Credit Builder

Predict a user's affordability to determine credit limits

The Problem

Many credit builder companies face losses when a person can’t pay off their credit limit.  Extending users a spending limit often requires conducting a risk assessment without looking at their credit history. 

The Solution

Pave’s predictive analytics allows credit risk teams to assess the appropriate credit limit and the likelihood a borrower can afford to repay a balance without resulting in NSFs or defaults. 

Products and Benefits

Reduce model development time

The 300+ attributes in our Attributes Store surface insights into a user’s cashflow including historical and projected income, debt, and ability to make repayments. Use these attributes in your risk model.

Retrieve balance trends

Pave's Balances Model computes the end-of-day balance based on inflows and outflows within an account. Get a user’s account balance history including the number of days the account has had a negative balance, single-digit balance, median balance, and more.

Predict upcoming bill and subscription payments

Pave allows you to track users’ recurring bills and predict the next bill date and amount using Bill and Subscription Detection. Receive a unified view of a user’s insights including bill, rent, utilities, BNPL, and subscription payments as well as repayment history.

Detect income and predict next paycheck deposit

Pave’s Paycheck Prediction Model returns a user’s predicted income amount and date and historical income transactions across income streams.

Detect financial accounts

Pave allows you to prompt users to connect additional accounts that have been detected in transaction history using Financial Accounts Detection. Increase acceptances and improve risk models by analyzing signals and behaviors across a user’s transaction data from additional detected accounts.

Reduce NSFs

Reduce the likelihood of an NSF with our Deposit Amount Required endpoint. Pave allows you to predict the required amount a user needs to deposit to avoid a failed payment.

Interested in exploring more of this use case?