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You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hi XXXX, Equifax and XXXX and plus XXXX XXXX ; I'm extremely confused why there still an item on my credit report that continues to shows and report incomplete, misleading, and unverifiable data. Especially there are so many inaccuracies on the way this XXXX XXXX Account is reporting between XXXX and XXXX. How come it's reporting different data and different balances. See attachments. Why XXXX saying that I owed {$1200.00}, but XXXX says that it was PAID and {$0.00} balance. Something doesnt seem right. And Equifax removed it from my credit report. I need this delete today from my credit because its reporting multiple inaccuracies, incomplete, and misleading information.
Also, why you continue to report different misspelled and variations of my names and some unknown previous or former addresses and phone numbers. I disputed these several times in the past and I've asked that you deleted them from my credit report because it's stressing me out. delete from my credit report effective today."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "On XX/XX/2019, I placed a security freeze on my file with XXXX XXXX XXXX.
On XX/XX/2019, I sent Equifax a certified letter requesting verifiable proof they have on file regarding the bankruptcies.
On XX/XX/2019, I sent US Bankruptcy court a certified letter seeking information on how they report to credit reporting agencies.
On XX/XX/2019, I received a letter from US Bankruptcy court stating bankruptcy court does NOT perform any activities with the credit bureaus.
On XX/XX/2019, I received a copy of Equifax investigation report. Equifax stated the information, I disputed was verified that the public record item is reporting correctly. Equifax obtained information from local, state and federal courts through a third-party vendor, XXXX."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Public record information inaccurate"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Credit card debt",
"Consumer complaint narrative": "I have disputed this debt and asked for verification on XX/XX/2018 and XX/XX/2018.
I received what appears to be a copy of monthly statement. A monthly statement does not validate a debt it needs to be a signed written contract under FDCPA and FCRA rules and guidlines.I have also received a letter threatning to sue me for the amount oued on the debt .On XX/XX/2018 i received a letter from an attorney threatning to sue me.
I have all documented communication."
}
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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "I was at my XXXX during 2012 and I XXXX. My Primary care physician contacted the XXXX XXXX Police and they handcuffed my and took me to XXXX XXXX XXXX. I told both parties that my MCO did not cover hospitalization. They insisted that I be committed for evaluation. I tried to sign-out XXXX ( XXXX ). I was denied. However XXXX XXXX and one of the medical providers billed me for this service. I became homeless and my case manager submitted a hardship waiver. For the most part the majority of the bill was waived. However, this one particular provider ( XXXX XXXX XXXX ) did not and they 've sent it to collection. Now under the 8th amendment of the US constitution all pretrial or post convicted prisoners are afforded medical treatment while in custody. Because of this constitutional right, I contend that I should have never been responsible for the bill in the first place."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Account terms"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "To make a long story short, I have an XXXX XXXX store card ( recently closed due to this issue ) with XXXX XXXX. Not only has comenity reduced my limit twice in one month on more than one occasion, they have me in their system as using 100 % of my credit limit ( {$940.00} ) even though my account is paid off, with no balance and no payments due!!!
I made a purchase in XX/XX/XXXX for a dresser that was {$790.00} plus tax and delivery. Delivery day came not merchandise..I called to be informed that I never confirmed the delivery date ( which I did upon leaving the store via the link sent in a text ) so I was going out of town and chose to not wait on another date and cancelled the order. Was told by the store I cant cancel with them I needed to contact comenity bank Which I did. They said since the delivery wasnt confirmed, nothing was taken from my card so there was nothing to refund. I confirmed that I still had my available balance and hung up.
Fast forward to today XX/XX/XXXX. I got some office stuff for Christmas and needed a desk. I went online to purchase one from XXXX XXXX.the desk was {$290.00} plus fees. Went to check out, it said unable to process. I logged in to my account to find I had {$97.00} avail credit. What!!!!! I was upset and called, I spoke to 3 different people who transferred me each time after being on the phone with each for 45min or so. I finally got someone who said my account is on hold..Why I said?? And the avail credit was now 0.. the rep ( supposedly a specialist supervisor went on to explain that the previous cold purchase was still in the system, and not only that, the purchase I just tried to make depleted the rest, and although one transaction was cold in XX/XX/XXXX and todays didnt process, I will have no available credit until XX/XX/XXXX because of the no interest promotion from the XXXX purchase..Im upset and I say, so basically I have no credit even though I have no merchandise, and completed XXXX transactions? And because of a system error, it is going to show that I am utilizing 100 % of my limit with your company to credit bureaus, even though I am utilizing NONE!!! Basically I was told this rarely happens but when it does, just sit tight, and wait. I said XXXX is not when I need the desk. Not only did this company ruin my score last year by their frequent decreases, way more than any other company despite on time doubled and extra payments, but now my credit will be destroyed once again by this company showing 100 % utilization when I havent utilized a dime!!!! Please help me..this must be illegal!! To make matters worse, I have a XXXX XXXX card through this same bank ... .please help me, I closed this card do to the frustration of it all, and now I fear my credit will show this inaccurate utilization for months..this is unfair and unprofessional"
}
Output:
{
"Issue": "Trouble using your card",
"Sub-issue": "Can't use card to make purchases"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Purchased some furniture from a furniture store. We bought a sofa and love seat, a bunk bed and a mattress. The sofa and love seat were suppose to have wooden legs and removable cushions. The legs were plastic and the cushions were sewn in. The bunk bed turned out to be XXXX different colors. The mattress had a manufactured on date the same as the date we purchased it. We tried to resolve this with the merchant but were unable to do so. Filed a dispute against the XXXX with Bank of America, the company whose credit card we used. We filed the dispute about XX/XX/XXXX. Soon after we were informed the dispute was denied because we did not submit necessary information. The person I talked to reviewed the file and saw that I had actually submitted the necessary information and they would resubmit it. Later we were informed that the pictures showing the problems with the products were not acceptable. I had to write out what the problem was. Pictures showing the problems were apparently not good. XX/XX/XXXX we received a letter dated on XX/XX/XXXX that the claim was denied. It stated that all available options were exercised and they were unable to obtain a credit from the merchant. And that they considered the claim resolved and wo n't respond to any further correspondence relating to this claim. We are not sure why they denied our dispute considering the documents we submitted to support it. And any attempts to find out have not be productive. Apparently they are not responding to any further correspondence relating to this matter. I have even attempted contact on XXXX and after doing what they say, do n't hear anything back. I can not give exact dates because when I attempt to access the information on their website it seems to have been removed. They wo n't answer my question on why that is either. On my account they have my address and phone number but tell me to send contact info. When I do they say they did n't receive it, even though they already have it. This has been going on for 4 months. All of the items we purchased had issues. Ca n't submit certain documents because I ca n't access them even though Bank of America says they will be accessible for 120 days. Bank of America has yet to answer why I ca n't access even though I have made numerous attempts to find out."
}
Output:
{
"Issue": "Other",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have reviewed my credit report and noticed that my name was misspelled, have incorrect addresses and inaccurate accounts.
I have sent out numerous disputes to removed this inaccurate information and accounts on my credit report. Please remove this inaccurate information and accounts that can not be verified.
Here are the dates I Sent out letters : XX/XX/XXXX."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Was not notified of investigation status or results"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I applied credit card and rejected my application got the letter on XX/XX/2018. My application id is # XXXX. After contacting customer care they said they were not able to find credit history and asked me to send SSN fax. I did my SSN fax on XX/XX/2018 and confirmation number is # XXXX. Between XX/XX/2018 and XX/XX/2018, an inquiry made by XXXX XXXX was added to your credit report. They rejected my application because i am staying in USA on XXXX XXXX XXXX, Credit report manager confirmed this on XX/XX/2018 evening around XXXX XXXX. Why they pulled my credit report again? What is the need of those many times doing hard inquiry? They didn't inform about that. Kindly do the needful."
}
Output:
{
"Issue": "Improper use of your report",
"Sub-issue": "Credit inquiries on your report that you don't recognize"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other (i.e. phone, health club, etc.)",
"Consumer complaint narrative": "I had a missed call from them I called back she started off with an attitude I told her that my computer is broken so I could n't do my schooling I work to much to get to a library and my friends do n't have an extra computer to borrow she told me the balance and she offered {$15.00} a month I said that 's fine no problem but I had to get back to work told her to call me back after XXXX. She called back and I had my boyfriend answer they one did n't asked him to verify my info and they disclosed to him that the call was about a debt. I gave them my okay to speak to him after they disclosed. they were giving him a hard time and he asked to speak with a supervisor or manager and they said no they said that they are not gon na be calling and any more communication will be written .. I never said I wanted that the lady ended call after. we called back and was pretty much begging for a supervisor the male kept saying we were very rude and he wont assist us. and kept hanging up on us. I have recordings of this going on also."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Used obscene/profane/abusive language"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose prepaid card",
"Consumer complaint narrative": "XX/XX/XXXX I found out when it was still pending that it was used unauthorized. The amount was {$47000.00}. I called right away to report it being stolen. I was told to wait for it to be posted to dispute it. I called the store to tell them its not me, they couldnt find a sale with my name on it.
XX/XX/XXXX The charge was posted, but it changed to {$14000.00}, which is what I had on the card. I called Blackhawk to start the dispute process. Filed a police report with a notarized dispute form. I was told the process takes up to 90 days XX/XX/XXXX I emailed the XXXX customer service in case its an online purchase because the store staff cant find that sale the multiple times Ive called, hoping that they can stop the shipment. I got a response that XXXX reported that sale to their loss and prevention department.
XX/XX/XXXX I received an email from XXXX detective XXXX XXXX asking for more information on the case. XXXX filed a stolen police report on the sale. I told him the location of the ATM I used. He had someone went and got the skimming device. I also called Blackhawk to give them all this information, hoping itll help with their investigation.
XX/XX/XXXX XXXX the email from Blackhawk stating that theyve found no error and the case is considered resolved and closed. I called right away to appeal the decision and was told they will reopen the case and the decision was from XXXX, and wait till 90 days for a resolution.
XX/XX/XXXX I couldnt get through to XXXX, I called Blackhawk again to ask what was the reason it was denied. A supervisor XXXX said through Blackhawks investigation, that they couldnt see evidence of fraud, in an over 1 hour recorded conversation, I argued that the evidence is actually overwhelming that its a fraud, he asked me to prove fraud by saying repeatedly, You are saying that, but can you prove it?. I sent the screenshots, he said none is considered proof of fraud, I said I can get the footage of the suspect making the purchase, he said thats not proof either, I asked him to talk to the detective, who said hes happy to confirm its a fraud. The supervisor said they dont do that. He said I am not their customer, I am someone whos authorized to use that fund. Their customer is XXXX, itll depend on if XXXX wants to restart the fund. I argued it is given to me then its my money, he said no, but if youd like to call it your money, he calls it the fund, the entire conversation was extremely frustrating and insulting, he confirmed that Blackhawk wouldnt consider this as fraud even with all the evidence I had, and wouldnt talk to the detective, and wouldnt consider me as a customer even though the fund was given to me. I contacted the detective whos working on the case to get more evidence because everything I had was rejected as evidence by Blackhawk. He said that doesnt make sense, because XXXX filed a stolen police report and flagged the sale as fraudulent and didnt get paid. Hes got the footage of a suspect buying 6 watches with multiple cards, each of our cards have {$14000.00} max, he has to use multiple cards to pay for them. He also gave me the contact information of the detective XXXX XXXX, whos working on another victim in my area in the same sale. As in the email, he wasnt able to give me more evidence but it confirms that there are detectives working on both the XXXX and the victims side, and therere more than one victim in the same sale.
From XX/XX/XXXX to XX/XX/XXXX Ive talked to all the detectives and the merchant to collect as much evidence as I could to prove that its a fraud case, and sent them to Blackhwak, and they were not helpful at all, some say the decision is from XXXX, some say that it didnt occur to Blackhawks investigation team that card information can be stolen and made a copy of. Some say that somebody might have clicked something wrong in the system and it was a mistake, none was able to confirm whats happening with the dispute and all had told me to wait for 90 days for a resolution. Their responses have been so inconsistent that I filed a complaint on XXXX and CFPB, Blackhawk responded on both websites with the call log and asked me to wait for 90 days.
