigan, which apparently was where my bills and information was being sent. When I explained to her that this was a fraudulent address and had nothing to do with me, and that XXXX had removed it, she seemed to understand the problem I had been having with the account due to a wrongful address and/or credit fraud. XXXX told me she was going to file a dispute and that I would be hearing from them. At that time, I also called XXXX XXXX but was unable to get anyone to listen to my concerns since the account had been charged off and was with LVNV. The Bank personnel referred me to LVNV to address my issues. On or about XX/XX/XXXX, I received a letter with attachments from LVNV/Resurgent sent on XX/XX/XXXX stating that the enclosed information was a verification of my debt and a demand to pay {$290.00} ( EXHIBIT A ). Enclosed was a summary of the debt and original creditor, XXXX Bank, XXXX. XXXX XXXX, XXXX, GA XXXX, who had charged off the account on XX/XX/XXXX for {$290.00}. Also was attached a billing statement from XXXX XXXX date XX/XX/XXXX, the date of charge off. Ironically, the address on the billing statement was the fraudulent address in Michigan with my name at the top : % XXXX XXXX @ XXXX XXXX XXXX XXXX XXXX, XXXX XXXX, XXXX, MI XXXX. ( SEE EXHIBIT A ) Here, it is clear by their own fact statement on their billing statement that XXXX XXXX was sending my billing statements and correspondence to a fraudulent or wrong address in Michigan. However, in bad faith, LVNV/Resurgent made no effort to address this error, as had been promised by XXXX on XX/XX/XXXX, and to stop its collections efforts, despite my sending them two letters and substantial documentation to prove that they were in the wrong and should address this issue with XXXX XXXX to remove the charge off from my credit reports ( EXHIBITS E & F ). LVNV also made no effort to remove the illegally placed collections notice on my credit report but persisted in collections and in damaging my credit. Finally, as a result of this situation I reviewed my information regarding XXXX and found that on XX/XX/XXXX I had received at my real address in Nebraska correspondence from XXXX XXXX, XXXX : XXXX XXXX XXXX, XXXX XXXX, IA XXXX ( EXHIBIT B ). This was a notice that my account with XXXX was past due but could be brought current and paid in full for {$39.00}. However, the account number on the notice from XXXX did not match the account number I had received from XXXX in XXXX of XXXX, so I had set it aside thinking it was a scam. Finding this notice again, I called XXXX XXXX. XXXX and spoke with a representative. She knew nothing about LVNV/Resurgent or any charge off from XXXX XXXX or collections action. She also said that the account number on their billing statement did not match my account number but that this was their own accounting number for my actual account from XXXX. I asked her if I could still pay this balance due for the XXXX account and she affirmed this and told me how to find my Nationwide account online. As a result, I went online and paid the {$39.00} balance due to bring my XXXX account current. The payment was retained by XXXX and withdrawn from my bank ( EXHIBIT C ). Proof of this payment was also forwarded to LVNV/Resurgent but made no difference to them since they only cared about extorting money from me. The payment was also made to XXXX via XXXX but was also not acknowledged by removing the charge off. Under law, since I never received a Section 136 NOA, any payment made to the Assignor, here XXXX XXXX, must be accepted and be removed from your accounts balance due. Under this law and error, XXXX XXXX would have to reverse its charge off on this fact alone, not to mention the incorrect billing to a fraudulent or wrongful address that violates numerous federal laws, including the Fair Credit Billing Act ( FCBA ), the Truth and Lending Act ( TILA ), and the Fair Credit Reporting Act ( FCRA ). XXXX XXXX sloppy, careless, disorganized management of its XXXX accounts, or its lack of providing secure access to online credit card information and billing, was the cause of the charge off and any accumulation of late fees or interest on the {$39.00} balance due. In fact, in Nebraska there is a usury law under which Synchronys {$290.00} interest charge, as it is listed, is illegal. {$290.00} interest on a balance of {$39.00} over six months is 632 % interest. This is usury and such exorbitant interest charge likely violates many federal and other state laws. To summarize : 1 ) For whatever reason, XXXX XXXX sent my federally required billing statements and any other correspondence to a third party or fraudulent address in Michigan instead of to me at my address in Nebraska, where my account was established and my first statement was sent. I discovered this fraudulent address on my XXXX, XXXX, and XXXX credit reports in XXXX of XXXX and these were removed by these reporting agencies after they conducted investigations. As a result of this error or fraud, my credit was damaged and defamed by a charge off of his account by XXXX XXXX, a charge off I knew nothing about and for which I received no federally required Notice of Assignment. 2 ) I paid this account in full for {$39.00} after speaking with XXXX XXXX XXXX XXXX, when discovering their offer and after speaking with a XXXX representative. 