Federal Reserve's Consumer Credit Dataset

Author: Noah Gomez
Created: 1 July 2023
Published: 20 December 2023
Updated: 22 December 2023

Status: Active

Active - Continually Updated

Dataset Metadata

Product Concerned: Credit Builder Loan
File Type: Created in Microsoft Excel, Hosted Privately
Availability Status: Public
Mother Catelog: Public Datasets
Mother Catelog Description: A collection of datasets acquired, processed, refined, and analyzed by Thick Credit.

Date Updated

22 December 2023


This data table hosts commercial data of over 130 consumer credit metrics, as held by banks, credit unions, and online lenders. It is a public dataset available to the public and used by ThickCredit to find the best offer recommendations for consumers via content and products.

Sample (Image)

Similar Datasets

This dataset is similar to but not the same as:

  • the credit builder card dataset,
  • the rent reporting services dataset,
  • the credit builder loan dataset,
  • the secured credit card dataset,
  • the savings-secured loan dataset,
  • the credit union call report data,
  • the cardholder agreement dataset.

Data Fields in Table

  • Series Description. The name of the series for which unit, currency, and multiplier apply.
  • Unit. The unit of the data series analyzed.
  • Currency. The currency of the data series analyzed.
  • Multiplier. The of the value of the series analyzed.

Data Quality & Validation


Data is chosen and collected with consumer borrowers in mind. Data fields are limited to metrics that inform decision-making and risk-mitigation, as well as contact information. Most of the raw data must be analyzed by Thick Credit to drive insights, except for simple fields such as name and purchases.


All data is continually collected from the Federal Reserve by human agents. The process can involve manually extracting data from PDFs and other untransferrable files.


Accuracy is reinforced by dual-authentication and continual updating of evolutionary figures, such as interest rates that may evolve over time.


In some instances, cardholder agreements do not include standard metrics, or require an application to determine interest. In these cases, the agreements are excluded by default or sourced by call with the card issuer if critical to analysis.


The data is updated continually to include new data and new offers entering the market.


Cleaning includes the conversion of PDFs or other non-tabular files into spreadsheets for analysis.


The dataset is hosted natively offline and on hard drives inaccessible via internet channels.


Outliers do not impact raw data, only analysis. We use a conservative framework that eliminates outliers with a material impact on analytical metrics helpful to consumers. There are no aggregation weaknesses in the dataset as of publishing. We adjust all calculations to present the most accurate reality to consumers.

About the Author

Noah Gomez (founder of Thick Credit) is a transatlantic professional and entrepreneur with 3+ years experience in consumer finance education. He also has 5+ years of experience in corporate finance, including debt financing, M&A, listing preparation, US GAAP and IFRS.

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