Insurance fraud schemes are adaptive -- as detection rules become known, fraud rings adjust their behavior to stay below the thresholds. Machine learning does not have that limitation.
Rules-based fraud detection systems are transparent by design: they flag claims that exceed specific thresholds or match known patterns. Sophisticated fraud networks study those rules and calibrate their schemes accordingly. The cat-and-mouse game favors the fraudster who can adapt faster than rules can be updated.
Machine learning models detect fraud differently -- by identifying statistical anomalies in claim characteristics, network relationships among claimants and providers, and behavioral patterns that deviate from normal without necessarily triggering any single rule.
Carriers deploying AI fraud detection are reporting material improvements in referral quality to their SIU teams, with fewer false positives and more confirmed fraud cases. The technology is genuinely changing the economics of insurance fraud for carriers willing to invest in it.
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