XX/XX/XXXX Its been 90 days, I tried talking to a manager whos assigned to my case after all the complaints Ive filed, I was refused to get transferred the call to her, she later sent me an email saying they are still working on it and will send me an email when theres a resolution. Theyve violated the 90 days and they dont have a reason for the delay or how much longer they need."
}
Output:
{
"Issue": "Problem with a purchase or transfer",
"Sub-issue": "Charged for a purchase or transfer you did not make with the card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "I did not expect the interest rate of the loan to be over 70 %. I have probably paid off my loan but because of the very high rate, my principal is barely moving. They did not tell me this when I open the loan or explained to me in clear detail what I was signing up for. Net Credit is charging way over the usury rates. I'm paying over 70 %."
}
Output:
{
"Issue": "Charged fees or interest you didn't expect",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "It has been nearly XXXX years since I filed my last complaint against Wells Fargo Home Mortgage with the CFPB and I still have not received any positive outcome or results. Wells Fargo Home Mortgage has been servicing my mortgage loan for over 10 years and has denied me the opportunity to modify my high interest loan. I currently have an FHA loan with an interest rate of 7.25 %. I have made at least XXXX attempts to apply and qualify for a loan modification to reduce my interest rate and monthly mortgage payment through various mortgage assistance options that were readily available to consumers like myself over the years. I have applied for the streamline refinance, recast, and President Obama 's Making Home Affordable Plan and other loan modification plans for the past XXXX years and Wells Fargo has continually denied my request based upon their investor guidelines from what I have been told. When I initially reached out to the CFPB I was hoping to receive additional guidance and support on how to communicate with Wells Fargo and bypass the blatant discrimination practices I have had to endure throughout this process. In XX/XX/XXXX, I suffered an economic hardship after I became unemployed as a result of a massive company layoff after working nearly XXXX years on the job. I filed for unemployment and received {$140.00} per week until I became gainfully employed in XX/XX/XXXX. When I notified Wells Fargo to explain my hardship and the drastic reduction of income due to the layoff, I was hoping to receive an offer to reduce the monthly mortgage payment until my financial situation had improved. However Wells Fargo claims the only option the investor could offer me to prevent foreclosure on my home was to accept a repayment plan that would increase the current monthly mortgage payment of {$1000.00} to {$1600.00} per month if I want to keep my home and avoid foreclosure. I was informed by Wells Fargo once again, that I am ineligible for any mortgage assistance options, including a loan modification, recast or streamline refinance based upon the investor guidelines for my specific loan. Following the denial, I consulted with a HUD counselor, real estate attorney and other knowledgeable lenders in the real estate industry and I learned there may be a better solution or alternative to help me keep my home. At this time, I am requesting that the CFPB investigate and review all prior and current complaints against Wells Fargo Home Mortgage to let me know whether or not Wells Fargo Home Mortgage and the Investor for my loan who has continually denied my request to modify my loan for the past 15 years has engaged in housing discrimination. Based upon my limited understanding of the Fair Housing Laws and rules and regulations of the CFPB and the Dodd-Frank Act I strongly agree that Wells Fargo is not in compliance with the rules and regulations of the CFPB and Federal Laws. Furthermore, I have now become a victim of discrimination due to my age, race, gender and familial status. The interest rate I have at 7.25 % is not an affordable rate and I want my interest rate reduced to reflect the current market rate in order to provide me with an affordable monthly mortgage payment so I may continue to reside in my home."
}
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": "Accounts status is inaccurate or should not be there and should be deleted XXXX XXXX XXXX XXXX show charge off XXXX XXXX XXXX XXXXXXXX show charge off XXXX XXXXXXXX remove status comment XXXX XXXX XXXXXXXX remove status comment XXXX XXXX remove the status {$3.00}, XXXX written off.
XXXX XXXX XXXXXXXX remove the status {$4.00}, XXXX written off.
XXXX remove the status {$4.00}, XXXX written off."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "XXXX XXXX XXXX XXXX ( XXXX ), XXXX XXXX XXXX XXXX, has a late fee policy that is gauging and loan sharking consumers by applying high late fees and on top of the late fees 29 % interest on the late fees, a double penalty to the consumer. These fees are incredibly higher than what mortgage company charges. Worst all, the customer service reps ( CSRs ) are measured by the number of fees they " do not '' remove and the number of " courtesy removals '' not applied. Upon these CSR reaching their goals, they receive bonuses that those bonuses are greater in the executive management hierarchy. Just look at these companies SEC filings!"
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Problem with fees"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "I want to file a complaint regarding my student loans. I have received differing information regarding consolidating my federal loans. I called several weeks ago and inquired about consolidation of all my federal loans. I was told by the loan agent that my loans would be consolidated into one loan and my PSLF qualifying payments would be recalculated and counted as the highest number of payments made to all loans. So I proceeded with the consolidation. I completed the app online and got confirmation that my loans were being consolidate. Then on XX/XX/22 I got a letter in the mail that was regarding my consolidation. The letter stated that if I consolidate these loans I would have to re-start my payment counts toward PSLF. I panicked. This is direct opposite of what the agent told me. I placed many calls to MY FED Loan and was put on hold for over 30 minutes, when I tried to get a call back I was either hung up on or placed on another hold. Never was able to ask anyone. I searched the website for information and saw on there as well that my payments would restart. I canceled the consolidation request so I would not loose my payments. The website states that counts start over as well. However I am hearing from MANY other borrowers that this is no longer correct but that the site is not updated, that the letter I got 2 weeks ago is outdated. So I have once again been trying several times a day to contact the servicing center with absolutely no luck at all. I can not get the information that I need to determine if I need to apply for consolidation again. This has been very confusing and the information that I was given over the phone weeks ago is direct opposite of what is on the website. I need written facts so that I can determine if consolidation is the right course for me. I can not consolidate if I have to restart my payment counts. I just need correct information and can't even get a call back. I have also direct messaged them on XXXX XXXX and can not get a response. I do understand that everything is in process to change the information however there is a deadline I am trying to meet as well for the consolidation process. I need help getting the correct information. I have been getting misinformation for years with various student loan servicers."
}
Output:
{
"Issue": "Dealing with your lender or servicer",
"Sub-issue": "Received bad information about your loan"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Dear to whom it may concern, My name is ***XXXX XXXX . I have been repeating complaining process for a year. However. none of my request has been fulfilled and the CREDIT BUREAUS always refuse to review and remove inaccurate items of my account. The items below I have disputed and none of the data furnishers have provided me any scope of the " so-called '' investigations as required by 1681s-2 ( b ) and it has already passed the legal time from of 30 days to do so. ] had disputed several items that do not belong to me to XXXX, XXXX, and Experian and I also included all proper and required identification such as my drivers license, social security card, and proof of address and yet the bureaus have been using " stall tactics '' to delay my disputes and or not give me the proper fairness of the FCRA. On XX/XX/XXXX the bureaus sent me a letter requesting that I send them my social, drivers license, and proof of address saying that they " Can not ' help me but I know this is just a stall tactic they are using because I have proof and transmission receipts from CFPB including my drivers license, my social security card, and my proof of address.
**Address** XXXX XXXX XXXX XXXX XXXX XXXX XXXX, PA XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX, IN XXXX XXXX XXXX XXXX XXXX, OH XXXX XXXX XXXX XXXX XXXX XXXX, OH XXXX These address are either not belong to me or old. I have attached proofs of my current address below. Please check it!!!
**Auto Loans** _XXXX XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX Where is the original contract, application or other proofs which shows my consent and signature? Who is the person you receipt this information from. What was your method of verification. Moreover, section [ 15 U.S.C. 1681i requires the credit bureaus to reinvestigate each and every disputed item and to complete the reinvestigate each and every disputed item and to complete the reinvestigate or delete all the disputed items within one month. ** How this account can update an remark " disputed resolved " when I don't receipt any description of the procedures with your reinvestigation.
**Hard Inquiries** _XXXX XXXX XXXX Inquiry : XXXX XXXX, XXXX UNAUTHORIZED INQUIRY AND DATA FURNISHER HAS VIOLATED METRO2 REPORTING COMPLIANCE BY NOT REPORTING THEIR CONTACT INFORMATION XXXX XXXX XXXX Inquiry : XXXX XXXX, XXXX Auto Financing UNAUTHORIZED INQUIRY AND DATA FURNISHER HAS VIOLATED METRO2 REPORTING COMPLIANCE BY NOT REPORTING THEIR CONTACT INFORMATION _XXXX XXXX XXXX Inquiry : XXXX. XXXX, XXXX Miscellaneous Reptg. Agencies UNAUTHORIZED INQUIRY AND DATA FURNISHER HAS VIOLATED METRO2 REPORTING COMPLIANCE BY NOT REPORTING THEIR CONTACT INFORMATION **Collection*** _XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX I do not have any financial obligation with this company. So please delete it.
XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX This collection account does not belong to me and the creditor or collection agency does not have any signed contract with me or my information. I demand this to be removed.
XXXX XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX This collection account does not belong to me and the creditor or collection agency does not have any signed contract with me or my information. I demand this to be removed.
XXXX XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX This collection account does not belong to me and the creditor or collection agency does not have any signed contract with me or my information. I demand this to be removed.
XXXX XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX This collection account does not belong to me and the creditor or collection agency does not have any signed contract with me or my information. I demand this to be removed.
_XXXX XXXX XXXX Reported : XXXX. XXXX, XXXX Where is the original contract, application or other proofs which shows my consent and signature? Who is the person you receipt this information from. What was your method of verification? Using electronic response is not an acceptable proof and must be verified itself. This collection does not belong to me and the creditor or collection agency does not have any sign contact with me or my information ) I kindly ask the CFPB to aid me in the Ex Parte of the REMOVAL of all of the above accounts as all investigations on the items in question have not been verified and passed the legal time frame of 30-45 days. I have validated all debts from these creditors and filed complaints on all parties to XXXX, XXXX, Experian and every creditor reporting the above items ) and have not received any proof that items belong to me.
Also In accordance of the FCRA [ 15 U.S.C. 1681d ] ( a ) Disclosure of investigative consumer reports I must receive a disclosure that this letter was is received and it must be made in a writing mailed, or otherwise delivered, to the consumer, not later than three days after the date on which the report was first requested.
**All the collection that i have listed above are in violation of the FCRA [ 15 U.S.C. 1681l ] " Whenever a consumer reporting agency prepares an investigative consumer report, no adverse information in the consumer report ( other than information which is a matter of public record ) may be included in a subsequent consumer report unless such adverse information has been verified in the process of making such subsequent. First of all, these collections are not authorized by me as I am the only authorized user on my credit report. I also never receipted any disclosure or notice before these account are listed on my report which is VIOLATION OF the FCRA [ 15 U.S.C. 1681l ]. Secondly, These collections are not belong to me and the credit bureau don't have any proofs, my signed contact. They just want to ignore my request and try to steal my money as their dirty ways. In addition to that, after my complaint, I receipted no reinvestigation description of all the disputed items when the Credit Bureau update remarks " dispute resolve '' on these account."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information is missing that should be on the report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "In accordance with the Fair Credit Reporting act XXXX XXXX XXXXXXXX Account Number : XXXX XXXX XXXX XXXX Account Number : XXXX XXXX XXXX, Account Number : XXXX 1. The inquiry was not authorized XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report.
2. The inquiry was not authorized XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report.
3. The inquiry was not authorized XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report.
4. The inquiry was not authorized XXXX XXXX XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report.
5. The inquiry was not authorized XXXX XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report.
6. The inquiry was not authorized XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report.
7. The inquiry was not authorized XXXX Date of inquiry : XX/XX/XXXX Please remove it from my credit report., 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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Moneylion has been constantly withdrawing money from my account! I spoke with a fraud department representative for Moneylion on XX/XX/XXXX and after talking for almost XXXX hrs we were told that my Moneylion account would be frozen and suspended while the investigation is launched, meaning no transactions or anything can be done with my account. At the end of the call with the fraud department, I was told that I would receive a case number in my email for my account investigation, I even have a voicemail that was left on my mobile device confirming that the case number would be sent to my email. Here I am today with no case number, and no representative answering the fraud department line even after being on hold for over XXXX.
I first noticed my Moneylion account was breached on XX/XX/XXXX after receiving notifications of unauthorized transactions being processed from my primary bank account through Moneylion. Also, on XX/XX/XXXX there was another big breach into my account after noticing more unauthorized transactions being processed, these transactions totaled more than {$1500.00}! Today XX/XX/XXXX I have had a total of over {$2500.00} withdrawn from my primary external bank account for Moneylion payments and deposits even after reporting this account to the fraud department.