3 ) At charge off on XX/XX/XXXX, XXXX XXXX sold his account to debt buyer, LVNV, for pennies on the dollar. The balance due at that time was {$290.00}, {$250.00} of which that was listed as interest charged by XXXX for 6 months on {$39.00} principlea return of 632 %, clearly in violation of Nebraskas Usury statute. However, under both contract and under federal and state law, XXXX XXXX can not legally hold me liable for payment, late fees or interest, or charge-off of his account when I did not receive required billing statements and other notices in regard to his account, and when indeed the account was paid in full to XXXX XXXX XXXX XXXX XXXX 4 ) I made debt buyer, LVNV/Resurgent fully aware of this situation, sent documentation to support his claims, and two letters of explanation, as well as speaking on the phone about what happened several times, explaining that they had a duty to contact XXXX XXXX and to remedy the error, including removing negative collections reporting that they had placed on his credit reports. LVNV/Resurgent did nothing, other than demand that I prove fraud by filing a police report, despite that I had forwarded the information regarding fraud from my XXXX report. LVNV/Resurgent made no considered effort to resolve the situation, missing the point of his dispute and making no attempt to understand and repair it. Instead, LVNV/XXXX continue to send me perfunctory validation letters and demands for payment, while continuing to harm my credit by leaving the erroneous and defamatory negative credit information on my reports. LVNV/Resurgent gave the impression that all they care about was collecting money, whether or not this was actually due, a just action, a mistake, or extortion. At this time, I simply ask that XXXX XXXX immediately remove the charge offs from all my credit reports and place a statement of error in my files and with credit bureaus, serving me notice of this action in a formal letter. I also ask that XXXX XXXX contact XXXX XXXX, XXXX, to verify my payment. Finally, I ask that XXXX contact debt buyer LVNV/Resurgent to report this error and to demand that they also remove any collections information from all of my credit reports along with a letter of explanation to the credit bureaus to be placed in my file. LVNV/Resurgent should also send a letter of apology to me, releasing me from any liability to them for payment. If this is not done ASAP, I will have no choice but to sue XXXX XXXX for damages, along with debt buyer, LVNV/Resurgent, who I am not releasing from legal liability at this time but giving the opportunity to mitigate their damages to me. This is not an idle threat. I am retired but have a XXXX XXXX and know my way around the courts in litigation." } Output: { "Issue": "Attempts to collect debt not owed", "Sub-issue": "Debt was paid" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Medical debt", "Consumer complaint narrative": "This is to confirm that I am submitting this complaint on my own behalf. Midlands Funding is reporting information on my credit report that is inaccurate across agencies and is unverified. I have attempted to resolve this matter several times and this company continues to report this inaccurate information. I do not owe these debts and have no knowledge of such XXXX XXXX XXXX Facts : 1. I do not owe this debt 2. This alleged debt is reporting inaccurately across credit bureaus 3. I have disputed this account and it has been validated. 4. This unvalidated account is reporting as OPEN ACCOUNT which is additionally not under FCRA compliance. 5. This company does not have permission to communicate with me. This is intentional inaccurate reporting and I demand that it is removed immediately as per 15 U.S. Code 1681i." } 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": "The dates on late payments and balances are not accurate and collection status should not be on my credit report. XXXX XXXX XXXX BALANCE : {$1200.00} STATUS : DEROGATORY ACCOUNT # XXXX XXXX ( Original Creditor : XXXXXXXX XXXX XXXX XXXX BALANCE : {$610.00} STATUS : DEROGATORY ACCOUNT # XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX XXXX ) BALANCE : {$1200.00} STATUS : DEROGATORY ACCOUNT # XXXX XXXX XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX ) BALANCE : {$830.00} STATUS : DEROGATORY ACCOUNT # XXXX XXXX ( Original Creditor : XXXX XXXX XXXX XXXX ) BALANCE : {$2000.00} STATUS : DEROGATORY ACCOUNT # 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", "Sub-product": "Not Available", "Consumer complaint narrative": "Equifax removed many CURRENT accounts from my credit history, which had a great impact on the age of my credit history. As a result, my credit score decreased by 65 points! This may be due to confusion over my single vs. married name." } Output: { "Issue": "Incorrect information on credit report", "Sub-issue": "Account status" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "While inquiring about trading in my vehicle to purchase a new/used vehicle, I went to several car dealerships ( ex. XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX ) and each dealership that I went to I explained to the sales person as well as to the finance manager that I did not want my credit ran multiple times nor to multiple banks as this would lower as well as have a bad impact on my credit score. I also explained to them how reluctant I was with