The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.

Objective

The objective of this analysis is to explore and analyze the latest CFPB Consumer Complaint Database to uncover trends and insights.

Dataset Description

This dataset contains consumer complaints about financial products and services submitted to the Consumer Financial Protection Bureau (CFPB). It includes information such as the product involved, the issue, the company, and the consumer's narrative.

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database.

CFPB Complaints

A dataset of 7M complaints from the Consumer Financial Protection Bureau (CFPB), from 12/01/2011 to 01/02/2025. For descriptions of each column, please see consumerfinance.gov/complaint/data-use.

  More Details

A description from the CFPB website:

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming… See the full description on the dataset page: https://huggingface.co/datasets/davidheineman/consumer-finance-complaints-large.

This dataset is essentially a 4 year snap shot of complaints filed with the Consumer Financial Protection Bureau ranging from January 2019 to January 2, 2023. It has information that is expected such as dates, companies, products and sub products, issues, states and zipcodes, and consumer complaint narratives. I was originally interested in this dataset because the CFPB heavily regulates the mortgage industry, a field I am in currently. Upon sifting through this mountain of data I quickly was presented with numerous opportunities for further analysis projects. Maybe opportunities will present themselves to you too.

Each week we send thousands of consumers' complaints about financial products and services to companies for response. Those complaints are published here after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaint narratives are consumers' descriptions of their experiences in their own words. By adding their voice, consumers help improve the financial marketplace. The database generally updates daily.

Consumer Complaint Database from CFPB

Each week we send thousands of consumers’ complaints about financial products and services to companies for response. Those complaints are published here after the company responds or after 15 days, whichever comes first. By adding their voice, consumers help improve the financial marketplace.

Each week we send thousands of consumers' complaints about financial products and services to companies for response. Complaints are listed in the database after the company responds or after they’ve had the complaint for 15 calendar days, whichever comes first.

We publish the consumer’s description of what happened if the consumer opts to share it and after taking steps to remove personal information. See our Scrubbing Standard for more details

We don’t verify all the facts alleged in these complaints, but we take steps to confirm a commercial relationship. We may remove complaints if they don’t meet all of the publication criteria. Data is generally refreshed nightly. Company level information should be considered in context of company size and/or market share.

More about the Consumer Complaint Database | How we use complaint data | Technical documentation

The Consumer Complaint Database is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first. Complaints referred to other regulators, such as complaints about depository institutions with less than $10 billion in assets, are not published in the Consumer Complaint Database. The database generally updates daily.

Use this dataset to classify what product or service a complaint is pointing to, given the complaint narrative provided by the customer. Link: https://catalog.data.gov/dataset/consumer-complaint-database

These are complaints we’ve received about financial products and services.

Debt Collection Complaints (India)

Investigating the Consumer Experience

By Lewis Kirvan [source]

About this dataset

The Consumer Financial Protection Bureau (CFPB) Complaint Database is a treasure trove of over 675,845 complaints, demonstrating the issues that accompany consumer financial products and services. Dating back to July 2011, this data has been used to help connect consumers with the companies they need in order to rectify various debt and lending problems. By analysing this data, CFPB can pinpoint trends in the marketplace which will allow them to do a better job in enforcing federal consumer financial laws as well as provide insight for rule writing.

This comprehensive dataset contains all manner of information about each complaint - from date of submission and product it regards, through to how the company responded and whether or not the consumer disputed that response. It also contains additional details such as location (ZIP code and state) as well particular tags associated with each complaint. Above all else, though, it includes an intimate account of what happened from the perspective of the customer themselves - something which provides context and invaluable insight into their experience of dealing with these large companies.

With this wealth of data at our fingertips we have been able to uncover some truly eye-opening results about both our economy’s banking systems - giving us an understanding far deeper than ever before about how big businesses interact with their customers when it comes time for repayment or loans management.. Furthermore since these details regarding specific complaints are so detailed we can be sure that any conclusions reached are more accurate than ever before!

