- A claims fraud model scores every claim before payment from claim, member, provider and history data, so review happens while the money is still in your hands.
- Anomaly detection flags billing patterns that match no known scheme, so new fraud types reach an investigator early.
- The same scores identify likely overpayments, such as duplicate, upcoded or unbundled claims, and let low-risk claims pay without delay.
- Reason codes show investigators which factors drove each score, and H2O runs beside your current claims and integrity systems.
Catch fraud and overpayments before the claim is paid, not after
Fraudulent and improper claims raise the cost of care, slow down valid claims and push premiums up for members. When your team finds them after payment, recovery takes months or years, and much of the money never comes back.
Your investigators cannot review every claim. Rules catch known schemes, but they also flag legitimate claims, and each false alarm costs review time that should go to the claims that matter.
Confirmed fraud labels are also rare and arrive late. A model has to be built for that reality: learning from the cases you have confirmed, and flagging what looks unlike anything you have seen.
How medical claims fraud and overpayment detection works
Step 1: Start from the data you already hold
Join claims, member, provider and payment history with the outcomes of past investigations and recoveries.
Step 2: Learn the patterns behind confirmed cases
H2O Driverless AI trains classification models with automatic feature engineering, such as a provider's billing against peers and a member's claims against their own history.
Step 3: Find what the rules miss
Anomaly detection adds a second view for billing that matches no known scheme.
Step 4: Score before payment
Each claim gets a risk score and reason codes. Your policy decides how scores route work: high-risk claims go to review, and low-risk claims move to payment.
Step 5: Monitor and retrain
Watch for model drift as schemes, codes and policies change, and retrain on each quarter's confirmed cases.
Claims overpayment detection, not just fraud
Not every improper claim is fraud. Duplicate submissions, upcoding, unbundled services and billing that does not match the member's coverage all cost money, and the same models can flag them.
Overpayment scores go to your payment integrity or recovery team with the reason attached, so you can correct a claim before payment instead of chasing the money afterward.