signing the application because of those reasons as well and ever time they stated " oh the banks will notice that you are car shopping and if there are applications to the same bank they will only count as one if it 's within a 30 day period '' I am just now finding out that they all have provided me with inaccurate information and that if it 's more than 1 of the same inquiry to the same bank or company within 30-45 days only 1 inquiry should be showing up not 1 for every inquiry. I have notified the dealerships and the banks asking them to remove these inquiries from my credit report, however they continue to pass the buck. The credit bureaus ( XXXX and XXXX ) says they ca n't remove inquiries and to call the dealerships and dealerships say they ca n't remove inquiries and to call the banks, the banks says they are n't the one 's who requested the inquiries call the dealerships. XXXX stated that they ca n't see that far back to pull the application from XX/XX/XXXX. Ally ( XX/XX/XXXX, XX/XX/XXXX, and XX/XX/XXXX ), XXXX XXXX ( XX/XX/XXXX, XX/XX/XXXX, and XX/XX/XXXX ), XXXX also known as XXXX ( XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX and XX/XX/XXXX ), XXXX XXXX ( XX/XX/XXXX, XX/XX/XXXX ) and XXXX XXXX ( XX/XX/XXXX, XX/XX/XXXX, XX/XX/XXXX ). I was just recently informed that XXXX XXXX ran my credit to 10 banks which is unacceptable when I specifically asked them not to. As a individual that work hard for their money and try to have things where its feesable for my pockets, I get very upset and find it disturbing that car dealerships will lie to customers in order to benefit and get over just to make a sale. As a customer these are people that we put trust into and they have let us down. It is extremly difficult to get a new vehicle, buy a house etc. with all of those inquiries." } Output: { "Issue": "Improper use of your report", "Sub-issue": "Credit inquiries on your report that you don't recognize" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I have ask Transunion to update account information many time yet they have fail to do so. I strongly believe that they are in violation of the FCRA and are outright refusing to uphold the law." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information is missing that should be on the report" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit repair services", "Consumer complaint narrative": "For some reason, some time on XXXX, I started to received some letters regarding an alleged account with AMERIFINANCIAL SOLUTION XXXX XXXX XXXX XXXX XXXX XXXX, at that time I really did not know what to do and I forgot about it. Unfortunately, earlier this month, after pulled and reviewing my credit report, I found out that account with AMERIFINANCIAL SOLUTION XXXX XXXX XXXX XXXX XXXX XXXX on my credit report, I have not idea of where this collection account is coming from, in other words, I am not the Debtor of this account. So I am requesting to remove it as soon as possible from my credit file." } Output: { "Issue": "Unexpected or other fees", "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": "Reverse mortgage", "Consumer complaint narrative": "There is a reverse mortgage on my house in the name of my deceased husband. When we applied for the reverse mortgage in XX/XX/XXXX our home was in both our names and we were told that while the mortgage and deed would be in the name of my husband initially, after three years the deed and reverse mortgage would be transferred to include me. I remember at the closing we signed two deeds -- with the second being the one that would transfer the house back to both our names once I reached XXXX years of age. After my husband died in XX/XX/XXXX, I was shocked to learn that I had not been added to the deed or mortgage as promised. After my husband died on XX/XX/XXXX, I received a letter from Reverse Mortgage Solutions dated XX/XX/XXXX that told me that I might be eligible for a HUD program for surviving spouses called the " Mortgagee Optional Election Assignment '', which would allow me to stay in the house that I have lived in for the last 40 years ( and which I live in now with my XXXX year old XXXX veteran father ). In that letter, RMS said that I had to continue to pay the property taxes and insurance as a condition of being eligible for the HUD program. I quickly sent them back all the documents they requested in hopes of being able to continue to live in my home and care for my father. The documents I sent included my marriage certificate, my husband 's death certificate, my husband 's will, my driver 's license and a social security verification. I also continued to pay the taxes and insurance ( and am still paying the taxes and insurance ) as I was told to do by RMS. Over the next two years, RMS kept sending me requests for the exact documents that I had provided them in XX/XX/XXXX. Each tine, I sent them exactly what they requested within the deadline that they placed in their letter. Again, during all this time I paid my taxes and insurance as requested by RMS. I never received a written letter from RMS telling me I was not eligible for the HUD program. In XX/XX/XXXX, I received a letter from RMS that said they were going to start foreclosure proceedings against me in 90 days. The only default they were alleging was the death of my husband. In the letter they again