A new study from CLARA Analytics has revealed that advanced AI-powered techniques can flag potential fraud in property and casualty insurance claims as early as two weeks after the first notice of loss, drastically outpacing traditional methods.
The findings are part of a study completed in November 2024, based on 2,867 claims spanning from 2020 to 2024.
The research used an unsupervised machine learning approach to assess claims without relying on pre-set indicators. Instead, it identified cost and treatment outliers and drew connections between attorneys and medical providers — links that conventional fraud detection tools often miss.
This novel approach offers insurers the ability to initiate Special Investigation Unit (SIU) referrals significantly earlier, potentially mitigating costly fraud losses.
CLARA Analytics director of claims solutions Pragatee Dhakal said, “This research represents a significant advancement in how the insurance industry can approach fraud detection. By leveraging advanced analytics, we’ve shown that insurers can identify potential fraud much earlier in the claims process, potentially saving billions in fraudulent payouts.”
The study found that 9% of open claims warranted SIU referral. Notably, Michigan and Arizona emerged as the states with the highest frequency of potential fraud indicators. Moreover, the machine learning model’s predictions closely mirrored actual referrals made by claims adjusters, while identifying potential fraud weeks in advance.
The FBI has estimated that insurance fraud costs the industry about $40bn annually, excluding medical insurance, often resulting in higher premiums for policyholders. CLARA’s study suggests that the introduction of AI-driven network analysis could not only flag fraud earlier but also act as a deterrent, leveraging the so-called “Sentinel Effect” — the behavioural change triggered when fraudsters are aware their actions are being monitored.
Dhakal added, “What’s particularly promising about this approach is that it doesn’t rely on preestablished fraud indicators. By using unsupervised learning techniques, the system can potentially identify novel patterns of fraudulent activity that might not match historical cases.”
As part of its ongoing innovation strategy, CLARA is expanding its network analysis capabilities, integrating legal and medical data to further uncover hidden fraud patterns. The company’s AI platform uses agentic reasoning to interpret complex claims data, providing insurers with decision-ready insights and helping them optimise their fraud detection workflows.
Experts believe these advancements mark a turning point in InsurTech, where AI-enhanced fraud detection methods could redefine industry standards and substantially reduce the financial toll of fraudulent claims.
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