I havent used my Moneylion account in over 6 months in XXXX since I began working my new position with the United States Postal service, so it was mind blowing that so many transactions as well as loans and withdrawals from my external bank account was being processed by Moneylion. I CAN NOT GET THEM TO STOP TAKING MY MONEY and it is getting in the way of my real bills and paycheck deposits. Im seeking help in this case because this company obviously doesnt care about the claims and complaints from customers. THIS IS NOT ME USING THIS MONEYLION ACCOUNT and I refuse to keep losing money over a careless company! I will be taking all steps necessary to pursue legal actions against this company in hopes to stop the fraud and to receive my hard earned money back.
( I also have photos of all the unauthorized transactions Moneylion have processed in less than a month totaling more than {$2500.00} )"
}
Output:
{
"Issue": "Problem with a lender or other company charging your account",
"Sub-issue": "Can't stop withdrawals from 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": "According to the Fair Credit Reporting Act, Section 609 ( a ) ( 1 ) ( A ), you are required by federal law to verify, through the physical verification of the original signed consumer contract, any and all accounts that you post on a credit report. Otherwise anyone paying for your reporting services could fax, mail or email in a fraudulent account.
I demand to see verifiable proof ( i.e. : an original consumer contract with my signature on it ) that you have on file for the account listed below.
Your failure to positively verify these accounts has hurt my ability to obtain credit. Under the FCRA, unverifiable accounts must be removed and if you are unable to provide me a copy of verifiable proof, you must remove the account listed below."
}
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",
"Sub-product": "Not Available",
"Consumer complaint narrative": "Hello, Just an fyi, you may find more than one complaint from me as I have an open bankruptcy case. I tried disputing the fact that Experian is reporting XXXX as an open account on my credit. This is part of Chapter XXXX bankruptcy XXXX filed XXXX XXXX, 2016. I contacted XXXX today and they told me that they contacted all XXXX bureau 's on their end and have done everything they can. So it is an Experian issue. I tried disputing an Experian popped up and said I cant and said they cant give me a reason why. The only # is automated and not for disputes.
I should not have to purchase a report, which I cant afford just to dispute.
Thank you XXXX XXXX"
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Public record"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I did not authorize the following companies to do inquiries : XXXX XXXX, XXXX XXXX, XXXX, XXXX XXXX Bank, XXXX XXXX, XXXX Of 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 XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXX XXXXXXXX XXXX I have no knowledge of these inquiries. These inquiries were done without my knowledge. This if fraud"
}
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 light of the XXXX breach, I am trying to place a freeze on all credit reports.
Experian is making this impossible.
I have tried to access the website/link to do this on their website, but they took the link down. I also tried to call the company, but they are saying there is excessive wait times and I can not reach a person.
Instead of allowing me to place a freeze on my credit report, they are offering me paid " alert '' services at prices of $ XXXX {$20.00} per month.
They are trying to take advantage of the situation, and are doing something illegal by not allowing me to place a freeze on my credit report."
}
Output:
{
"Issue": "Problem with fraud alerts or security freezes",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Again, I was a victim of identity theft and this is another request to remove the information from my credit file resulting from alleged identity theft. Per FCRA 605B ( 15 U.S.C. 1681c-2 ) CREDIT BUREAUS are required to remove any items not later than 4 business days after the receipt. Also, the information provided on some items are UNKNOWN to me and REMOVED from other bureaus and I have doubts about their accuracy.
XXXX XXXX XXXX XXXX XXXX XXXX, NVXXXX XX/XX/XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX XXXX NV XXXX XX/XX/XXXX ; XXXX XXXX XXXX XXXX XXXX XXXX NV XXXX ; XXXX ( Original Creditor : XXXX XXXX XXXX ) XXXX XXXX : {$1200.00} ; XXXX XXXX XXXX XXXX ; XXXX XXXXXXXX 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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "I received a loan from one main financial in the month XXXX. I have made every payment within 30 days. During this week week of XXXX. They have called me several times a day. I spoke with a representative via telephone call on XXXX and advised the rep I would be making the payment asap and within my allotted time grace period. On XXXX I received a call at XXXX XXXX leaving me a message to call them. They then called me again that same day at XXXX to ask me the same thing. I am receiving multiple calls a day and week for a payment that is 7 days late. I feel harassed and it is affecting my job as I have to be interrupted with calls as I am working from home. The are calling my cell phone and I live in New Hampshire where messages for debts is not allowed. I also believe this is a XXXX violation as well.
XXXX XXXX XXXX"
}
Output:
{
"Issue": "Problem when making payments",
"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": "According to XXXX XXXX ( Original Lender ) our mortgage account was sold to XXXX XXXX and in turn sold to Rushmore Loan Services on XX/XX/XXXX. All servicer 's without exceptions violated Federal Rule 1024.33 ( a ), 1024.33 ( b ) ( 3 ). We have NOTreceive notices from any of the above servicer 's regarding the transfer among the different lenders. XXXX XXXX does not exist any longer therefore took us years to here from XXXX XXXX, and was told the loan was sold to XXXX XXXX. The company stated that the loan was sold to Rushmore since XX/XX/XXXX. Sometime on or before XX/XX/XXXX, I contacted Rushmore and convey that had yet to receive notices of transfer and mentioned the Federal Rules .These notices must be sent to the PROPERTY ADDRESS PURSUANT TO THE ABOVE MENTIONED FEDERAL RULES. Few weeks later after our phone conversation the servicer tried to cover their error by sending mortgage statements. The servicer acquired our loan on XX/XX/XXXX, yet failed to send notices of transfer, nor provided with notices in regards to being the new servicer, Nor statement until XX/XX/XXXX. The servicer wasted valuable time while we could have applied for a loan modification years ago. It appears on its face that Rushmore does not follow Federal Servicing Rules or have any respect for the above mentioned Federal Rules. At this juncture we want to retain our property and are interested in home retention. We have provided Rushmore with a loan modification request."
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Auto",
"Consumer complaint narrative": "My father has defaulted on an auto loan with this creditor. The creditor is having a hard time finding their vehicle in order to reposes it. The creditor has sent a repossession agent to my house, my work, and has called my wife and I several times. I have explained to the creditor that I have no contact with my father nor do I have anything to do with their debt.. I have asked the creditor repeatedly to leave me alone and quit harassing me and they still wont quit."
}
Output:
{
"Issue": "Cont'd attempts collect debt not owed",
"Sub-issue": "Debt is not mine"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I was in a relationship with a man who obtained my Bank of American credit card information. He attempted to purchase bit coin with my credit card. He then made two transfers to my credit card account. The first transfer paid off my credit card entirely. The second transfer to my credit card account was for approximately {$21000.00}. The man I was in the relationship asked me to transfer this amount of money from my credit card account to my checking account and wire the money to his relatives. Since the money was not mine I did as he asked.
Bank of America contacted me and advised that they felt that what was going on was a potential scam. Bank of America put a hold on the credit card and advised that nothing would happen with the credit card, that no one would be able to move money from the credit card account at all.
Two weeks later, the man who transferred a total of {$42000.00} to my credit card account was able to withdraw {$42000.00} from the credit card account. The man had an account with XXXX XXXX. Bank of America allowed this to happen all though they told me that there was a hold on the credit card account.
Bank of America advised that I am now responsible for the entire amount of the scam. I have none of this money. I did as this man asked and sent the money to his family. I have provided Bank of America with all receipts from the wire transfers. I have provided Bank of American with the name and telephone number of the man I was talking to. I provided Bank of America with a police report for fraud.
I have asked Bank of America to file a claim for fraud but they refuse and hold me responsible."
}
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": "XXXX XXXX XXXX XXXX XXXX XXXX XXXX To Whom It May Concern : Please be advised that I have received your correspondence dated XX/XX/2017 This is not a refusal to pay or a cease notice, but a notice that your claim of monies owed is disputed. According to the FDCPA you must validate a debt to a debtor if the debtor responds to your primary notice within a matter of days. Under the FDCPA and according to recent findings with the Federal Trade Commission, you must send valid proof of this debt and a printout of records is not valid. See FTC.gov staff attorney opinion findings for details. Please dont send back a print out. The proof requested is as follows : Please know that everything must match Date you purchased debt Amount you paid for said debt Original Date of Charge Off Date of last payment/activity if any Creditors full name and address All records pertaining to actual debt to prove validity. <P/>I have put you on notice then I reserve the right to file a suit against you for FDCPA violations in my courts venue. You will have to travel to defend yourself in that scenario. Please also be advised that this request is an official validation of debt request and not a verification of address request. Proper proof of said debt is required. Please understand as well that under the FCRA any furnisher of information must put the account rating on hold while the debt is being investigated. Continuing to report this disputed debt to my credit reports is a FCRA violation also open to damages collected from you. Awaiting your reply in a timely manner. Sincerely, XXXX, XXXX SS # XXXX DOB XX/XX/XXXX ADDRESS XXXX XXXX XXXX XXXX, XXXX, FL 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 filed for bankruptcy in XXXX. Thereafter a creditor that was included in my bankruptcy sold my debt to a debt collection company. This of course being a violation of Federal law. I alerted the debt collection company, the original debtor and XXXX, Transunion and XXXX. They investigated the claim simply updated the collection as being included in bankruptcy in XXXX but will not delete the illegal debt off my report. Despite the report stating included in bankruptcy in XXXX and the debt being reported in XX/XX/XXXX. I have tried and tried to point this out to XXXX and they are either ignorant of the law or simply refusing to correct their error. Please intervene. I am trying to buy a home and this is destroying my credit score."
}
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": "there is a invalid charge on my cc which i donot recognized charged without my authorization and without my knowledge and fraudulant and cc refuses to remove this invalid fraudulant charge from my cc and continue to acumulate interest charges on this purchase and made a big debt amount on my cc."
}
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": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I received a letter from XXXX XXXX on or about XX/XX/XXXX. They offered me a " reduced balance opportunity '' for a XXXX debt. However I contacted XXXX and I do not nor have I have ever had a debt owed to them. I called the number listed on the letter and it just disconnects after being placed on hold. <P/>The date of the letter is XXXX17 The sender is : XXXX XXXX XXXX XXXX XXXX XXXX XXXX/XXXX XXXX XXXX XXXX, WA XXXX XXXX"
}
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": "Student loan",
"Sub-product": "Non-federal student loan",
"Consumer complaint narrative": "Hello, I have a private loan with XXXX XXXX XXXX. I defaulted on my loan and I was n't able to pay because of unemployment. They sent my debt to collections were I was making {$40.00} a month payments. Then they sent it to another collections agency and now it is with XXXX XXXX XXXX. They sent me a letter in mail saying pay in full. But I 'm still not working and have XXXX money in bank they only thing I can afford is {$15.00} a month until I 'm employed and I 'm more than willing to make higher payments. My father co sign but I 'm responsible for the loan he is n't working either and have medical bills because he had XXXX and had to have surgery last year. He had a total of XXXX surgeries in XXXX months the most recent in XXXX 2015. When he cosign he had no idea I would n't be working and could n't pay. Every month XXXX XXXX XXXX keep sending loan to different law offices. So I 'm responsible and just need to make monthly payment plans or a rehabilitation program that I was n't offered by original loan."
}
Output:
{
"Issue": "Can't repay my loan",
"Sub-issue": "Can't get flexible payment options"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX XXXX are not mine. Credit Bureaus are misrepresenting that I owe this debt and yet they could not substantiate its representation.. They have provided information while knowing or having reasonable cause to believe that the information was inaccurate. They failed to abide the provisions of FCRA that's why I am filing this complaint"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "XXXX XXXX is reporting my Account inaccurately at first I mailed them to prove their must have been something wrong because I do not ever remember having any collections on my account then when they responded back to me I looked further ino the account and notice it had some issues in its reporting Collection should be coded under " OTHER '' or " O '' not as either a INSTALLMENT ( " I '' ) nor as a REVOLVING ( " R '' ) account.
The ARGUMENT I CONTEND is that this MISAPPLICATION of proper TYPING creates a DECEPTIVE REPORTING DEROGATORY and in effect MIS-REPRESENTS the consumer 's TRUE CREDIT UTILIZATION RATE!
