Context

These are real world complaints received about financial products and services. Each complaint has been labeled with a specific product; therefore, this is a supervised text classification problem. With the aim to classify future complaints based on its content, we used different machine learning algorithms can make more accurate predictions (i.e., classify the complaint in one of the product categories)

Content

The dataset contains different information of complaints that customers have made about a multiple products and services in the financial sector, such us Credit Reports, Student Loans, Money Transfer, etc. The date of each complaint ranges from November 2011 to May 2019.

Acknowledgements

This work is considered a U.S. Government Work. The dataset is public dataset and it was downloaded from https://catalog.data.gov/dataset/consumer-complaint-database on 2019, May 13.

Inspiration

This is a sort of tutorial for beginner

The Consumer Complaint Database is a collection of 719,794 complaints, on a range of consumer financial products and services, sent to nearly 3,000 companies for a response. All the facts alleged in these complaints are not verified, but steps are taken to confirm a commercial relationship between the consumer and the company.

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

In 2025, Inter was the Brazilian bank with the highest number of customer complaints per one million clients in the country. During the second quarter of 2025, Banco Inter received over ** complaints per *********** clients. It was followed by Bradesco, with over ** complaints per million customers.

This dataset contains anonymized customer complaint records used to build a Complaint Tracking and Analytics Dashboard in Power BI. The data can be used for learning SQL data cleaning and Power BI visualization. It simulates real-world customer complaints from a banking context and enables analysis of complaint trends, categories, and resolutions. Features:- * SQL- Power BI linking * Refresh Of Data * Multiple Charts * Story-telling

"This dataset captures customer complaints tied to service and experience failures, offering critical insights into where and how breakdowns occur. Sourced from reviews across 160+ industries, it focuses on moments when expectations weren’t met — and how consumers express that failure.

Key data features:

-Complaint text classified by service failure (e.g., “agent never responded,” “damaged item,” “billing error”) -Sentiment of the review (e.g., positive, negative, neutral) -Optional metadata: company/brand, timestamp, region, platform -Resolution request tagging (e.g., refund, apology, fix, cancellation)

The list may vary based on the industry and can be customized as per your request.

Use this dataset to:

-Train AI models that triage and escalate high-frustration complaints -Monitor systemic failure trends across brands or departments -Detect CX touchpoints that drive dissatisfaction or legal risk -Develop bots and assistants that recognize emotional cues in complaints -Inform service design teams about recurring pain points

Whether for automation, empathy modeling, or escalation tracking, this dataset transforms raw frustration into structured intelligence for customer experience leaders and AI builders."

In 2022, Frontier Airlines reported ***** customer complaints for each 100,000 enplanements on domestic-scheduled operations. This was the highest rate of customer complaints among U.S. carriers.

This statistic shows the share of customers in the U.S. and worldwide by if they have a more favorable view of brands that respond to customer service questions or complaints on social media in 2018. During the survey, 47 percent of respondents from the United States indicated that they have a more favorable view of brands that respond to customer service questions or complaints on social media.

Don't forget to hit the upvote🙏🙂

The Consumer Complaints Dataset is a structured collection of 14,000 complaints filed by customers regarding financial products and services. It is designed to analyze consumer grievances, company responses, and resolution efficiency. The dataset helps in identifying patterns in customer complaints, dispute trends, resolution times, and company performance across different financial institutions.

This dataset is crucial for financial analysts, data scientists, regulatory bodies, and businesses looking to improve customer satisfaction, ensure compliance, and optimize their complaint resolution processes.

Columns-Details:

ID (int) – A unique identifier for each complaint.

Company (object) – The name of the company that received the complaint.

Product (object) – The type of financial product the complaint is about (e.g., Mortgage, Credit card).

Issue (object) – The specific issue related to the complaint (e.g., Loan servicing, Billing issues).

State (object) – The U.S. state where the complaint was filed.

Submitted via (object) – The method used to submit the complaint (e.g., Web, Phone, Email).

Date received (object) – The date the complaint was received.

Date resolved (object) – The date the complaint was resolved.

Timely response? (object) – Indicates whether the company responded in a timely manner (Yes/No).

Consumer disputed? (object) – Indicates whether the consumer disputed the company’s response (Yes/No).

