- H2O Driverless AI, H2O's AutoML, trains application fraud models on your own past applications and their outcomes, with reason codes on every score.
- A fraud score at the point of application moves your fraud controls earlier, before a bad account can move money.
- Fewer false positives means fewer manual reviews, a higher auto accept rate, and a faster onboarding experience for legitimate applicants.
- The models run in any of four deployment options: on premises, air gapped, private cloud and cloud, so applicant data stays where you choose.
Stop fraud before the account opens, not after
Your onboarding team has two jobs that pull against each other. You want more good customers through the digital front door, faster, and you want every fraudster stopped before the account opens.
Fraudsters apply with stolen identities, synthetic identities built from real and invented details, and accounts that exist only to move money for someone else. Static rules catch known patterns, send too many good applicants to manual review, and still let new patterns through.
How application and account opening fraud detection works
Step 1: Start from your own applications
Begin with past applications and what happened to them: approved, declined, confirmed fraud, early default or closure. Add identity, device and channel attributes, and the application data itself, whether it came from a digital form or a scanned document.
Step 2: Learn what fraud looks like at the door
H2O Driverless AI trains classification models that separate legitimate applicants from fraudulent ones. Automatic feature engineering builds signals such as identity data that does not fit together, reuse of the same details across applications, and velocity by device or address. Anomaly detection flags applications that match no known pattern.
Step 3: Score before you decide
Each application gets a fraud risk score and reason codes before it is accepted or declined. Your policy decides the thresholds: low risk goes straight through, and only applications that need a person go to your fraud analysts.
Step 4: Keep watching after the account opens
Feed payment activity across channels back into the model, so mule behavior that appears after opening is caught early. Retrain on new outcomes as fraud patterns change.