See, for a " O '' mark, the AMOUNT of debt would NOT MATTER as a $ XXXXdebt would be EQUAL the negativity weight as a $ XXXXdebt ( given ALL OTHER aspects similarly reported that is such as age ). However, misappropriating a CHARGE-OFF under the " R '' type for REVOLVING as opposed to " O '' for OTHER as it should be allows for the AMOUNT of the debt to matter DRASTICALLY where a $ XXXXdebt would have near zero effect on the consumer 's CREDIT UTILIZATION RATE whereas a XXXX DOLLAR DEBT would SIGNIFICANTLY XXXX that client 's CREDIT UTILIZATION RATE there also SUBSTANTIALLY and NEGATIVELY more effecting the DEPTH OF THE DEROGATORINESSES of the reported claim ( s ).
Also this Account his inconsistent Dates of last payment and last activity which makes it very hard to consider verifiable or accurate FCRA Section 623 CUSHMAN, v. TRANS UNION CORPORATION US Court of Appeals for the XXXX Circuit Court Case 115 F.3d 220 June 9, 1997, Filed ( D.C. No. 95-cv- 01743 ).
U.S. Code1692e.False or misleading representations 8 ) Communicating or threatening to communicate toany personcreditinformation which is known or which should be known to be false, including the failure to communicate that a disputeddebtis disputed.
This Account has been disputed multiple times all i have received is itemized letters which do not satisfy the explanation of the debt according to the FDCPA and the FCRA, I have received no explanations for the balance and past due balance still reporting on a charge off or different DOLA and as I XXXX information on this issue with the company I can clearly see I am not alone various consumers suffer this same issue which will eventually lead to a consumer lawsuit."
}
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": "Rude customer service. When I called to try and resolve problem which I suspected fraudulent, I wasnt even in their system"
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I contacted this company on XX/XX/XXXX at XXXX because I received a call on XX/XX/XXXX through voicemail from a women named XXXX XXXX from a company by the name of XXXX XXXX XXXX that it was an attempt to collect a debt. I called on XX/XX/XXXX and gave them some pin number XXXX left on my voicemail. I was told that I had a debt of XXXX and something and they were collecting on behalf of citizens bank. I never received a letter in the mail verifying any information that I have a debt with company. Please provide me with the following : What the money you say I owe is for ; Explain and show me how you calculated what you say I owe ; Provide me with copies of any papers that show I agreed to pay what you say I owe ; Provide a verification or copy of any judgment if applicable ; Identify the original creditor ; Prove the Statute of Limitations has not expired on this account Show me that you are licensed to collect in my state Provide me with your license numbers and Registered Agent. I would like verification of the debt or any copy of a judgment, or the name and address of the original creditor, and a copy of such verification or judgment, or name and address of the original creditor, is mailed to the consumer by the debt collector. I would also like to request, in writing, that no telephone contact be made by your offices to my home or to my place of employment."
}
Output:
{
"Issue": "Communication tactics",
"Sub-issue": "Frequent or repeated calls"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Since XXXX of XXXX, I have been disputing my account with Wells Fargo for my Home Equity Line of Credit ( HELOC ). I have sent certified letters, regular mail and called to speak with the representatives of Wells Fargo.
I have disputed all of my credit reports with Wells Fargo because they are reporting as if the account is currently delinquent and they are not reporting all of the account payments prior to XXXX. This account has been a charge off account for years and each time that I dispute the account it is re-aged as if it were a new delinquency showing as a collection account, not a charge off account.
I have spoken with a representative of Wells Fargo and informed him that I received a letter back and it stated that the account is still CONSIDERED collectible with an outstanding balance.
I explained to him that I have read the Fair Credit Reporting Act ( FCRA ) and the Fair Credit Billing Act ( FCRB ) as well as the Statute of Limitations on filing with a collection agency and the courts. The representative put me on hold to speak with a supervisor and then explained to me that the account is not scheduled for expedited collections but I will periodically receive letters for the balance.
I requested a letter from Wells Fargo and that they report the account accurately as stating in the FCRA AND FCRB and that I understand that the word Considered is synonymous with may and that Wells Fargo may file with a collection agency or they may not file with a collection agency, which from my understanding violates my rights. The home was foreclosed on and I only requested accurate filing status because it has stopped me from gaining employment due to inaccurate filing on my credit report, which I believe is an intentional act by Wells Fargo.
I was informed that notes would be taken and forward to correct any misfiling on my credit reports, which has not been done to date."
}
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": "review the following accounts that appear on my credit report, without any authorization, are hurting me XXXX Account Number : XXXX This is not mine.
CB/VICSCRT Account Number : XXXX"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Information belongs to someone else"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "For last two months, I have made an electronic payment to Capital One on credit card ending in XXXX. Each time, I received an email notifying me the payment was late. I, in turn, checked with my bank only to learn that the electronic payments were submitted and paid timely. When I contacted XXXX about the same, I was to by " XXXX '' that each time, I underpaid the minimum amount due -- {$230.00}. First, XXXX never informed me about the sudden, substantial minimum payment increase. and second, that it supposedly wavered last month 's late fee.
Each time I have filed a consumer complaint against Capital One, I have been retaliated against."
}
Output:
{
"Issue": "Problem when making payments",
"Sub-issue": "Problem during payment process"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I have received a debt settlement notice in my mailbox. I do not owe any kind of debt to any one. All my credit cards are regular and up to date. I do not carry any balance forward.
The sender is XXXX XXXX, XXXX XXXX XXXX XXXX XXXX, XXXX, Michigan XXXX XXXX"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "Hello, Im shocked, surprised, XXXX out both XXXX and XXXX to find this XXXX collection in my credit without proper notice or even allow me to contest it. Its not mine! - Don't waste my time by partially responding to this demand. Therefore, I am demanding under my consumer rights that you send me ALL the following documents to me. Because if not now, then you will be forced to produce them to my attorney in the discovery phase of the lawsuit. So, to save yourself potentially paying my legal fees XXXX Which are ridiculously expensive ), I will just go ahead and order them now, so I can turn it over to him.
Please provide me with the following : A signed and Notarized copy of the HIPPA release that allows you to access this alleged medical record What the money you say I owe is for Explain and show me how you calculated what you say I owe Provide me with copies of any papers that show I agreed to pay what you say I owe Provide a verification or copy of any judgment if applicable Identify the original creditor Prove the Statute of Limitations has not expired on this account Show me the you are licensed to collect in my state Provide me with your license numbers and Registered Agent or Agent of Service If your offices are able to provide real documentation as requested in the following Declaration, I will allow at least 15-days to investigate this information and during such time all collection activity must cease and desist.
Also, during this validation time, if any action is taken which could be considered harmful to any of my credit reports, I will consult with my legal counsel for suit. This includes listing any information with a credit reporting agency that could be inaccurate or invalidated or verifying an account as accurate, when in fact there is no provided proof that it is accurate."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I received a phone call from XXXX ( XXXX ) XXXX XX/XX/18 XXXX, which went to my cell phone voicemail with a recorded message regarding the following : This is final courtesy call from the Weinberg Mediation Group . Before reporting that you've been uncooperative in the investigation against you, if we do not hear from either yourself or your legal representation today we will be left with no recourse other than to report your noncompliance issue to the proper jurisdiction. Our office number is XXXX again thats XXXX thank you good day I have not received any other information nor communication otherwise written or verbal from this group and/or office regarding any alleged debts. The person does not leave his name or ask for a name of the individual hes seeking. The tone of the person leaving the message is very demeaning.
I have not tried to contact this mediation group and dont plan to based on my legal rights."
}
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": "Money transfer, virtual currency, or money service",
"Sub-product": "Domestic (US) money transfer",
"Consumer complaint narrative": "At XXXX XXXX XXXXXXXX XXXX, I receive a call from XXXX. It is one of the board numbers listed on the XXXX XXXX XXXXXXXX Sheriff Office XXXX XXXX XXXX XXXX XXXX. I miss the call but check the voicemail they left. It said " yes this message is for XXXX ( my name ) ... this is XXXX XXXX XXXX from XXXX XXXXXXXX XXXX Sheriff Department... you can reach me at field device number ... that number is XXXX " When I check the missed call was in fact listed on the Sheriff Office website, I call back at the XXXXXXXX XXXX XXXX XXXX XXXXXXXX Time. This person, supposedly XXXX XXXX XXXX, answers my call and explains that I missed a court appearance and was being held in contempt of court. The case materials for jury duty were served in-person. When I said that I did not receive, he said that my matter would be processed as DNR ( Did Not Receive ). Then he advised that per the Judge orders, I have 72 hours to be criminally charged and arrested or be processed in a civil proceeding. The civil proceeding would involve a payment of bail money ( refundable ) for {$3000.00} for contempt and another {$3000.00} for XXXX ( do not remember what it stands for ). He also said that per procedure I can not hang up and talk to anyone else. If I do not comply, I would be deemed as absconding and my XXXX XXXX license would also be tarnished and potentially lose my employment prospects as well. Then he transferred me over a XXXX XXXX who walked me through to process the payment via XXXX, a funds transfer service that most bank apps use. They suggested that due to COVID, procedures were updated and they could take payments over the phone. I could not believe I was doing it. As there was limit, I ended up processing 2 payments, one for {$5000.00} ( from Chase Bank ) and another for {$3500.00} ( from XXXX XXXXXXXX XXXX ). They put me on-hold for several minutes on the phone and then said that the subpoena has been lifted and I need to an additional sum. This was for finger-printing analysis as the case materials that were supposedly served to me in-person ( that they had mentioned earlier above ) were fraudulently received by someone else on my behalf. So this XXXX XXXX suggested I use XXXX XXXX. When XXXX XXXX blocked the transfer, it occurred to me that it is very likely to be a scam. So I reported it to XXXXXXXX XXXX XXXX XXXX XXXX XXXX XXXX dispatched a police officer to make the above notes. I am not sure why he asked if I wanted to press charges or not. He also advised that the likelihood of this scammer being caught is low. While I have reported this to the banks that I use, I am very skeptical if all this would make any difference let alone recover the money. Chase Bank said that while the money transferred was in response to the scam, they may not be able to recover it. And I thought they had insurance coverage or somehow be able to reverse the transfer since I reported it right away. As a law-abiding citizen, I truly believe I did the right thing to cooperate with law enforcement but lost money in the process. I hope the banks to cooperate and hold someone accountable somehow for what happened to me."
}
Output:
{
"Issue": "Fraud or scam",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "This information reporting on my credit profile is inaccurate and I would like you to validate that it belongs to me. Per the accounts below : Equifax : XXXX XXXX XXXX Original creditor : XXXX XXXX XXXX XXXX {$100.00} Account number XXXX Collection opened XX/XX/XXXX XXXX XXXX XXXX Original creditor : XXXX XXXX XXXX XXXX {$100.00} Account number XXXX Collection opened XX/XX/XXXX XXXX XXXX XXXX Original creditor : XXXX XXXX XXXX XXXX {$100.00} Account number XXXX Collection opened XX/XX/XXXX XXXX XXXX XXXX Original creditor : XXXX XXXX XXXX XXXX {$50.00} Account number XXXX Collection opened XX/XX/XXXX XXXX XXXX XXXX Original creditor : XXXX XXXX XXXX XXXX {$50.00} Account number XXXX Collection opened XX/XX/XXXX XXXX"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "This complaint is in reference to case numbers XXXX and XXXX. You do not have a functionality for the consumer to upload documents after a compliant has been submitted. So, I am submitting a third complaint to be able to supply all my documentation. Please review the attached documents as well as the cases referenced above."
}
Output:
{
"Issue": "Loan servicing, payments, escrow account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose prepaid card",
"Consumer complaint narrative": "Sombody used my card without my authorization and XXXX my all money, Need spend preepaid card Company don't wan na help me to get my money back they still ignoring me and I'm not getting my money back All trasaction on this account its fraudulent"
}
Output:
{
"Issue": "Problem with a purchase or transfer",
"Sub-issue": "Charged for a purchase or transfer you did not make with the card"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional adjustable mortgage (ARM)",
"Consumer complaint narrative": "The current servicer of my mortgage loan Nationstar has failed to comply with Nevada state rules regarding mediation and has failed to provide me an accounting or explanation of all the late fees, legal fees and junk fees that they have added to my loan. They have failed to open up a review of my current financial situation and instead have scheduled a foreclosure sale date. I have tried my best to follow their guidelines and provide them with everything that they need but they continue to be difficult to work with. I spent over one year trying to go through their process and they switched points of contacts on me it seemed every month. They requested the same documents over and over and I would send them -- then Nationstar would say they lost them or that they never received them. I started tracking these documents and on many occasions when Nationstar said they never received the documents, I had PROOF that the documents were received. It was like they were intentionally dragging their feet.
