AI for Fraud Detection in Claims

Healthcare fraud costs health systems and insurers tens of billions annually. The most common forms — upcoding, unbundling, phantom procedures, and organised provider fraud rings — leave patterns in claims data that are detectable with ML. But traditional rule-based detection catches only the most obvious cases and generates high false-positive rates that overwhelm investigation teams. ML-based detection changes the ratio.

What we build

We build anomaly detection models trained on historical claims data to identify billing patterns that deviate from peer norms — by provider, specialty, patient population, and procedure combination. Provider network analysis surfaces clusters of providers with unusually high claim volumes, co-billing relationships, or shared patient populations that indicate organised schemes. Duplicate submission detection identifies the same service billed through multiple channels. All findings are scored by confidence and financial materiality, with investigation workflow integration that routes high-priority cases to special investigations units.