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How to use the dataset

Research Ideas

• Utilizing this dataset to analyze and visualize customer trends in financial services industries, such as identifying issues related to mortgages, credit cards and bank accounts; or tracking how complaints differ by geographical location or product type.
• Leveraging the data to create interactive complaint dashboards that provide insights on customer satisfaction levels with different companies. This could be expanded into a predictive model which extrapolates future customer satisfaction trends based on historical complaints data.
• Predictive analytics that utilize this data set to identify at risk customers before they submit a complaint, through classifying users based on demographics, products used and past interactions with the company's website/services

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: full_dataset.csv | Column name | Description | |:---------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | Date received | The date the complaint was received by the CFPB. (Date) | | Product | The type of product the complaint is about. (Text) | | Sub-product | The specific sub-product the complaint is about. (Text) | | Issue | The issue the cons...

This dataset contains complaints about bank accounts or services as well as credit card related complaints.

The original source of the data is the Consumer Financial Protection Bureau. For further information on the data, you can consult the CFPB Field Reference.

Acknowlegement

Foto von Avery Evans auf Unsplash

Context

The Consumer Financial Protection Bureau (CFPB) is a federal U.S. agency that acts as a mediator when disputes arise between financial institutions and consumers. Via a web form, consumers can send the agency a narrative of their dispute. An NLP model would make the classification of complaints and their routing to the appropriate teams more efficient than manually tagged complaints.

Content

A data file was downloaded directly from the CFPB website for training and testing the model. It included one year's worth of data (March 2020 to March 2021). Later in the project, I used an API to download up-to-the-minute data to verify the model's performance.

Each submission was tagged with one of nine financial product classes. Because of similarities between certain classes as well some class imbalances, I consolidated them into five classes:

• credit reporting
• debt collection
• mortgages and loans (includes car loans, payday loans, student loans, etc.)
• credit cards
• retail banking (includes checking/savings accounts, as well as money transfers, Venmo, etc.)

After data cleaning, the dataset consisted of around 162,400 consumer submissions containing narratives. The dataset was still imbalanced, with 56% in the credit reporting class, and the remainder roughly equally distributed (between 8% and 14%) among the remaining classes.

Acknowledgements

• Many thanks to Author halpert3 for providing us the Clean Dataset- Github URL.
• Data is organised by CFPB.

The NSRDB is a serially complete collection of hourly and half-hourly values of the three most common measurements of solar radiation—global horizontal, direct normal, and diffuse horizontal irradiance—and meteorological data. The current NSRDB is modeled using multi-channel measurements from geostationary satellites. The older versions of the NSRDB were modeled using cloud and weather information primarily collected at airports. Sufficient number of locations and temporal and spatial scales were used to represent regional solar radiation climates accurately. Using the NSRDB data, it is possible to estimate the amount of solar energy that is historically available at a given time and location anywhere in the United States. The NSRDB is also expanding to encompass a growing list of international locations . Using the long-term NSRDB data in various models, it is possible to predict the potential future availability of solar energy in a location based on past conditions. Typical Meteorological Year (TMY) data can be derived from the NSRDB time series datasets. Visit NREL's TMY page for detailed information about this data type and its uses. The latest addition to the NSRDB is spectral datasets. Spectral datasets are calculated on demand based on user specifications of tilt and orientation. Please visit NREL's Spectral Datasets page to learn more. The NSRDB metadata has been parsed into BigQuery tables for easy subsetting and analysis. See the metadata tables here. This public dataset is hosted in Google Cloud Storage and available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

Each week the CFPB sends thousands of consumers’ complaints about financial products and services to companies for response. Those complaints are published here after the company responds or after 15 days, whichever comes first. By adding their voice, consumers help improve the financial marketplace.