I have owned my home for 21 years. My ex wife and I bought this property 21 years ago. We refinanced through XXXX in XX/XX/2004 and I was making a solid living in XXXX. Problems began after the recession and crash hit our area -- my income took a huge decline. We also had an issue with a second mortgage/line of credit that was somehow pushed through by XXXX even though we did not request this loan or complete paperwork for it. This caused a lot of XXXX in our household and combined with my XXXX it was too much and we ended up getting divorced. In the divorce settlement my wife was granted the property and was responsible for making the mortgage payment so I moved out.
I found out several months later that my wife had not been making the payment and she eventually vacated the property. I was able to get the appropriate paperwork that allowed me to have the home back in my name but when I tried to retake possession of the house I found XXXX there and had to get the police involved to evict them. There was a lot of damage done and I spent many months working to get the place back into a livable condition and then began trying to work out the delinquency on the loan that had been caused by my ex-wife. During this process my loan was transferred to several different companies. In fact, this loan has been held by I believe XXXX different mortgage companies. After spending so much time I was FINALLY offered a modification by Nationstar about 6 months ago. However, the figures seemed way off. I asked for some clarification and an accounting of payments received, payments missed, late fees, etc. so I could understand the HUGE discrepancy. Nationstar would not provide any of this information and I told them that I did not believe the payment and balance etc. was correct and that if they could not provide documentation I could not accept this modification. After this happened Nationstar immediately started the foreclosure process and set a sale date for XX/XX/XXXX. I have now sent a Qualified Written Request to Nationstar in hopes that they will give some transparency and document the history and the accounting of this loan. I want to save my house but do n't feel it is so unreasonable to try and get some basic information on my loan from Nationstar. I want to resume making payments, but if I am going to sign the paperwork for a restructured loan I would like some explanation for ALL of these thousands and thousands of dollars that have been added to the balance that seem far in excess of what the late payments alone would be. I was hopeful this could have been worked out in mediation, but Nationstar skipped that step and never even gave me that option even though it is required by Nevada law."
}
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 am a victim of identity theft. I have filed a complaint with the Federal Trade Commission because someone open unauthorized accounts in my name. I have tried several times to correct this erroneous information on my credit reports but to no avail."
}
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 fixed mortgage",
"Consumer complaint narrative": "My mortgage company, Specialized Loan Servicing LLC, based in Colorado, is using investor guidelines for PMI removal that are in excess of what is allowed by the Homeowners Protection Act of 1998. Specifically, SLS is attempting to impose a seasoning requirement on my mortgage before they will remove PMI. I sent a letter to SLS explaining why the seasoning requirement is not enforceable. SLS sent a written response stating that the seasoning requirement is valid. I am sending another, more detailed letter to SLS at the same time I am submitting this complaint. I will attach copies of all of the relevant correspondence.
Additionally, I recently received the annual PMI disclosure notice from SLS. The annual disclosure notice does not mention a seasoning requirement. It appears that SLS does n't mention a seasoning requirement until you write to them requesting that PMI be removed."
}
Output:
{
"Issue": "Loan servicing, payments, escrow account",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "Dear Sir /Madam Re falsification of Information by XXXX XXXX XXXX and transfer of The Said False Information To debt collection company Credence Resource management base at XXXX XXXX XXXX XXXX XXXX Texas Zip code XXXX who had call me From a XXXX to my Telephone Line XXXX repeatedly asking several interrogating questions without giving me reasonable time to address their false claim and statement repeated and uttered over eight times supervisor for the company was over aggressive even when i told him i have not received any services from XXXX XXXX XXXX from XX/XX/2021 when I engaged the company with an intent to contract them as service provider after they collected payment of XXXX XXXX & XXXX XXXX Dollars and XXXX cents { XXXX } XX/XX/2021 for gadgets and service that they said they would provided and did not deliver on the date and time agreed upon i call several time there customer service department to have the service cancelled which they told me they did. the confirmation number i cant provided the immediately but there records that must save for seven years should be available for reference. these calls were made between XX/XX/XXXX and XXXX when I have to call my bank to dispute the initial charge which was paid back on XXXXAs a result of this false claim i am respectfully asking for an investigation for this false claim of payment of XXXX XXXX and XXXX XXXX dollars and XXXX XXXX cents which i am not owing and was never gotten any service to this address .Submitted for your immediate attention."
}
Output:
{
"Issue": "False statements or representation",
"Sub-issue": "Attempted to collect wrong amount"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "Hi I got an alert on my email regarding the change in my credit report because some of the companies were using my credit report without my concent. XXXX are as follow : - XXXX. XXXX XXXX XXXX XXXX. Wells Fargo"
}
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 disputed this XXXX XXXX XXXX and it has not been corrected. The account is still reporting wrong on my credit report but it is already closed. This account have been closed however, the account payment status is 30 days late. The account payment status CAN NOT be 30 days late on an account that has been closed. The account status is wrong and should be closed. Now, I want you to correct this misinformation on my file. XXXX XXXX XXXX XXXX XXXXate Opened : XX/XX/2018 Balance : {$0.00}"
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "XXXX credit inquires on credit not getting resolved. My XXXX from me on XXXX XXXX and again multiple times on XXXX XXXX. I have calledthe companies multiples times to have them send over the request tocredit bureaus to take inquires off my credit because the companiesfound it to be fraud right away. I have sent all my paper work in everyoneneeded, disputed the inquires online with credit bureaus. The other XXXX bureaus have taken action and fixed the problem expect Equifax, Inc. They keep telling me they are not receiving any request fromcompanies. As of today XXXX XXXX still saying same thing. Need doneASAP past my time frame, ruining my life, and causing emotional stressit 's causing me."
}
Output:
{
"Issue": "Credit reporting company's investigation",
"Sub-issue": "Problem with statement of dispute"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Money transfer, virtual currency, or money service",
"Sub-product": "International money transfer",
"Consumer complaint narrative": "I did a Wire transfer in Bank of America, they called me about security alert and I told them Yes I did the transfer, but I called them in order to cancel it, I ve been calling several time, an anyone is able to help me. The amount is XXXX usd."
}
Output:
{
"Issue": "Other transaction problem",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "XXXX was my loan servicer and it is now Mr. Cooper. The official Notice of Servicing Transfer letter is dated XX/XX/2017. It indicates the servicing of our mortgage loan ( no dollar amount was listed on the Notice of Servicing Transfer ) was being transferred effective XX/XX/2017. I have a copy of this letter.
I have read in The XXXX XXXX online that : " ... [ Y ] our old and new servicers must notify you about the transfer of your servicing rights no less than 15 days before the effective date of transfer. If you never received the servicing transfer notice, you can also file a complaint with the CFPB online. '' In our case we did receive notice, but a week or so AFTER it occurred. In my opinion this does not conform to federal regulatory requirements governing the transfer of mortgage loan services from one business to another as I have read and understood them."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I had Home Depot, and Best Buy cards both issued by Citibank CBNA.
At the beginning of the Covid Pandemic, I received phone calls.
I explained due to the pandemic I am unable to pay, and I also had a XXXX XXXX.
While waiting for XXXX, and that I would pay in full once I received XXXX.
They agreed, and put me on a deferred payment, all the while reporting me for non payment.
When I received the XXXX, I paid both cards in full.
Now that I looked at my credit score they got me with all kinds of late payments etc This destroyed my credit rating, and I would like both a letter of satisfaction from their debt collectors.
I would also like to have that false reporting removed.
Thank you."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Difficulty submitting a dispute or getting information about a dispute over the phone"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "XX/XX/2020- I spoke to a representative in regards to an overdue balance.
I informed the associate I was not aware of the overdue balance and was under the assumption my card was no longer valid ( previously XXXX XXXX card and issued new TD card with new card # ). The card was used by an authorized signer without my knowledge I informed the associate I would pay the balance in full ; however, I requested to have the {$35.00} late fee and interest charges waived as this was an honest mistake. The agent informed me she, herself, could not waive the fee but would escalate to a supervisor. I was informed I would receive a return call on my cell phone ( verified by agent ) within 48 hours.
XX/XX/ - I called back as I had not received a returned call on my verified cell # as promised.
I was informed that I was now past 30 days delinquent (XX/XX/2020) cutoff and I would be reported to credit reporting agencies. I was once again told by the agent she, herself, could not not waive the fee but would escalate to a supervisor. I was informed I would receive a return call on my cell phone ( verified by agent ) within 48 hours.
After the call I logged into my account and paid the balance in full to " stop the bleeding '' as a callback does not seem promising.
****I was charged an additional {$35.00} late fee and $ XXXX in interest due to TD 's inability to return my call as promised AND I was reported to the credit bureau*********"
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Problem with fees"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "Store credit card",
"Consumer complaint narrative": "During the holiday season of XX/XX/XXXX, I have made several jewelry purchases from XXXX 's Department store. Later in the month, I chose to return my purchases. It was n't until I received my monthly statement in XX/XX/XXXX that I realized that my returns were not properly allocated to their correct account. XXXX 's has two accounts : 1 ) a Regular Account ( that bears interest ) ; and 2 ) a Club Account ( that defers interest for 1 year ). I made 3 returns with the same XXXX 's employee on the same day. All 3 of these returns should have corresponded to purchases I made to my XXXX 's Club Account ; however, 1 of the returns was credited to my Regular Account, while the other 2 were misallocated to other items in my Club Account. When I identified the problem I immediately contacted XXXX 's at XXXX 's Customer Service to explain the situation. The agent assured me that they will resolve this issue, but that I wo n't see it on my statement for 1-2 billing cycles. I waited 2-3 month, but instead of the problem being corrected, XXXX 's had fraudulently charged my account. I contacted Customer Service to tell them that I see fraudulent activity on my statement. Again, an agent told me that they are working on my account and that I wo n't see any changes for 1-2 billing cycles. It has been 1.5 years since the incident and yet my letters to XXXX 's are ignored, no changes have been made to my accounts, no response or attempt to resolve this matter are made by XXXX 's. In fact, instead of rectifying the problem that XXXX 's Employee has created, they fraudulent added charges to my account that I did not make, in addition to interest on said fraudulent charges, and they chose to send my account to credit reporting agencies and have creditor 's call and harass my family and I. I have been with XXXX 's for a long time. In fact, XXXX 's was one of the first credit cards that I have opened. I never had any issues with them in the past ; however, their lack of customer service is deplorable. I am ashamed to have ever opened an account with this company."
}
Output:
{
"Issue": "Problem with a purchase shown on your statement",
"Sub-issue": "Credit card company isn't resolving a dispute about a purchase on your statement"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Other debt",
"Consumer complaint narrative": "RECEIVABLES PERFORMANCE inserted fraudulent accounts into my credit file. Not only are these accounts getting inserted Receivables Performance is failing to communicate to me that they hold any accounts under my name. I am not being given my right to dispute the validity of each account to inform Receivables performance that these accounts do not belong to me and are a direct result of identity theft. You are hereby notified that I am a victim of identity theft and the accounts listed below that appear on my credit file are a result of identity theft. I I did not open or authorize this account, and I request that it be closed immediately. Please send me written confirmation that I am not responsible for charges on this account, and take appropriate steps to remove information about this account from my credit files. I have enclosed a copy of my FTC Identity Theft Report. Because the information you are reporting is the result of identity theft, and inaccurate, I request that you stop reporting this information to the CRAs, as directed by section 623 ( a ) ( 1 ) ( B ) of the Fair Credit Reporting Act, 15 U.S.C. 1681s-2 ( a ) ( 1 ) ( B ). I ask that you take these steps as soon as possible."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt was result of identity theft"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "Home equity loan or line of credit (HELOC)",
"Consumer complaint narrative": "I have a HELOC line of credit at Bank of the West. I recently took an advance ( {$200000.00} ) against the line and paid it back over several payments. I made the payments at the branch. When I checked the posted date of the payments on my account they were over a week after I had made them at the branch ; 11 days for one and 8 for another. The deposits were made on XX/XX/XXXX and XX/XX/XXXXand not posted untilXX/XX/XXXX. I was charged interest for these days.
