Key takeaways
- 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.