Author: Noah Gomez
Created: 1 July 2023
Published: 3 December 2023
Updated: 30 May 2024
Active - Continually Updated
Product Concerned: Credit Builder Loan
File Type: Created in Microsoft Excel, Hosted Privately
Availability Status: Private
Mother Catelog: Private Datasets
Mother Catelog Description: A collection of datasets collected, processed, refined, and analyzed by Thick Credit.
30 May 2024
This data table hosts commercial data of over 200 credit builder loans in the market. It is a private dataset not available to the public used to identify, rank, and tailor the best offers to consumers in ThickCredit's product and content. It is the most comprehensive collection of credit builder offers in the market and is updated every month.
This dataset is similar to but not the same as:
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 like phone number and email.
All data is continually collected in the field by human agents. Most data comes directly from lender websites and representatives contacted via email or phone. In some instances, automated collection techniques are used after the process has been proven and vetted by a human agent.
Accuracy is reinforced by dual-authentication and continual updating of evolutionary figures, such as interest rates that may evolve with the prime rate.
In some instances, lenders refuse to communicate data points or require an application. If a reasonable substitution through peer-comparison or nearest-neighbor techniques can be used, we use these or other best practice to complete the data. Otherwise, we remove the lender from any analysis where the empty data would corrupt results. The sample is large enough to remain representative despite the removal of a small number of records.
The data is updated continually to include new data and new offers entering the market.
Cleaning is not required for this dataset because the data is collected raw in the field and not repurposed from other sources.
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. For example, fully-secured credit builder loans and their savings-secured sibling products do not usually provide maximum or minimum principal because the borrower's savings determines principal, so these lines are excluded from credit builder averages to ensure consumer borrowers have the most representative figures available.
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.
Thick Credit is not a credit repair organization, a credit conseling agency, or a debtor education providor. It does not act on your behalf to communicate with credit reporting agencies or provide pre-bankruptcy credit counseling and pre-discharge debtor education for bankruptcy.
©2024 Thick Credit, All right reserved.