I contacted the bank and they told me that the branch system and the loan processing systems were different systems and the posted date that the loan processing department used may not be the same as the date that the payment was made at the branch. They initially asked me to prove the deposit dates at the branch by giving them the receipt ; however, I do not keep those and most people don't either. They eventually were able to confirm with the branch on the deposit date correct the error.
I have had this line of credit for several years and have taken many advances and payments. However, I did not keep track to make sure that they posted the payments correctly. I assumed since they were a bank, and everything was electronic these days, it would be done correctly. Because of this experience I asked them to audit all my payments to check posted date of the payment against the deposit date at the branch for the entire history of the line. They informed me they were not able to do this because they don't have the transaction record at the branch. I think it is absurd that they would not retain this record."
}
Output:
{
"Issue": "Trouble during payment process",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "On XX/XX/2022, I received an email from my credit monitoring service. It informed me that my credit score had dropped a tremendous amount of points. I logged on and found that CONTRACT CALLERS, INC. had illegally posted an alleged collection account to my credit profile. Prior to the email, I have never heard of nor had an account with this company. My report shows that it is an open account and was opened on XX/XX/2022 with a balance of {$2200.00}. I had no knowledge of this company nor this alleged debt. I have never had any dealings with this company. By federal law, a debt collector is supposed to make contact with an alleged debtor and give them 30 days to validate or dispute. I never receive a letter in the mail that gave me 30 days to validate or dispute this account. I never received a letter stating that I have an alleged debt with this company and owe a balance on that account. Again, I have absolutely no knowledge of this account with them. I understand that, under the FDCPA guidelines for collection agencies, they have to provide ( 1 ) proof that they are the original holder of this debt and can legally hold me responsible for paying this debt and ( 2 ) contractual proof of obligation or an assignment SIGNED from an XXXX from the original creditors corporation that I am supposed to pay them. 15 USC 1692g Sec. 809 ( b ) of the Fair Debt Collection Practices Act ( FDCPA ) grants me the right to request validation of this alleged debt they say I owe. I did not receive notice of right to dispute nor did I receive enough information to verify that the alleged debt belonged to me. If I would have gotten anything in the mail or phone call from this company before they illegally reported to my credit report, I would have respectfully requested evidence that I am the person they are asking to pay this debt and a contractual obligation that legally binds me to pay them this debt. Upon them receiving my validation letter, they would have to legally cease from collection calls, letters, and efforts and delete the account from my credit files until they can validate this debt. But again, I never received such a letter about this account or phone call ever in the mail. They have illegally posted and reported to my credit file without adhering to the FDCPA laws. Im not sure if this company is a scam, but I know that this company has broken FCRA and FDCPA laws by reporting an alleged account to my credit report without informing me through mail or phone and I did not receive notice of right to dispute nor did I receive enough information to verify that the alleged debt belonged to me. My credit score has dropped tremendously in the middle of purchasing a home. Please help me get his account removed immediately. I'm preparing to sue under the FCRA and FDCPA because of the damages to my credit and opportunity costs with the purchase of my home and business ventures that have to be delayed until this illegal account is removed. CONTRACT CALLERS, INC., XXXX XXXX XXXX, XXXX, GA, XXXX, ( XXXX ) XXXX"
}
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": "Checking or savings account",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Within the last few weeks I've been having a lot of issues with PNC Financial Institution. There have been three separate incidents that have caused me to think that this corporation may not be as reputable as I once thought. My first two issues were temporarily resolved however, this last transaction dispute that I've had with them is the absolute deal breaker. An ACH debit was processed in the amount of {$700.00} on XX/XX/2018 which caused my account to overdraw in the amount of $ XXXX. I was correctly accessed a {$36.00} fee, bringing the balance to XXXX. There were three separate deposits of {$240.00}, {$30.00} and {$2.00} were made on XX/XX/2018. There were then 5 other transactions that went through for a total amount of {$45.00}. Another deposit of {$450.00} was then transferred to the account. The deposit was made at XXXX eastern time which is in fact prior to PNC XXXX XXXX deadline for a deposit to avoid insufficient fees. At XXXX on XX/XX/2018 my account had {$0.00} available as a result of the deposits and transactions throughout the day. On XX/XX/2018 I was accessed two more {$36.00} fees. The {$450.00} deposit that was made on XX/XX/2018 was showing as pending. It has been told to me in the past that if the deposit is " pending '' it is still showing in your account which should prevent from receiving fees. I have reason to believe that PNC bank is scamming their consumers by adding inaccurate fees. Please see my attached documentation that which reflects the each transactions including the deposits. In addition, it has also been told to me by a PNC rep and branch manager that their system is reflects differently than that of which the consumer is able to see via their online or mobile banking. That is unethical because consumers are misled to believe that there accounts are in good standing only to be inundated with fees."
}
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": "Mortgage",
"Sub-product": "Conventional home mortgage",
"Consumer complaint narrative": "This complaint is in regards to the offer of a trial modification. Payments of {$1300.00} for 3 months starting XX/XX/XXXX through XX/XX/XXXX. I can not afford to pay this. On XX/XX/XXXX, I spoke with XXXX of the Relationship Manager Department. ID XXXX. The payment of {$1300.00} is unreasonable. I need a payment between the 25 % - 31 % range of {$750.00} to {$800.00} per month. This Streamline Modification does not work for me. XXXX XXXX XXXX, Relationship Manager called me on XX/XX/XXXX regarding the Modification Agreement. XXXX XXXX stated that the Interest Rate would remain the same at 4.125 %. The arrears would be put on the end of the loan, and there would be a balloon payment at the end. I explained to XXXX XXXX, that I could not afford this payment. XXXX XXXX also proposed a Down Payment of {$2600.00} then payments of {$1800.00} per month for 12 months to repay the arrears. This is totally unreasonable. I can not afford this Repayment Plan as proposed by Ocwen/XXXX XXXXXXXX XXXX. I spoke with XXXX XXXX of NACA, who is an advocate on my behalf with Ocwen. XXXX XXXX did not appear to see my stress with these proposals which were made by Ocwen and Representative XXXX XXXX. XXXX XXXXXXXX, Supervisor at NACA will visit with Ocwen on XX/XX/XXXX and will present my proposal to Ocwen. XXXX XXXXXXXX did not give advice that is helpful to me as my advocate. XXXX XXXX said she would call me after she speaks with Ocwen on my behalf. I need help from CFPB to assist me to keep my home, and assist me in getting a good interest fixed rate or leave the interest rate the same and lower the principle, and reduce my payments for the duration of the loan to {$750.00} per month Fixed. I have requested the name and address of the Investor/Investors who hold the loan, and have been denied this information. I am updating this request as of today 's date. Please send me the Investor/Investors address, name, telephone number who holds the mortgage for the captioned property. This is my second complaint filed against Ocwen Loan Servicing, LLC and the Investor/Investors who hold the mortgage."
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "PLUSFOUR INC is the creditor has been on our credit report for a debt owed of {$860.00}. We have included PLUSFOUR INC. in our Chapter XXXX filing bankruptcy. It is still showing on our credit report as active. It needs to be taken off because it is included in our bankruptcy. The original creditor was XXXX XXXX."
}
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": "At the very beginning of our Mortgage loan, while my wife was on the phone with XXXX XXXXXXXX XXXX XXXX XXXX XXXX Representative, she was helping my wife set up the account and enter our banking information so that we could process our mortgage payments. At that point the account number that was entered by the representative was missing the final digit. This caused our first payment to be returned on XX/XX/2022 ( XXXX days after we initiated the payment ). We were able to catch it in time and also were able to correct the account number online. At that time My wife initiated another payment in the system on XX/XX/2022. That payment went through and was credited properly to my account. The company also did not assess a late fee for this transaction as they recognized that the clerical error had been on their part.
In XXXX, my wife initiated the payment on XX/XX/2022, however the payment account had defaulted back to the bad account number entered into the system by their service rep. By the time we realized that the payment had been returned it was the beginning of XXXX and our payment was already 30 days past due. As soon as we realized there was an issue, my wife made the payment on XX/XX/2022. Part of the reason she was unaware of this issue was that when you initiate a payment through XXXX payment portal online the screen says Payment Confirmation at the top. ( Picture attached ). By this we assumed that our payment had gone through.
We have disputed these facts, the fact that it is not due to lack of funds or refusal to pay, it was simply a clerical error on the part of the company ( which they now deny ) to both the 3 major credit bureaus and also with the company directly.
The late payment has been removed from my wife 's XXXX report, but it has remained on her Experian and XXXX as well as remained on my 3 credit reports. Most recently when I was on the phone with the Credit Bureaus, they told me to contact XXXX directly. When my wife did that, the supervisor that she ended up speaking to said that it had all been cleared up on their end and that it should have been removed back in XXXX. However, in a subsequent call they contradicted this supervisor and said that they can not remove the 30 day late reporting because it shows in their system that my wife is the one who entered the account number incorrectly. She did in fact enter the correct account number after we realized that the rep had originally entered the account number but had omitted the final digit. Now Im left to wonder if this is the account number that their system shows that my wife entered.
Recently my wife and I got denied for refinancing our car loan to a much better interest rate and the bank cited this late payment as one of the main reasons we were denied credit. We have been working desperately since XXXX to recover from some delinquencies and derrogatories on our credit report and are simply trying to build our credit. This is really hurting our progress and holding our family back. Had we been approved for the car refinance the payment would have gone down over {$100.00} a month, which would provide us with some needed relief. All we are asking is that the 30 day late is removed, we have made every payment before and since and this is a simple clerical error that should be understood as a reason to remove this negative item from our credit reports once we brought it to their attention.
A recent check of my wife 's credit report shows a 30-day late in XXXX of Last year on Experian, but in XXXX on XXXX.
A recent check of my 3 reports shows it's late in XXXX on Experian, in XXXX on XXXX, and in XXXX on XXXX. None if this information coincides and needs to be removed.
After hours of time on the phone and trying to get this company to help us out, we turned to the CFPB for help. Please help!"
}
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": "Payday loan, title loan, or personal loan",
"Sub-product": "Installment loan",
"Consumer complaint narrative": "I applied for first and second draw PPP loans with XXXX and it was sent and approved by the SBA, and Benworth Capital was the lender. It said that Benworth funded my account. They lied! They never sent the money to my account and I know they are going to start making withdrawals from my account after the deferral period. I signed a promissory note and it's been almost a week and still no money. I can prove it with a certified statement from my bank that they never funded the account. I tried calling Benworth and the line is always busy. I tried emailing them and the email was undeliverable, so that means it's a fake email address. I tried contacting XXXX and the page won't load."
}
Output:
{
"Issue": "Getting the loan",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "PayPal has again hijacked my credit card as a " backup payment source '' without my input or permission. This is a follow up to Case number : XXXX ."
}
Output:
{
"Issue": "Identity theft / Fraud / Embezzlement",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I contacted this company via XXXX certified mail to request debt validation. The company has not responded or provided any validation. They have reported this account with all XXXX credit bureaus"
}
Output:
{
"Issue": "Disclosure verification of debt",
"Sub-issue": "Not given enough info to verify debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "I just realized this just now.
The cash to close on the closing disclosure sent from the country place mortgage to the CFPB on XXXX was {$590.00}.
The cash to close that we were told during closing with XXXX XXXX with country place mortgages information on the earnest money contract that we signed XXXX. States that we needed to put down {$7300.00} to close on top of the {$1000.00} deposit that we had already paid.
They also were requesting this money XXXX when we closed on XXXX"
}
Output:
{
"Issue": "Closing on a mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "FHA mortgage",
"Consumer complaint narrative": "National Default Servicing Corporation ( NDSC ) made material intentional misrepresentations as to their relationship as it relates to my home loan mortgage account and foreclosure processes.
NDSC provided written documents originating from them in which the affixed the XXXX XXXX XXXXXXXX XXXX at the top giving the appearance the correspondence originated from XXXX XXXX XXXXXXXX and not NDSC. Within the correspondence, NDSC stated it had been retained by XXXX XXXX XXXXXXXX to collect on a delinquent mortgage debt prior to XX/XX/XXXX. Neither NDSC nor XXXX XXXX XXXX has provided any written documentation confirming NDSC a Trustee relating to foreclosure processes to date.