Context

This data is a collection of complaints about consumer financial products and services that we sent to companies for response. Complaints are published after the company responds, confirming a commercial relationship with the consumer, or after 15 days, whichever comes first

Content

The dataset comprises of Consumer Complaints on Financial products and we’ll see how to classify consumer complaints text into these categories: Debt collection, Consumer Loan, Mortgage, Credit card, Credit reporting, Student loan, Bank account or service, Payday loan, Money transfers, Other financial service, Prepaid card. Also we will try to identify the companies from the dataset

Acknowledgements

The source of data is : https://cfpb.github.io/api/ccdb/

Inspiration

1. Supervised Problems

   a. Predict product and issue using the complaints text
   b. Using data predict complaints which will not be resolved
   

2. Unsupervised Problems

   a.Extract Company names from textual data
   b. Normalize company names so that Cap One , Capital One etc have same name
   

   c. Understand the topic of complaint using textutal data which will help in better organizing complaints in future

Problem Statement

When consumers are not happy with some aspect of a business, they choose to reach out to the customer service and might raise a complaint. Businesses try their best to resolve the complaints that they receive. However, it might not always be possible to appease every customer.

Unhappy consumers might raise follow-up questions/complaints about the resolutions provided, and this is detrimental to the business as it points to systemic failures in the Customer Support division and could lead to poor brand image. Disputed complaints which are being/have been resolved could be a critical dataset to derive essential learnings for any business.

Predicting whether a complaint resolution will be accepted or rejected by a consumer can enable a business to proactively look at complaints which might be disputed and hence save unnecessary escalation as well as their reputation. Systemic issues can be identified by noticing which complaints have a higher potential to be disputed, and customer support agents can be trained to pay more attention or enhance the quality of communication for certain types of complaints.

The Consumer Financial Protection Bureau (CFPB) in the United States receives several consumers’ complaints about the dealings of financial companies. It sends these complaints about their products and services to them for eliciting a response. The CFPB makes sure that these complaints are published here soon after the company responds or after 15 days since sending the complaint to the company.

Dataset

You have been provided with a dataset containing the following columns –

● Date received: Date when the complaint was received

● Product: Type of product identified in the complaint, e.g., “Student loan”

● Sub-product: Type of sub-product identified in the complain

● Issue: The issue raised in the complaint, e.g., “Struggling to repay your loan.”

● Sub-issue: E.g., “Problem lowering your monthly payments.”

● Consumer complaint narrative: This is a consumer-submitted description of “what happened”. Reasonable steps have been taken to remove personal information that could be used to identify the consumer

● Company public response: The response to a consumer’s complaint. It can be from a pre-set list of options, e.g., “Company believes the complaint is the result of an isolated error”

● Company: For which the complaint is about

● State: Derived from the consumer’s mailing address

● ZIP Code: Derived from the consumer’s mailing address

● Consumer consent provided: Flag to specify whether the consumer allowed the publishing of their complaint description

● Submitted via: E.g., “Web” or “Phone.”

● Date sent to the company

● Company response to consumer

● Timely response: Flag specifying if the response was timely

● Consumer disputed: Flag specifying if the consumer disputed the resolution

● Complaint ID: Identifier for each complaint

Two files have been provided.

● Training Data: Consumer_Complaints_train.csv

● Test Data: Consumer_Complaints_test.csv

Complaints Management for Financial Services Market Outlook

According to our latest research, the global complaints management for financial services market size reached USD 2.45 billion in 2024, driven by a robust focus on regulatory compliance, customer satisfaction, and digital transformation within the financial sector. The market is expected to expand at a CAGR of 9.1% from 2025 to 2033, with projections indicating the market will reach USD 5.46 billion by 2033. The primary growth factor is the increasing need for financial institutions to efficiently manage, resolve, and analyze customer complaints in real time, as regulatory scrutiny and customer expectations continue to intensify across global markets.

The growth of the complaints management for financial services market is propelled by the mounting regulatory mandates imposed on banks, insurers, and other financial entities. In recent years, authorities such as the Financial Conduct Authority (FCA), European Banking Authority (EBA), and the U.S. Consumer Financial Protection Bureau (CFPB) have established stringent guidelines for complaint handling, documentation, and reporting. Financial organizations are compelled to adopt advanced complaints management solutions to ensure compliance, minimize penalties, and maintain their reputations. This regulatory landscape has led to increased investments in both software and services that automate complaint intake, routing, resolution, and analytics, fueling market expansion.

