- 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.
How AI for buy now, pay later works
Your risk team trains models on your own application and repayment history, adds alternative data, and scores every checkout in real time. Your credit policy stays in charge of the decision.
Step 1: bring bureau and alternative data together
Combine application data and bureau data where you have it with alternative sources: transaction history, repayment behavior on earlier plans, merchant and basket data, and device signals.
Step 2: build approval, limit and default models
H2O Driverless AI runs automatic feature engineering and trains classification models for default risk and regression models for limits. Custom recipes let your team add features that describe repayment behavior over time.
Step 3: decide and explain at checkout
The scoring pipeline returns a score, a suggested limit and a repayment schedule in milliseconds. Explainable AI reason codes show what drove each decision, for the customer notice and for your audit file.
Step 4: monitor and retrain
Customer behavior shifts with the season and the economy. H2O MLOps tracks model drift and supports retraining, so decisions stay accurate as your book grows.
Beyond the credit decision
The same customer and repayment data can screen for fraud at onboarding and support identity checks. It can also find good repayers who are likely to take up a higher limit or another offer, without building a second data pipeline for each job.