Key takeaways
- H2O Driverless AI builds approval, limit and default models from bureau data plus alternative data such as transactions and repayment behavior.
- Every decision carries reason codes, so you can explain an approval, a decline or a limit to the customer and to your regulator.
- The same data supports affordability checks, fraud screening at onboarding, and offers to good repayers.
- Models deploy as low-latency scoring pipelines that return a decision in milliseconds.
Approve at checkout without lending more than customers can repay
Your customers expect an answer in the time it takes to pay. Many of them have thin credit files, so a traditional bureau score says little about whether they will repay.
Approve too freely and losses and complaints rise. Decline too often and merchants send their checkout volume to a competitor. The rules are also moving: you need to show that each customer could afford the plan you offered, and why you decided the way you did.