Upon receipt of correspondence from NDSC prior to XX/XX/XXXX claiming any connection to my home loan mortgage debt was unclear and incorrect. I actually believe this correspondence to be a scam of some sort as I was still pending in the modification process at the time of receipt. On XX/XX/XXXX, I was approved on appeal for a MHA HAMP TPP. However, I had not been offered the MHA HAMP TPP acceptance package at the time of the NDSC correspondence. As dual tracking is prohibited under California law, debt collection processes, including foreclosure, can not begin or proceed.
All previously requested resolutions are desired to include three times the median home value in XXXX XXXX County."
}
Output:
{
"Issue": "Struggling to pay mortgage",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "I did two balance transfer with Bank of America and the expiry dates as follows. When this balance transfer about to expire. I was about to pay the near expiry one which is {$9600.00}. I did pay after 2 months of expiry. So I incurred interest charges on {$9600.00} balance, to avoid the further interest charge, I paid the {$10000.00} on XX/XX/XXXX, and it got returned because of the insufficient amount in my bank account, which was my bad because I chose the wrong bank account while paying the amount for my credit card ( BOA ). So I again went ahead and I paid {$10000.00} with the right account this time and it got credited successfully with Bank Of America. But BOA didn't apply the payment towards Balance Transfer which expired already on XX/XX/XXXX and they applied my payment on Balance Transfer 0 % which expires on XX/XX/XXXX.
I was under the impression that I paid my expiring balance transfer of {$9600.00} and I have to only pay the balance of {$3300.00} which will expire on XX/XX/XXXX.
Recently I noticed that I'm paying only the interest of {$420.00} on my credit card, not the remaining balance. It was a big surprise and shock for me to accept that Bank of America didn't consider my payment towards high-interest charges and they closed my 0 % Balance Transfer amount and now collecting interest charges which they shouldn't do because I paid for my promotional balance transfer fee of 3 % in advance for the balance which I'm carrying now.
Bank of America is denying to revert all the interest charge of {$420.00} and not honouring Balance Transfer offer which has not expired yet. I explained a lot and complained about the system inability to distinguish about returned payment and they are considering it as a purchase instead of return payment. BOA system thinks that returned payment is a purchase and applying future payments towards lower APR balance instead of applying to higher APR balance. I waited a lot for their reply and there was no solution and finally, they concluded that they can not do anything on this matter. So I left with no option than reaching out to consumer finance to resolve this issue.
Solution : I want Bank of America to revert all the interest charges ( {$420.00} ) from XXXX since I paid my expiring balance transfer in XX/XX/XXXX. Please find my statements in the attachment.
Balance Transfers Promotional APR - 0.00 % - XXXX - XXXX - XX/XX/XXXX - {$9600.00} Promotional APR - 0.00 % - XXXX - XXXX - XX/XX/XXXX - {$3300.00} Payment history : XX/XX/XXXX XX/XX/XXXX BA ELECTRONIC PAYMENT XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX BA ELECTRONIC PAYMENT XXXX XXXX XXXX XX/XX/XXXX XX/XX/XXXX BA ELECTRONIC PAYMENT XXXX XXXX XXXX TOTAL PAYMENTS AND OTHER CREDITS FOR THIS PERIOD - {$20000.00}"
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Charged too much interest"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "I do not know",
"Consumer complaint narrative": "I received a notification from my credit managing company that 'Ability Recovery Services ' has stated that I owe {$750.00} from a 2016 hospital visit and has put this account in collections. This is now affecting my credit score. I spoke with a rep at this Ability company and the woman stated that I could 'pay {$300.00} for this to all go away '. I stated that this debt is not valid and that this hospital account was closed 5 years ago.
After further research I discovered that this specific company buys closed medical accounts and attempts to collect money from unexpecting people. I have requested my medical records through a medical release form to prove the validity of my statements. I have also discovered via a XXXX search that Ability Recovery Services is involved within several law suits due to their fraudulent activity. I am disputed these claims and I am filing this complaint because my credit manager with XXXX recommended I do so. Please feel free to contact me if there is further questions. Thank You"
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "THIS IS NOT A CREDIT REPAIR COMPANY. THIS IS THE ACTUAL CONSUMER XXXX XXXX. I HAVE ALSO SUBMITTED THIS COMPLAINT VIA MAIL WHICH YOU SHOULD BE RECEIVING ANY DAY NOW. I SUBMITTED A SIMILAR COMPLAINT OVER A MONTH AGO WHICH STILL HASN'T BEEN RESPONDED TO. YOU ARE WELL OVER THE TIME LIMIT FOR RESPONDING TO THE COMPLAINT AND THE ITEMS BELOW NEED TO BE REMOVED IMMEDIATELY AS YOU ARE NOT NOT COMPLIANT VIA THE FCRA. STOP DELAYING. AGAIN THIS COMPLAINT IS DIRECTLY FROM THE CONSUMER AND NOT A CREDIT REPAIR COMPANY. YOU WILL BE SUED BY MY ATTORNEY IF YOU DELAY THIS AND DON'T REMOVE THEM ITEMS IMMEDIATELY!!!
Equifax has a bankruptcy listed on my credit report that was deleted and then added back on. I called the courthouse. They stated that they don't have anything in the system and that they don't report to credit bureaus. They said that a third party does and that I needed to dispute or file a police report which I have. This needs to be removed again permanently. I have retained an attorney. I have also disputed this multiple times. My attorney states that we can sue the credit bureau and win due to all of my documentation and Equifaxs refusal to remove it. Also, all of the bureaus have multiple inquiries on my report that I did not authorize. They are as follows : XXXX XXXX, XXXX, XXXX XXXX, XXXX XXXX, XXXX XXXX, XXXX XXXX XXXX XXXX, XXXX XXXX, XXXX, XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX should not be reporting 3 times, XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX, XXXX XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX, XXXX XXXX, XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX XXXX, XXXX XXXX, XXXX, XXXX XXXX. Please remove these asap."
}
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 teached out to Equifax plenty of times in regard to my birthdate being incorrect and they still continue to report it as such. i have sent my identification card as well as sent XXXX disputes with no moving forward with them. this is affecting me as a consumer and is hindering my consumer profile with this agency. my birthdate is XX/XX/XXXX not in XXXX and i have sent them documentation to verify this and they have responded and failed to correct such information. they are knowing and willing furnishing incorrect information about me. this has hindered me o collect my reports from specific mediums and i have been denied the opportunity to extend my credit due to their negligence."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I am applying for a mortgage loan. I was denied because of not having a XXXX on my credit report. I knew my credit was n't great, but below a XXXX seemed incorrect. I paid for my credit report from Experian. The report I paid for gives me a score XXXX . When I forwarded this information to the bank, I was told Experian sent them a report stating my score is XXXX , with XXXX & XXXX scoring me even lower. I asked the woman at the bank if this was common, her response was " Let me just say that the scores we have are usually lower than the ones consumers tell use they are given by the actual agency. '' Considering the importance of these scores, how is it legal to sell information that is different to two separate parties about the same individual? People are more th an a 3-digit credit score & companies who have such a large role in determining such important aspects of Americans lives ' must operate fairly. Experian is either lying to me or lying to the Bank, neither of which is of any comfort to me. I can not buy a home due to this error/falsehood. It is your responsibility to oversee these companies & protect consumers. Do your job, please."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I have disputed this issue several times, called the creditor and confirmed this is a mistake. What is it going to take to get this corrected with Equifax which is the ONLY company out of all three reporting such inaccurate information on this account.
Account is under XXXX XXXX XXXX XXXX Paid charged off listed which is 110 % inaccurate this account was paid as agreed and NEVER went even late."
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Account status incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "in regards to midland funding having not yet removed this time barred debt that has more than exceeded the time statute to legally collect from XXXX credit bureau. I contacted midland funding a month or so ago in regards to this time barred debt and they removed it from my XXXX & XXXX credit report but THIS DEBT still remains only on my XXXX credit report. Midland funding already did so with the other XXXX credit bureaus ( XXXX & XXXX ) so they must have forgotten to do the same with XXXX. This time barred debt that has more than exceeded the statue of limitations MUST BE REMOVED IMMEDIATELY"
}
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": "Student loan",
"Sub-product": "Federal student loan servicing",
"Consumer complaint narrative": "Hello, My complaint is regarding a refund request with EdFinancial for the purpose of relieving it under the One-time Federal Student Loan Debt Relief program. I spoke with an EdFinancial representative on XX/XX/XXXX to begin the refund process, and I was informed it would be up to three weeks before I would see the balance reinstated. Today marks just over a month, so I am filing a formal complaint as the one-time debt relief form is now available and is time-sensitive closing at year 's end. Per the rules of the one-time relief, I expect the refund from EdFinancial to be a minimum of {$10000.00} as that is the total amount of voluntary payments I made to EdFinancial during the payment pause ( from XX/XX/XXXX, through XXXX XXXX, XXXX ), which I intend to apply towards the one-time relief as soon as the balance has been reinstated.
Thank you"
}
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": "Syncb/Walmart reports to all three bureaus a 1x30 late prior to year 2019."
}
Output:
{
"Issue": "Problem with a credit reporting company's investigation into an existing problem",
"Sub-issue": "Their investigation did not fix an error on your report"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Mortgage",
"Sub-product": "VA mortgage",
"Consumer complaint narrative": "My loan was sold to Carrington Mortgage Services by XXXX XXXX XXXX effective XXXX XXXX, 2015. Prior to the sale of my loan, I was provided with my yearly escrow analysis which explained that my mortage payment would increase by approximately {$30.00} per month. This seemed like a large increase, so I did my research and found out that my taxes and insurance did increase so the increased escrow payment was justified. Fast forward to XXXX 2015. I am now an unwilling customer of Carrington Mortgage Services. I received a new escrow analysis from Carrington stating that my mortgage payment would increase {$120.00} per month OVER the increase that XXXX XXXX XXXX had already implemented. I lookled up my taxes, insurance, principal and interest amounts for each month and came up with exactly what XXXX XXXX XXXX was going to raise my payment to. {$1200.00} per month. I called Carrinton XXXX and was on hold for XXXX+ minutes each time. And while both employees were courteous, I was eventually told that they could " cushion '' my escrow however much they want and I have no recourse. So my payment is now {$1300.00}. This is unacceptable. I did not agree to these terms and will not pay this. It is appalling that I now have to deal with a company that I did not choose and who can change the terms that I legally agreed to with XXXX XXXX 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": "Debt collection",
"Sub-product": "Federal student loan debt",
"Consumer complaint narrative": "As a follow up of complaint ID XXXX.
After reviewing my recent consumer report, I have noticed that Fed Loan Servicing has failed again to comply with the federal law. They have furnished numerous late payments to my consumer report from their company.
They have stated and I quote, " Effective XX/XX/2022, your Direct XXXX Loans transferred to a different servicer, XXXX. Because FedLoan Servicing no longer administers the account, no further action is planned at this time. If you wish to pursue this matter further, you would need to contact the new servicer directly. '' Thus meaning, that this company committed an act that they want me to contact another company to resolve.
As of XX/XX/2022, Fed Loan Servicing is in violation of the federal law pursuant to 15 USC 1666b ( a ) and 15 USC 6821 ( a ) ( 2 ).
In addition to transferring my personal and private information to another company without my consent, Fed Loan Servicing needs to provide the original instrument of indebtedness with my wet signature or proof that it still exists. Please confirm that what you are sending states that I am the obligated party for repayment."
}
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": "In accordance with the Fair Credit Reporting act XXXX XXXX XXXX Account # XXXX, XXXX XXXX XXXX XXXX Account # XXXX, XXXX XXXX XXXX Account # XXXX, XXXX XXXX Account # XXXX, XXXX XXXX Account # XXXX, US dept of ed Account # XXXX, US Dept of ED Account # XXXX, XXXX Account # XXXX, XXXXXXXX XXXX Account # XXXX, US DEPT OF ED Account # XXXX, US DEPT OF ED Account # XXXX, XXXX XXXX XXXX Account # XXXX, XXXX XXXX XXXX # has violated my rights.