State name (object) – The full name of the U.S. state corresponding to the "State" column.

Date received.1 (object) – Duplicate of the "Date received" column.

Date resolved.1 (object) – Duplicate of the "Date resolved" column.

Resolution time (in days) (int) – The number of days taken to resolve the complaint.

Year (int) – The year in which the complaint was filed.

QTR (US FLY) (object) – The quarter of the year in which the complaint was filed (e.g., Q1, Q2, Q3, Q4).

A daily data dump of NHTSA data that includes complaints filed by customers, vehicle safety ratings, recalls, and investigations. The data spans all makes and models from the year 2016 onwards. Note that it may take up to two weeks for a new complaint to make it into this dataset.

➡️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.

When it comes to making a complaint about a problem they are experiencing, consumers in the United Kingdom seemed to be somewhat hesitant, as there was, on average, a ***** percent difference between the share of consumers who experienced a problem and those who reported it to the respective organization during the measured period. In addition. the share of customers having a problem increased consistently in the last two years and amounted to about **** percent in January of 2022.

Individual informal consumer complaint data detailing complaints filed with the Consumer Help Center beginning October 31, 2014. This data represents information selected by the consumer. The FCC does not verify the facts alleged in these complaints.

Comprehensive Consumer Review Dataset from PissedConsumer

This unique, extensive dataset from PissedConsumer includes over 5 million reviews covering more than 140,000 companies globally. It is ideal for hedge funds, venture capital firms, and investment companies seeking to deepen their understanding of internal processes and predict emerging trends.

Key Features:

Volume and Coverage: Over 5 million reviews on 140,000 companies, offering a broad and precise view of consumer opinions.

Detailed Complaint Insights: Each review includes a complaint title and text, allowing for an in-depth understanding of consumer issues and typical expectations.

Desired Solutions: Data includes preferred resolutions, enabling analysis of company standards and responsiveness to consumer demands.

Device and Date Specifics: Reviews include device type and activation dates, adding further context to your analysis.

Geographical Information: Data includes company locations down to the state and city levels for precise regional analysis.

Company and Industry Data: Reviews are organized by company name and industry type, facilitating targeted research.

This unique dataset from PissedConsumer offers investment analysts valuable insights into consumer needs, business resilience, and improved investment strategies. Leverage this resource for more accurate stock price forecasting, understanding customer satisfaction levels, and assessing companies’ operational practices in competitive markets.

Category: Consumer Review Data

NI Water Annual Information Return 2020 2021 Table 5a Key Outputs Customer Complaints Data for Consumer Council for Northern Ireland

This dataset contains information on customer feedback submitted by riders of the transit system on the MTA’s website. For each piece of feedback provided, it is categorized as a complaint or commendation, and there is information provided for the agency (Buses, Subway, Long Island Rail Road, or Metro-North Railroad), the subject matter, the subject detail, the issue detail, the year, the quarter, and, if applicable, the branch/line/route.

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.

The dataset is a fictional collection of customer complaints related to a supermarket. It contains 300 rows of data, with each row representing a unique complaint. The dataset includes the following columns:

1. Complaint ID: A unique identifier for each complaint.
2. Customer Name: The name of the customer who lodged the complaint.
3. Complaint Type: The type of complaint raised by the customer, such as product, delivery, staff, etc.
4. Staff Name: The name of the staff member associated with the complaint.
5. Department: The department of the supermarket to which the complaint relates.
6. Product Details: Details about the specific product mentioned in the complaint.
7. Date of Complaint: The date when the complaint was made.
8. Store Location: The location or name of the store where the complaint was made.

The dataset provides a variety of complaints across different complaint types, staff members, departments, and store locations. The data can be used to analyze patterns, identify areas for improvement, and develop strategies to enhance customer service in the supermarket.

Please note that this dataset is entirely fictional and created for demonstration purposes.

In 2022, complaints relating to identity theft were the most numerous, with ********* complaints being lodged with the Federal Trade Commission (FTC) in the United States. Imposter scams, credit bureaus, information furnishers, and report users, online shopping and negative reviews, and banks and lenders rounded out the top five consumer complaints with the FTC.

Equal Opportunity Commission Annual Report Data – Customer Satisfaction with Complaint Handling Services for the year