- H2O Driverless AI can build claim fraud models from your own claims, policy, payment and investigation history, with reason codes on every score.
- Claim scores sit above your existing screening rules and claims system, so neither is replaced and adjusters keep working in the tools they know.
- An agent can verify facts a claimant reports, such as the weather at the time and place of a loss, and return a structured summary for review.
- Every model ships with AutoDoc documentation, and the platform runs on-premises, air-gapped, private cloud or cloud.
Your SIU can only work so many referrals
Your screening rules catch the schemes you already know, such as staged accidents, inflated repair estimates and claims filed soon after a policy starts. They also refer a steady stream of claims that turn out to be legitimate, and every weak referral takes investigator time away from a strong one.
Fraud does not wait for the rules to be updated. Organized rings spread activity across claimants, providers and repair shops so that no single claim looks unusual, and the pattern shows up only when someone connects the files. When you do refer or deny a claim, you need a documented reason that stands up to a complaint or an examination, and a score nobody can explain does not provide one.
How AI claims fraud detection works
H2O can be used to build explainable claim-scoring models in H2O Driverless AI that sit above the screening rules you run today, and to add AI agents that check the facts a claimant reports before a referral is made.
Step 1: score each claim at first notice of loss
Start with claim, policy, claimant, provider and payment data, joined to the outcomes of past SIU investigations. H2O Driverless AI trains classification models on which past claims proved fraudulent, and automatic feature engineering builds signals such as time from policy start to loss and repeat parties across claims.
Step 2: catch what the rules have not seen
Anomaly detection adds a second view for claims that match no known scheme, so new patterns reach your SIU as flagged claims with reasons instead of surfacing months later.
Step 3: explain every score and route the work
Each claim receives a fraud score and reason codes that show which factors drove it. Your team sets the routing policy, so low-risk claims stay on the fast track and high-risk claims go to the SIU.
Step 4: verify reported facts with an agent
An AI agent checks facts that adjusters otherwise look up by hand, such as the weather, date and location behind a reported loss, and returns a structured summary for the adjuster or investigator to review.
Step 5: read the claim file
H2O Document AI can read claim forms, repair estimates and police reports with intelligent character recognition, returning structured fields that feed both the model and the agent.
What stays with your team
H2O sits above your existing claims system and screening rules. Your adjusters keep working in the tools they use today, and your team decides how scores route claims and whether a flagged fact changes a referral.