Another significant growth driver is the rapid digitalization of the financial services industry. As customers increasingly interact with financial institutions through digital channels, the volume and complexity of complaints have surged. Financial organizations are leveraging artificial intelligence (AI), machine learning, and automation within complaints management systems to capture, categorize, and resolve complaints efficiently. These technologies not only enhance operational efficiency but also provide valuable insights into customer pain points, enabling proactive improvements in products and services. The demand for cloud-based complaints management solutions is particularly strong, as they offer scalability, real-time access, and seamless integration with other digital banking platforms.

Customer experience has emerged as a critical differentiator in the highly competitive financial services sector. Institutions are recognizing that effective complaints management is essential for fostering customer loyalty and trust. By implementing robust complaints management systems, organizations can ensure fast and transparent resolution of issues, track trends, and identify systemic problems. This customer-centric approach is driving the adoption of sophisticated complaints management platforms, particularly among retail and corporate banks, insurance providers, and fintech companies. The ability to leverage complaint data for continuous improvement and regulatory reporting further underscores the strategic importance of these solutions in today’s financial landscape.

From a regional perspective, North America currently dominates the complaints management for financial services market, accounting for the largest share of global revenue in 2024. The region’s leadership is attributed to mature financial infrastructure, early adoption of advanced technologies, and a stringent regulatory environment. Europe follows closely, driven by comprehensive consumer protection laws and the rapid digital transformation of its banking sector. Meanwhile, the Asia Pacific region is poised for the fastest growth over the forecast period, fueled by expanding financial inclusion, rising fintech adoption, and evolving regulatory frameworks. Latin America and the Middle East & Africa are also witnessing increased adoption, albeit at a more gradual pace, as financial institutions in these regions modernize their operations and prioritize customer experience.

Component Analysis

The complaints management for financial services market is segmented by component into software and services. The software segment comprises complaint intake platforms, case management systems, analytics tools, and integration modules that collectively streamline the complaints lifecycle. In 2024, the software segment accounted for the majority of market revenue, as financial institutions prioritize end-to-end automation and real-time

In the realm of customer experience, the dynamics between clients and financial institutions have spurred a plethora of complaints, rendering them a pivotal subject of study. This Kaggle dataset delves into the intricate landscape of customer complaints lodged at banks. By examining the grievances expressed by customers, this dataset offers an invaluable opportunity to uncover patterns, enhance services, and optimize the overall banking experience for present and future patrons.

Shape:

The dataset has 1473407 rows and 16 columns

Source:

Consumer Financial Protection Bureau

The columns include: - Complaint ID: int - Date received: datetime - Product: str - Sub-product: str - Issue: str - Sub-issue: str - Company public response: str - Company: str - State: str - ZIP code: str - Consumer consent provided?: str - Submitted via: str - Date sent to company: datetime - Timely response?: str - Consumer disputed?: str - Company response to consumer: str

(PENTING)

Dataset yang digunakan dalam tantangan ini merupakan versi ringan dan terkurasi dari Consumer Complaint Database, yang dikelola oleh Consumer Financial Protection Bureau (CFPB); lembaga independen di Amerika Serikat yang menangani perlindungan konsumen di sektor keuangan.

Setiap entri dalam dataset mencerminkan keluhan nyata dari konsumen terhadap produk dan layanan keuangan, lengkap dengan narasi bebas, label produk dan isu utama, tanggapan perusahaan, serta informasi tambahan lainnya. Data ini memuat berbagai informasi penting yang dapat dimanfaatkan untuk memahami pola isu yang sering terjadi dan meningkatkan kualitas layanan keuangan.

Untuk mempermudah proses eksplorasi dan khususnya modeling, versi dataset yang disediakan telah disampling secara harian dengan bobot tertentu, sehingga menghasilkan distribusi seimbang antara data teks narasi (tidak missing) dan data tanpa teks narasi. Tujuannya adalah untuk menjaga keberagaman data sambil tetap mempertimbangkan keterbatasan sumber daya komputasi peserta.

Peserta diperbolehkan menggunakan dataset asli CFPB apabila menginginkan cakupan data yang lebih luas. Penggunaan dataset tambahan dari sumber lain juga diperbolehkan, selama peserta mencantumkan sumbernya dengan jelas dan menjelaskan relevansinya terhadap solusi yang diajukan.