Credit Builder Card Offers Dataset

Author: Noah Gomez
Created: 1 July 2023
Published: 6 December 2023
Updated: 7 December 2023

Status: Active

Active - Continually Updated


Dataset Metadata

Product Concerned: Credit Builder Card
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.

Date Updated

7 December 2023


Description

This table hosts commercial data of  20+ credit builder cards in the market. It is a private dataset not available to the public used to identify, rank, and tailor the best offers to consumers throughout ThickCredit's product and content.

Sample (Image)

Similar Datasets

This dataset is similar to but not the same as:

  • the credit builder loan dataset,
  • the secured credit card dataset,
  • the savings-secured loan dataset, and
  • the rent reporting services dataset.

Data Fields in Table

  • Lender. The name of the lender for each offer in the dataset.
  • Year Founded. The year the lender was founded, each offer in the dataset.
  • App Dwlds (18-Sep-23). The number of downloads in the App Store and/or Play Store, for each offer in the dataset.
  • URL. A link to the offer page, for each offer in the dataset.
  • Product Name. The commercial name of the credit builder card, for each offer in the dataset.
  • URL_Product. A link to the the sub-offer page, if any, for each offer in the dataset.
  • UVP. The unique value proposition, or unique sales pitch, for each offer in the dataset.
  • Institution Type. Whether the institution is a bank, credit union, or online lender.
  • Features. Include flash credit, rolling prepaid accounts, auto-pay protection, sub-monthly reporting single purchase limits, spending limits (different than credit limits), and cash creditworthiness metrics.
  • Type. Include Daily Spending, Leave Home, Classic Credit, and Loan combo.
  • Cardholder Agreement. A link to the cardhodler agreement, for each offer in the dataset.
  • Deposit Agreement. A link to the deposit agreement, if any, for each offer in the dataset.
  • Est. Score Min. The estimated minimum credit score needed for approval, if any.
  • Income Minimum. The minimum income requirement, if any, required for  each offer in the dataset.
  • Est. Score Increase. The expected change in credit score, if any, required for each offer in the dataset.
  • Time-to-Effect (days). The number of days required on average to see results after opening the credit builder card.
  • Max Limit (Est.). The maximum amount in dollars of credit as reported to the credit bureaus, for each offer in the the dataset.
  • Min Limit (Est.). The minimum amount in dollars of credit as reported to the credit bureaus, for each offer in the the dataset.
  • Max APR. The minimum annual percentage rate, for each offer in the the dataset.
  • Min APR. The minimum annual percentage rate, for each offer in the the dataset.
  • Paid Membership. Whether the offer is part of a mandatory paid membership required by the lender, for each offer in the dataset.
  • Max Member Fee (mth). The maximum amount in dollars of the membership, if any,, for each offer in the the dataset.
  • Min Member Fee (mth). The minimum  amount in dollars of the membership, if any,, for each offer in the the dataset.
  • Credit Check. Whether the lender will run a hard inquiry, soft inquiry, or no inquiry during the application process.
  • Security Deposit. The amount of the security deposit, if any, for each offer in the the dataset.
  • Annual Fee. The amount of the annual fee, if any, for each offer in the the dataset.
  • Late Payment Fee. The amount of the late payment fee, if any, for each offer in the the dataset.
  • Returned Payment. The amount of the returned payment fee, if any, for each offer in the the dataset.
  • Foreign Transactions Fee. The amount of the foreign transaction fee, if any, for each offer in the the dataset.
  • Advance on Paycheck. Whether a paycheck advance is allowed, and the cost if allowed, for each offer in the the dataset.
  • Cash Advance (ATM) Fee. Whether a cash advance is allowed, and the cost if allowed, for each offer in the the dataset.
  • Balance Transfer. Whether a balance transfer is allowed, and the cost if allowed, for each offer in the the dataset.
  • Signup Bonus. Whether a signup bonus, and the amount if there is one, for each offer in the the dataset.
  • Limit Increase. Whether a limit increase is allowed for each offer in the the dataset.
  • Reward Type. If there is a reward, whether it is Cash back, Partner discounts, Partner discounts, or Cash rewards.
  • Reward Amount High. The highest reward amount in points, cash, or percentage.
  • Reward Amount Low. The lowest reward amount in points, cash, or percentage.
  • States Available. The state where availabe (all 50 states are avaiable through at least 1 lender).
  • CU Membership Field. The membership restrictions, usually geographic or by participating organization, that credit unions impose, for each credit union offer in the dataset.
  • Cancellation. When and in what way the card can be cancelled, if any, without penalty, for each offer in the dataset.
  • Single-use cards. Whether single-use card numbers and security codes are avaialble, for each offer in the dataset.
  • Payment Alert. Whether payment or other transaction alerts are avaialble, for each offer in the dataset.
  • App. Whether the credit builder card offers an accompanying mobile application, for each offer in the dataset.
  • Card Locking in App. Whether a card locking feature is available in the app, if an app exists, for each offer in the dataset.
  • SSN Required. Whether the borrower's social security number is required during application, for each offer in the dataset.
  • Subscription Tracker. Whether a subscription tracking feature is available for each offer in the dataset.
  • Interest on Savings. Whether the cash collateral, if any, earns interest in a savings account or certificate of deposit during the use of the card, for each card in the dataset.
  • Deposit. Whether the rolling deposit is stored in a Checking account, Rolling secured account, or other.
  • Pre-Approval. Whether pre-approval is available for each offer in the dataset.
  • Automated payoff. Whether automated payoff is available for each offer in the dataset.

Data Quality & Validation

Relevance

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.

Collection

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

Accuracy is reinforced by dual-authentication and continual updating of evolving figures, such as interest rates that may evolve with the prime rate.

Completeness

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.

Recency

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

Cleaning

Cleaning is not required for this dataset because the data is collected raw in the field and not repurposed from other sources.

Privacy

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

Validation

Outliers do not impact raw data, only analysis. We use a conservative framework that eliminates outliers with a material impact on analytical metrics that can be helpful to consumers.

For example, not all cards provide an estimated score improvement figure or an accompanying timeline.

To provide and aggregate view for this metric, we limit the calculation to cards that publish this information.

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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