XXXX5 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": "When I applied for a Home Equity Loan, an issue of a delinquent loan showed up on my credit report. I did not borrow the {$3000.00}. I have never heard of Seedfi, XXXX or XXXX XXXX XXXX until it showed up as a delinquent account on my credit report.
It showed up recently and not when the loan was originated so it is probably a problem with the reporting rather than the initial loan."
}
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 was affected by the Equifax credit breach. I wanted to freeze my account with Equifax. I attempted to do it online yesterday and it said that I needed to call to get it done. I called yesterday and they said that their system was down for a security update and were unable to process the request. They told me to call back later, which I did. I tried to place a security freeze today and got the same error message telling me to call them. I have now spent almost 12 hours either on the phone or waiting for a phone call from Equifax to get my credit report frozen. Today, on separate occasions, they said they were unable to do it over the phone and that it had to be done online, that their system was down for a security update and that I needed to call back later, and that I gave incorrect information and needed to fax them documents. I was given a random 888 number to fax personal documents to, which I am not comfortable doing. It feels like they keep dodging my request. Since I never wanted an account with them, never signed up for it, and they are the ones that allowed my data to be compromised, it should be a lot easier to put a security freeze on my account than this."
}
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": "Credit reporting, credit repair services, or other personal consumer reports",
"Sub-product": "Credit reporting",
"Consumer complaint narrative": "I am having a hard time getting approved for credit it has been brought to my attention there there are accounts showing on my credit provide with negative reporting. The negative accounts on my credit account does not belong to me and I have never opened these accounts. I am being told I need to follow these steps in order to have these account removed from my credit profile"
}
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": "Savings account",
"Consumer complaint narrative": "I opened an online savings account with Citibank. As part of the account opening process, I transferred a substantial amount of money into the account. At the conclusion of the process, I received an error message and was never provided a full account number. I only received the last four digits by email. However, I did receive notice from my transferring bank that Citibank has received the funds. Without the full account number, I cant get online access to my account, and cant even get access to my account over the phone. I am told I am supposed to wait another 7-10 days to get my account information in the mail before I can access my account, which would mean the bank does not give me access to my funds for up to 14 days after I open up the account. This is a scam. The bank is putting all sorts of roadblocks up so that they can hold my funds and not provide me access to my accounts for weeks. The online application process misrepresented that I would have immediate access to the funds online, which is not true. I had to call over 5 times, and repeatedly was disconnected without my consent. I requested to speak to a supervisor, and was eventually connected to someone named XXXX, who refused to give her last name, but provided the code XXXX. She said I could not access my account until I received the full account number in the mail. I asked to speak to another supervisor and she said it would take them 3 business days before someone could call me back. Citibank has grossly misrepresented its terms in the application process. This is a scam that could be very, very harmful to consumers if they transfer funds into an account based upon representation that immediate access to funds will be provided, when in reality the bank wont give you access to your money for two weeks or more."
}
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 card",
"Sub-product": "Not Available",
"Consumer complaint narrative": "On XXXX/XXXX/2016 to XXXX/XXXX/2016, I was a victim of fraud for my capital one credit card. In lieu of this, capital one cancelled my credit card. I had a good standing for my credit card, making payments on time and trying to rebuild my credit. Someone opened up another card and I was not notified. I do n't think capital one asked for my information before letting this fraud occur. How can someone just call and open up an account by saying I moved and my card was stolen without capital one calling my phone to make sure it was me? I am devastated about this. And how can they just rip my credit card from my life because of fraud? They gave me a new number for a new card and made up a secret code and question to be answered before anyone can get information yet, still took my credit away from me. I did nothing wrong at all. I do n't understand why I cant have my card."
}
Output:
{
"Issue": "Credit card protection / Debt protection",
"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": "I received a letter from Credit Corp Solutions on XX/XX/01. They are attempting to collect a debt on behalf of XXXX. Credit Corp phone number is XXXX. Their reference number is XXXX. They provided the last four digits of the XXXX account number as XXXX. The amount they are attempting to collect is {$38.00}.
I called Credit Corp to inquire about this. I have never had a XXXX account. The letter had my name XXXX XXXX and my address XXXX XXXX XXXX XXXX GA XXXX on the letter. When I called they informed me that the email address attached to this account was XXXX. I informed them that this email address was not mine but my ex-wife 's. They told me I still had to pay or it would report on my credit report. They offered to settle for {$20.00}. I declined the offer because it isn't mine.
I called XXXX and they have no record of the account. They have no accounts for me based on my name, email or SSN. I requested a letter from both Credit Corp and XXXX clearing this matter and both said there was nothing they could do.
This is not mine and there is no evidence to the contrary. I do not want anything reported against me to the credit bureaus and I would like a letter from the creditors assuring me this has been cleared up and will not be an issue going forward."
}
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": "mcm refuses to report payments to credit report XXXX XXXX is charging for a credit card that we did not sign up for or recieve"
}
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": "Auto debt",
"Consumer complaint narrative": "The laws of the state of Pennsylvania clearly states that when a repossession takes place, that there must be at least two auctions completed before there is an upset price pertaining to collection purposes. However, in fiscal year 2017, being that my family runs a XXXX business, we suddenly faced several financial issues that caused this repossession. Accordingly, I am requesting a reasonable settlement to this collection. The vehicle loan was for a high interest rate, and the payment was very high. I would appreciate an offer in compromise either through my email or direct mail, and Ill do my best to make that payment to benefit all parties."
}
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": "I recently obtained a copy of my credit report and noticed charges for a medical debt that does not belong to me and obviously a result of identity theft. i sent the bureaus letters asking them to verify the accuracy of the account and to send me all applicable proof as far as account statements, applications, signatures on applications services rendered but i haven't received a response. This is fraudulent and needs to be removed from my account."
}
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": "Between XXXX - XXXX I acquired long-term installments loans concurrently with the following companies : Check N Go, XXXX XXXX, XXXX, XXXX XXXX XXXX, and XXXX.
I struggled to pay the installments for each of these loans and have reached the conclusion that these businesses did not adhere to the laws 1041.5 ( Ability-to-repay determination required ) and 1041.4 ( Identification of unfair and abusive practice ).
In regard to Check N Go, on some occasions I was allowed to make a partial payment toward my existing installment loan in order to apply for a greater loan. This may fall under the definition of " rollover '', in which is prohibited in California.
Additionally, XXXX XXXX allowed me to obtain a Payday Loan and Installment Loan at the same time.
During this horrific period, I was robbing Peter to pay Paul and consequently was stressed beyond belief."
}
Output:
{
"Issue": "Struggling to pay your loan",
"Sub-issue": "Not Available"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "" Trans Union had a glitch in their system that violated the rights of their consumers under section 6.11. As a result, I have been gravely affected and my score is suffering as a result. No notice was ever given which is also a violation and I am requesting that ALL inquiries be removed. Also notice.d that my date of birth was incorrect"
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Personal information"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account 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": "Due to the fact that my problem does n't have a best way to describe my issue above I have to explain myself. I had an Auto Loan with XXXX XXXX that is paid off but showing a balance. I spoke with my Personal financial Consultant on this matter XXXX XXXX XXXX ask me to get a letter from XXXX XXXX showing the balance is " XXXX ''. I like to address the issue with you concerning TransUnion reporting tactics on my credit report. This is not a frivolous dispute and the word dispute needs to be removed off my credit report because this is a legitimate case and I will be providing the letter from XXXX XXXX. So, that TransUnion can change the balance owed to XXXX and take the word dispute off my credit report."
}
Output:
{
"Issue": "Incorrect information on credit report",
"Sub-issue": "Account 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": "Other personal consumer report",
"Consumer complaint narrative": "On my personal identification it states that I went by a former name. This is incorrect and I would like it removed because I never went by this name. My name is very common and I don't want to be confused with anyone else! I know this may seem frivolous to anyone else but with identity theft being so rampant these days, I would like that information taken off of my report as it is incorrect. I just don't want it to cause me any problems in the future, I have enough problems to deal with in the present!
Thank you for your time and I truly hope that this is not considered a frivolous complaint! Even though it may not seem life-threatening or earth-shattering it does mean a lot to me to have the incorrect information removed.
Respectfully yours"
}
Output:
{
"Issue": "Incorrect information on your report",
"Sub-issue": "Personal information incorrect"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Payday loan debt",
"Consumer complaint narrative": "This is a payday loan, I was told by a lawyer that is illegal in Virginia and not to pay this.
They have been calling multiple family members and now have called my father telling him it was a loan that we were paying and had a small amount remaining that we need to take care of. And it wasn't much but we needed to do this.
They are also calling me and my wife 3-4 times a day."
}
Output:
{
"Issue": "Threatened to contact someone or share information improperly",
"Sub-issue": "Talked to a third-party about your debt"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Debt collection",
"Sub-product": "Medical debt",
"Consumer complaint narrative": "XXXX is continuing to report errors and inaccurate information on my credit Report from the collection agency LockHart Morris & Montgomery original creditor XXXX XXXX XXXX on three separate accounts.
Federal law requires that lenders report the original delinquency date of the account, and that collection agencies carry over that date so that any collection account can be deleted at the same time. A collection account is considered a continuation of the original debt.
It is a violation of law for a collection agency to report old past-due amounts as if they are new again when the debts are sold. If an agency persists in reporting old debts As per the FCRA, only accurate and timely information may be listed on my credit report. Section 609 of the FCRA gives me the right to see what the furnisher LockHart Morris & Montgomery ( such as a lender, collector, or court ) sent to the credit reporting agency as evidence that the information is correct. The information needs to be verifiable, so if its not, it should be removed from my credit report immediately."
}
Output:
{
"Issue": "Attempts to collect debt not owed",
"Sub-issue": "Debt is not yours"
} |
You are a customer complaint classification assistant trained to analyze and classify complaints in the finance and insurance domain. Your task is to analyze customer complaints based on the provided product, sub-product, and consumer complaint narrative, and accurately determine the issue and sub-issue.
Input Format:
{
"Product": "<product_name>",
"Sub-product": "<sub_product_name>",
"Consumer complaint narrative": "<detailed_complaint_text>"
}
Output Format:
{
"Issue": "<main_issue>",
"Sub-issue": "<specific_sub_issue>"
}
Follow these guidelines carefully:
1. Ensure the output is concise, domain-specific, and follows the given format strictly. Do not provide additional data.
2. If the Issue field can be classified based on the available data, assign the most relevant issue, even if other fields (like Sub-issue) are left blank.
3. If the Sub-product and/or Sub-issue fields are missing, infer these fields from the context of the complaint narrative, or leave them empty if unclear.
4. If you encounter vague or unclear information that does not match predefined categories, classify it based on the closest matching category.
5. Ignore any masked or obfuscated data (such as the character 'XX') in the complaint narrative. Focus on the substance of the complaint without being influenced by these markers. For example, if the complaint narrative includes something like "I tried to open an account but XX is preventing me," you should treat it as a normal complaint related to account opening, without considering the obfuscated "XX."
6. Ensure that each complaint is classified in the most accurate and consistent manner possible.
Input:
{
"Product": "Bank account or service",
"Sub-product": "Checking account",
"Consumer complaint narrative": "Santander makes it impossible to close a bank account with them. I attempted to close my bank account by sending an email to Santander requesting my account to be closed, however they stated that they can not do this via email and would have to call. I then called them on their banking phone number and they told me that the " Bank Account Closing Department '' was closed, despite it listed as being open 7 days a week on their website here : XXXX Not closing my account after repeated requests is a violation of consumer protection laws, and I am now demanding that Santander close my account, unless they want to be taken to court for willful negligence. This is the worst experience that i 've ever had with a banking institution, and with these practices, I do n't see how they have n't been sued already."
}
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": "Credit reporting",
"Sub-product": "Not Available",
"Consumer complaint narrative": "My dispute is over the fact that my husband and I owe to the same creditors. Payment history is the same. His FICO score is XXXX points more than mine.
Trans Union - reporting that I owe XXXX mortgages on our house XXXX - Reporting my house is a business.
XXXX - His FICO score is XXXX points more than mine."
}
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 card or prepaid card",
"Sub-product": "General-purpose credit card or charge card",
"Consumer complaint narrative": "Credit line of {$1000.00} has turned into. Almost {$1900.00} in fees on a account that is not open"
}
Output:
{
"Issue": "Fees or interest",
"Sub-issue": "Problem with fees"
} |
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