Fraud detection in insurance refers to the systems and processes used to identify fraudulent claims, applications, or provider billing before payment is made.

Insurance fraud costs the industry an estimated $80-100 billion annually in the US alone. It spans opportunistic padding (inflating legitimate claims by 20%) to organized rings (staged accidents, fake identities, provider collusion). Detection capability directly impacts loss ratios and, by extension, pricing competitiveness.

Traditional fraud detection relied on red-flag rules and Special Investigations Unit (SIU) referrals. These catch obvious fraud but miss sophisticated schemes and generate high false positive rates. Modern approaches layer machine learning on top: anomaly detection across claim patterns, network analysis linking related parties, and predictive models trained on historical fraud outcomes.

The operational challenge is balancing detection with customer experience. Aggressive fraud flagging delays legitimate claims and frustrates honest policyholders. The goal is high-confidence identification: flag the clear fraud for investigation, fast-track the clearly legitimate claims, and focus human judgment on the uncertain middle.

Related terms: Claims leakage, Claims triage, Straight-through processing rate