said, " you may be considered an eligible non-borrowing spouse under a HUD program which allows you to remain in your home for the rest of your life. At that point I asked them to consider me for this program. I was surprised when I received RMS 's response dated XX/XX/XXXX, that said I was not eligible for the surviving spouse benefit because the documents had to be given to HUD within " 90 days from the death date '' of my husband. This means that when RMS first reached out to me on XX/XX/XXXX, the deadline had already passed. I feel that RMS should have told me about this deadline sooner and that, instead, they tricked me into paying the taxes and insurance for several years by giving me hope of staying in my home through the HUD program. They have now started a foreclosure action against me, and are seeking to put me and my XXXX year-old father on the street. I think something should be done about this. This is a very deceptive thing that has been done to me. ( Please note : I do not have access to a scanner, but I can provide hard copies of the letters mentioned above if you want them. )" } 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": "Money transfers", "Sub-product": "Domestic (US) money transfer", "Consumer complaint narrative": "Pay Pal set up a credit line for my wife and I and we did not know it. At best they got us at checkout and XXXX of us thought it was just a verification. We did not know it until we started getting emails about our balance! I asked them to reverse the transactions and put them on our credit card like we always do and they said they ca n't do that. I not only upset about that but that we were signed up for {$4500.00} in availability I never wanted and I 'm sure it hit our credit history ... I want them to put the transactions on our credit card like we have done for many years." } Output: { "Issue": "Other transaction issues", "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": "Im resubmitting a complaint to you today to inform you I was the victim of identity theft. I researched how to remove the fraudulent accounts in my report and found that I need to visit FEDERAL TRADE COMMISSION or https : //www.ftc.gov to file a report Per FCRA section 605b Credit Reporting Agencies are required to remove any accounts listed on an id theft report. Please find the ATTACHED documents to assist in blocking of the erroneous information which is being posted to my report. Here is the list of accounts which do not belong to me or were opened without my permission. XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX XXXX ) XXXX Balance : {$450.00} ; According to FCRA Section 605B ( a ) the CREDIT REPORTING AGENCIES shall block any information in the file of a consumer that the consumer identifies as information that resulted from alleged identity theft, not later than 4 business days after the date of receipt." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information belongs to someone else" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Mortgage", "Sub-product": "Conventional home mortgage", "Consumer complaint narrative": "Rushmore Mortgage continuously asks me to provide proof of flood insurance. I have multiple months of letters requesting insurance. Each time I receive one I call and give them the insurance info.Then it is quiet a month or two then they start again. If I could refinance with another company at the same interest, I would. I am trapped with them. As of XX/XX/XXXX, they purchased flood insurance on my behalf. My flood insurance is paid through my HOA. The insurance company sends out any requests for info promptly as evidenced by my second mortgage not engaging in these extortive practices. Rushmore agents are polite yet totally impotent to help in any way other than to say they will pass it on to their team." } Output: { "Issue": "Trouble during payment process", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "XXXX US SM BUS ADMIN ODA XXXX OF XXXX XXXX XXXX" } Output: { "Issue": "Improper use of your report", "Sub-issue": "Credit inquiries on your report that you don't recognize" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Other debt", "Consumer complaint narrative": "There was an old account which was paid in full during the time I had services with XXXX XXXX XXXX that for some reason was purchased by several collection agencies over the course of 7 years and I am not responsible for these charges. I have contacted each collector yet no one has sent me any information concerning this account. The last payment I paid was in the amount of {$100.00} to terminate my services with XXXX XXXX. A representative at the XXXX XXXX XXXX office located in my community took my payment and the equipment when closing my account. Several months later I began disputing the charges which are on my credit report, yet, nothing has been resolved." } Output: { "Issue": "Attempts to collect debt not owed", "Sub-issue": "Debt was already discharged in bankruptcy and is no longer owed" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "In accordance with the fair credit report. This list of accounts below has violated my federally protected consumer rights to privacy and confidentiality under USC 1681-section 602 States I have a right to privacy USC 1681c section 604 section 2- also states a consumer reporting agency can not