Key takeaways
- H2O Driverless AI builds customer risk and record-matching models from your onboarding, KYC and case history, with reason codes on every score.
- Document AI extractors in Enterprise h2oGPTe read identity documents and proofs of address into structured fields, checked automatically against the application.
- Low-risk applicants move through onboarding faster, and your analysts spend their time on the applications that need enhanced due diligence.
- Every model ships with documentation for model risk review, and customer identity data stays in the environment you choose.
Onboarding that is thorough, fast and defensible at once
Every new customer has to be identified, screened and risk-rated before the account opens. When that work is manual, good applicants wait, and some abandon the application before it is approved.
Your customer data rarely sits in one place. The same person can appear under slightly different names, addresses and document numbers across onboarding, cards and lending systems, so risk gets assessed on a partial picture. Every rating also has to stand up later: when an examiner or internal audit asks why a customer was rated low risk, a passed rule is a weak answer, and a score nobody can explain is weaker still.