Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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.
These are complaints we’ve received about financial products and services.
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
These are complaints received to the Consumer Financial Protection Bureau about financial products and services in Utah.
By Dataquest [source]
Explore the world of consumer finance with this dataset from the Consumer Financial Protection Bureau. This data set includes a rich compilation of detailed bank and credit card customer complaints and provides an invaluable insight into customer experiences in the financial sector. With over [number] records spanning across [date stream], this data set is ideal for researchers, policymakers, financial institutions and anyone looking to learn more about consumer finance.
For each record in the dataset, you'll find details such as date received, product name, issue category, consumer complaint narrative, company response to customer enquiries, state origin of complaint (where appropriate) , even tags associated with the complaint. You can also uncover how timely the company responded to customer query usingthe Timely Response? field or whether customers disputed a firm's reply with Consumer Disputed? field. Utilizing all these features along with deep analysis can aid businesses in creating better consumer experiences prepared explainable models on root causes responsible for issues like disputes or late-responses ultimately leadingtoindustrywidepolicy change that benefit customers alike. Enjoyed exploring data? Hop online to check out additional records available at https://www.consumerfinance.gov/data-research/consumer-complaints/#download-the-data . This dataset is released under Public Domain Licensing Info which meant everyone’s free access!
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It is important to note that some fields are optional and missing values are expected for those fields due to how many data points had been reported at time of collection. It can be beneficial to list all unrecorded information separately for comparison considerations if relevant for your research needs.
The data points found within this dataset can not only help you explore differences between experiences based on non-similar factors such as age but also broaden understanding into more specific discussions such as identifying racial disparities in access new types of technology like mobile banking applications versus traditional forms like checks or savings accounts. By using this tool along with other sources of information you should be able create a comprehensive picture regarding both individual's differences experiences in addition broader trends applicable across large swaths impacted people on both local and national levels. These findings could then be used potentially lead positive changes into institutions responsible providing us with these services over time alongside continued evaluation if growth has effectively occurred .
- Identifying states and specific areas with the highest number of financial complaints to target education and awareness initiatives.
- Analyzing trends in complaint investigations to improve customer service response times and accuracy rates.
- Developing a machine learning model that can accurately predict if a company will respond to a financial complaint in a timely manner
If you use this dataset in your research, please credit the original authors. Data Source
Unknown License - Please check the dataset description for more information.
File: Bank_Account_or_Service_Complaints.csv | Column name | Description | |:---------------------------------|:------------------------------------------------------------------------------------| | Date received | The date the complaint was received by the CFPB. (Date) | | Product | The type of financial product or service the complaint is related to. (Text) | | Sub-product | The sub-category of the product the complaint is related to. (Text) | | Issue | The issue the consumer is complaining about. (Text) | | Sub-issue | The sub-category of the issue the consumer is complaining about. (Text) | | Consumer complaint narrative | The narrative of the complaint provided by the consumer. (Text) | | Company public response | The public response from the company regarding the complaint. (Text) ...
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.
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
Consumer Financial Protection Bureau credit card, mortage and credit reporting complaints by zip code in Utah.
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.