furnish an account without my written instructions. USC code 1681 no consumer report containing any adverse items of information other than records of convictions. XXXX XXXX acct XXXX XXXX of XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX" } Output: { "Issue": "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": "One on my credit report is with XXXX XXXX XXXX XXXX XXXX XXXX BANK USA XXXX. XX/XX/XXXX {$600.00} The date is incorrect, its been more than 6 years & in the state of Arizona that is past the statute. The next one is with XXXX XXXX and I'm pretty sure its the same charge from XXXX XXXX from XX/XX/XXXX or before that year? It shouldn't be on any of my credit reports. XXXX XXXX XX/XX/XXXX {$880.00}" } 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": "On XXXX I filed a proper dispute of two unknown medical accounts being reported to Equifax by XXXX XXXX XXXX ( acct # XXXX and acct # XXXX ). On XXXX I contacted XXXX XXXX XXXX with a dispute and request for validation on these medical accounts per instructions from XXXX. On XXXX I re-disputed to Equifax with a copy of my dispute/validation letter to XXXX XXXX XXXX, along with proof of the collection agencies receipt of the dispute. Equifax has refused to properly investigate or remove this fraudulent account." } 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": "In accordance with the Fair Credit Reporting Act XXXXXXXX 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 an account without my written instructions." } Output: { "Issue": "Improper use of your report", "Sub-issue": "Reporting company used your report improperly" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "On XX/XX/20, I contacted BMO Harris Bank via phone to close my checking account that has {$0.00} in it. I was asked to verify my identity, and even though I am 100 % sure I answered all the verification questions correctly, I was told that my identity has to be verified another way. Consequently, I opted to have my identity verified through a text message associated with my phone number. I repeated to the representative the confirmation code I was sent, but again, I was told that I'll have to be verified another way. Now, I'll have to send BMO Harris Bank a copy of a current photo ID that has to be notarized ... .. Seriously, what bank asks for a form of photo ID that has to be notarized. I'm not spending around {$10.00} in order to close a checking account. I demand that my account be closed immediately and sent an apology for having wasted my time because of BMO Harris Bank 's antiquated and ineffective forms of identity verification." } Output: { "Issue": "Closing an account", "Sub-issue": "Can't close your account" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Debt collection", "Sub-product": "Auto debt", "Consumer complaint narrative": "United acceptance XXXX loan I paid my portion in payments I contacted this back not sure on credit report I believe it was the beginning ofXXXX I informed them that I had been paying XXXX XXXX XXXX in XXXX SC they said ok then I called XXXX XXXX XXXX and discussed the call with XXXX the owner he said he didn't sell my loan to keep paying him and I did now lateXXXX I totaled the XXXX XXXX insurance paid off car the co sent the check to XXXX XXXX and the account was paid in full the co. Called about the I informed of the information I was given then I see a negative report now I talked with several representative of this company and explained the situation and they said they would contact XXXX XXXX" } Output: { "Issue": "Attempts to collect debt not owed", "Sub-issue": "Debt was paid" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "This is my XXXX request that I have been a XXXX XXXX XXXX XXXX and XXXX XXXX XXXX XXXX XXXX XXXX, that I want to dispute specific records in my credit file that do not belong to me, or that I have signed any agreement. The items I'm challenging have nothing to do with any transactions I've done or authorized to gain products, services, or money. Please remove the following ITEMS. XXXX XXXX XXXXXXXX XXXX XXXX, TX XXXX XX/XX/XXXX ; XXXX XXXX XXXX XXXX XXXX TX XXXX XX/XX/XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX, LA XXXX XXXX XXXX ( XXXX XXXX : XXXX XXXX XXXX ) XXXX Balance : {$780.00} ; XXXX XXXX XXXX XXXX # XXXX Filed/Reported : XX/XX/XXXX ; XXXX XXXX XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XXXX XX/XX/XXXX; XXXX XXXX XX/XX/XXXX ; XXXX XX/XX/XXXX XXXX XXXX XX/XX/XXXX ; XXXX XXXX XX/XX/XXXX ; XXXX XXXX XX/XX/XXXX XXXX XXXX XXXX XX/XX/XXXX ; XXXX XX/XX/XXXX ; XXXX XXXX XX/XX/XXXX." