Key takeaways
- 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.