- 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.
How AI KYC automation works
Your financial crime and onboarding teams build explainable models on the customer data you already hold. Document AI reads the paperwork, the models score the risk, and your analysts make the calls that need judgment.
Step 1: read the documents
Document AI extractors in Enterprise h2oGPTe turn identity documents, proofs of address and company registration papers into schema-validated fields, using intelligent character recognition rather than basic OCR. Fields that do not match the application are flagged for review.
Step 2: match the customer to what you already know
H2O Driverless AI trains classification models on record pairs your team has already confirmed or rejected, so the model learns when two records with small differences describe the same person. Each new applicant is linked to existing relationships with a match score, instead of a fixed rule that misses a changed address or a transposed digit.
Step 3: score the customer's risk
Automatic feature engineering builds features from customer profile, product, geography, channel and early account behavior. A risk model rates each customer, and anomaly detection flags profiles that look unlike your existing customer base.
Step 4: route the work
Your policy decides what the score does. Low-risk customers continue through onboarding, higher-risk customers move to enhanced due diligence, and reason codes tell the analyst which factors drove the rating. Your screening and case systems stay in place.
Step 5: document and refresh
Documentation is written while the model trains, and reason codes travel with every score. The same customer foundation supports periodic reviews, so ratings can be refreshed as behavior changes.
Keeping identity data inside your environment
Customer identity data stays in the environment you choose: on-premises, air-gapped, private cloud or cloud.