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information belongs to someone else" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I have 6 loans that are 10 to 12 years old? Is that fair when I know for a fact they should be gone and Im always told it will be gone in 7 years? Here are the pictures of how long these companies will hold on student loans against someone. It isnt fair it isnt right. I have tried every way to get these removed and they are still here? Why? Why! Why? 10 to 12 year old student loans. Im tired of being mistreated wrong. Im tired of having bad credit due to overly old situations. This isnt fair this isnt right 6 loans thats been on my report for over 10 years." } 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": "The unauthorized account was removed from my credit report. National Credit Systems reduced the account by XXXX, added a fraudulent address to my credit report, and added it back to my credit file. XXXX allowed this to happen." } 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": "I have a Costco CitiBank Visa and encountered a problem with a spa vendor due to a business matter within the company. I purchased XXXX XXXX XXXX for a XXXX treatment on XXXX XXXX. At the conclusion of that treatment, I opted to purchase a package of ten for XXXX (XXXX) per session. Ten sessions were deemed to be cost effective and offer maximum results, as a la carte sessions were XXXXI had one follow-up visit on XXXX XXXX, wherein I had a massage and a XXXX treatment. The subsequent appointment scheduled for XXXX XXXX was cancelled due to the dissolution of the business relationship with the therapist.On XX/XX/2019 I filed a dispute, as I paid for ten sessions in good faith that this merchant could no longer provide due to the dissolution of relationship with the XXXX. Citibank reversed the charge and credited my account XXXX.On XXXX XXXX, Citibank furnished a subsequent response by the vendor that was dismissive and accused me of “scamming” , despite support documentation acknowledging I did not receive full package. Citibank returned the charge to of XXXX my account.I was encouraged to reply to the XXXX XXXX letter and did so on XXXX XXXX.On XXXX XXXX, Citibank sent a response to my letter stating they would work with the merchant bank to obtain a permanent credit and would advise me of any reason the credit would not be reversed. Citibank reversed the charge again and credited my account XXXX. Finally, on XXXX XXXX, Citibank informs me that the merchant will not honor the request based on information that I have provided. Citibank returned the charge to of XXXX my account this week. I do not know the other company/merchant bank involved and am unable to name them here.However, it appears that Citibank has handled the matter poorly. Citibank has reversed charges four times (with adverse credit score impact) and has not given me adequate consumer protection, sound basis for their decision or an equitable outcome given the facts." } Output: { "Issue": "Problem with a purchase shown on your statement", "Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Checking or savings account", "Sub-product": "Checking account", "Consumer complaint narrative": "I have had multiple overdraft fees which should not be because a overdraft is a extension of credit and you dont owe anything. XXXX XXXX XXXX ( XXXX Rules concerning extensions of credit ) states I can not be denied. XXXX XXXX XXXX - Extension of credit also states that overdraft fees are included in the extension of credit. When reviewing my account you will see overdraft fees on : XX/XX/XXXX for {$12.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$70.00}, XX/XX/XXXX for {$47.00}, XX/XX/XXXX for {$100.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$12.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$70.00}, XX/XX/XXXX {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$35.00}, XX/XX/XXXX for {$10.00}, XX/XX/XXXX for {$10.00}, XX/XX/XXXX for {$10.00} While reviewing my account I contacted the company and the agent told me that there are limits of reversal for overdraft fees, where in fact, as mentioned above, I should be be charged for an extension of my own credit." } Output: { "Issue": "Problem caused by your funds being low", "Sub-issue": "Overdrafts and overdraft fees" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Student loan", "Sub-product": "Federal student loan servicing", "Consumer complaint narrative": "Around XXXX I had an initial conversation with a representative of the Financial Aid office at the XXXX college in XXXX which CLOSED down a year after. During that almost four-hour conversation, I emphasized that I MIGHT be willing to cosign to support my daughters costs of attendance if she needed my help. The lady informed me that because my daughter was XXXX, she could enter into the loan independently and that she was guaranteed to be placed in suitable employment upon graduation, and that would enable her to repay the loan proceeds herself. And the loan would not be due to her until at least 5 yrs upon graduation. The school closed and my daughter was never given a job and now her degree is useless because there are no XXXX school left in USA. The financial person requested my personal information to run a credit check for eligibility " just in case '' my daughter needed a cosigner for the loan. She did not inform me that providing this information meant that I was granting permission to be listed as a co-borrower and for the loan to be generated using my personal credit profile. The result : there were XXXX loans generated on both my daughter and myself. My daughter has a loan for about XXXX. The Loan was granted ( me ) was done for almost XXXX WITHOUT my signature and I have paid back I believe close to XXXX because I was scared if I did n't pay my credit would be affected! I created an account and finally was able to contact customer service representatives in the XXXX XXXX and XXXX. They informed me that I had a loan that was due and payable immediately. I was told that if I did not make immediate payment arrangements, the loan would go into default and it would negatively impact my consumer credit rating. I felt explicitly threatened and intimidated and since I could not find anyone at the XXXX XXXX loan agency to contact stateside to contest what was a fraudulently made loan, I assented and agreed to make payments. I called every month to protest these payments, demanding other agency contacts that I could reach to rectify this gross injustice and fraud. No such information was ever provided and I continued to pay until I placed the loan in deferment for the past 5 years while I continue to fight and find answers. As of XXXX XXXX, I have paid more than {$20000.00} total, and there is still and outstanding balance of over {$40000.00} due on this loan I did n't not take out. Further, my daughter was also made a loan that is also in deferment with a XXXX balance due as of XXXX XXXX. And XXXX now Navient keeps telling me to defer the loan. I am trying my best to have someone assist me with this fraud - I have papers with NO signature of mine on this loan ; my name is TYPED by Navient." } Output: { "Issue": "Dealing with my lender or servicer", "Sub-issue": "Received bad information about my loan" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I was in prison from XXXX until XXXX. During that period someone used my personal information to open many accounts. All but one account ( XXXX XXXX XXXX ) has been removed from my report. The supporting documents clearly show that I was in prison. A XXXX search would also verify that I was in prison. I have disputed this item no less than 6 times and have shown XXXX that XXXX XXXX XXXX ( XXXX ) is acting in bad faith. XXXX has even tried to extort me, stating that if I gave them {$3000.00} they would take the entry off my credit report. I shared this information with XXXX and Experian. Both reporting agencies say there is nothing that they can do, that XXXX is basically in charge here and they won't take it off unless XXXX approves. A law is being broken here. Neither XXXX or the reporting agencies will provide me with the proof that I personally rented this apartment. They are relying on information used by an identity thief who had all of my ID. I would like the XXXX entry removed from my report and would specifically ask that both reporting agencies be reminded that they have a responsibility to both consumers as well as those that pay them." } Output: { "Issue": "Incorrect information on your report", "Sub-issue": "Information belongs to someone else" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I have not received anything from Equifax in regards to my credit report and XXXX XXXX. They claimed that they would send out a report but I have not received anything. It has been over two weeks. I need my consumer report and XXXX XXXX. I've been trying to reach out to Equifax, they have not given me access to my CONSUMER CREDIT REPORT and XXXX XXXX. I have attached screenshots as proof. All I want is to have my CONSUMER CREDIT REPORT AND XXXX XXXX. Nothing less, and nothing more. Pursuant to 15 USC 1681 ( 4 ). Equifax has a grave responsibility with fairness to the consumer to abide to the Fair Credit Reporting Act. Pursuant to 15 USC 1681 ( 3 ), consumer reporting agencies have ASSUMED a vital role in assembling and evaluating consumer credit. I demand my CONSUMER REPORT AND XXXX XXXX I am entitled to a free weekly report due to the current pandemic." } 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": "Mortgage", "Sub-product": "Conventional fixed mortgage", "Consumer complaint narrative": "XXXX XXXX homes offered to pay for 1 year 's HOA dues as an incentive for financing with their title company First American Title.. The title company calculated the HOA total incorrectly ; leaving the total 2 months short. XXXX XXXX paid settlement costs, but was short 2 months on the HOA dues. The HOA has now billed us, including late fees for the shortage, saying they are not responsible." } Output: { "Issue": "Loan servicing, payments, escrow account", "Sub-issue": "Not Available" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "XXXX collection items are continuously to be reported although the information furnisher can not verify the information with me directly. I have requested that XXXX collection items be verified with signatures, contract as well as a promise to pay. I have also asked for accounting that shows how the debt was arrived at should the identifying documents be produced. Neither XXXX XXXX or XXXX XXXX XXXX XXXX has provided me with this information over the course of one year. They are well past 30 days to respond. When I notified Transunion of this, this credit reporting agency refuses to take heed to the fact that I have proven these line items to be unverifiable. This agency continues to KNOWINGLY report unverifiable information." } 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": "Government benefit card", "Consumer complaint narrative": "I filed an SDI claim with the State of California EDD office. The State of California EDD office issued the payments to my Bank of America EDD debit Visa card and I set up automatic transfers of the full amounts to my bank account ( checking ), however, none of the payments were transferred to my checking account. The payments were supposed to be transferred as follows : XX/XX/2020 - {$89.00} XX/XX/2020 - {$620.00} XX/XX/2020 - {$170.00} XX/XX/2020 - {$400.00} XX/XX/2020 - {$620.00} All of these transactions appear on my B of A EDD card account as transferred however the amounts never ended up in my bank account." } Output: { "Issue": "Problem with a purchase or transfer", "Sub-issue": "Card company isn't resolving a dispute about a purchase or transfer" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "According to the FCRA I am required by law to have accurate and up to date reporting on my credit report and an old address was found on my report. The address is XXXX XXXX XXXX XXXX XXXX, Utah XXXX. Along with an old address their are old employers as well. The employers are XXXX XXXX XXXX and XXXX XXXXXXXX." } 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": "Medical debt", "Consumer complaint narrative": "In accordance to the Fair Credit Reporting Act the below account has violated my rights for privacy and confidentiality under 15 USC1681 : AMERICOLLECT INC, 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": "Attempts to collect debt not owed", "Sub-issue": "Debt is not yours" }

You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue. Input Format: { "Product": "<product_name>", "Sub-product": "<sub_product_name>", "Consumer complaint narrative": "<detailed_complaint_text>" } Output Format: { "Issue": "<main_issue>", "Sub-issue": "<specific_sub_issue>" } Follow these guidelines carefully: 1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data. 2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank. 3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear. 4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category. 5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX." 6. Ensure that each complaint is classified in the most accurate and consistent manner possible. Input: { "Product": "Credit reporting, credit repair services, or other personal consumer reports", "Sub-product": "Credit reporting", "Consumer complaint narrative": "I disputed a public reporting with XXXX and Equifax which was 2 bankruptcy filings that shows dismissed. I was told that the information reporting came from XXXX XXXX. I contacted XXXX XXXX and initiated a dispute in which they came back and deleted the reporting from my files. I received an updated report from XXXX XXXX along with information about why they deleted. I then forwarded this information to XXXX and Equifax for them to review and update my file and delete their public reporting to match XXXX XXXX. XXXX and Equifax refuses to update my file and claims that XXXX XXXX validated the reporting which is not true, because I spoken with XXXX XXXX and received a new file from them showing NO BANKRUPTCY reportings. XXXX XXXX update reporting came to me on XX/XX/2020 XXXX responded to my dispute with no investigation XX/XX/2020 and Equifax response is dated XX/XX/2020." } 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": "USC 1681s-2 Responsibility of furnishes of information to consumers reporting agencies ( a ) Duty of Furnishers of information to provide accurate information A person shall not furnish any information relating to a consumer to any consumer reporting agency if the person knows or his reasonable cause to believe that the information is inaccurate. My proof of evidence is the third party reporting agency XXXX. This reporting agency is being used by all three agencies XXXX, Equifax, and XXXX. They all confirmed that my consumer information is correct. But it a problem I am not a her. So all the information being given and shared has me as a her. I have provided the evidence." } 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": "Consumer Loan", "Sub-product": "Vehicle loan", "Consumer complaint narrative": "On XX/XX/XXXX me and my wife signed a auto loan with Prestige Financial based in XXXX XXXX XXXX XXXX for a vehicle bought from XXXX in XXXX, XXXX. This vehicle was bought with GAP coverage sold to us ( through XXXX XXXX ) that was sold to us and explained that would pay out the difference still lowed if the vehicle was deemed a total loss by insurance and we still owed on the loan. On XX/XX/XXXX, the vehicle financed by Prestige Financial was stolen. It was reported to the XXXX Police Department and two days later it was found, the XXXX, XXXX police arrested and charged a man driving our vehicle with vehicle theft under XXXX law. Our insurance provider XXXX deemed this as a loss from auto theft. After much delay through the GAP provider, they denied our claim stating we invited the act of theft in which we did not. There is still a balance owed Prestige Finance but due to job loss we are unable to pay anything to Prestige and we currently are still fighting the GAP provider for not paying out the policy for what XXXX Police Department and XXXX deemed as a auto theft. Our complaint is that for about a month, many individuals at Prestige are harassing me and my wife on a daily basis through phone calls, messages, texts and being demeaning and rude and not acknowledging that we are unable to make any payments and that we are still actively seeking XXXX XXXX XXXX to pay out as the policy was sold to us. Prestige does not need to contact us daily and it is harassing to us." } Output: { "Issue": "Problems when you are unable to pay", "Sub-issue": "Not Available" }