A groundbreaking CLARA Analytics study on fraud detection in property and casualty insurance claims has revealed that advanced analytical methods can accurately identify potential fraud indicators just two weeks after a claim is filed, significantly earlier than traditional approaches.
The research, completed in November 2024, analyzed 2,867 claims from 2020 to 2024 using an unsupervised machine learning approach. The study demonstrates that cohort modeling across claim development periods can effectively identify cost and treatment outliers while mapping connections between providers and attorneys that may indicate fraudulent activity.
“This research represents a significant advancement in how the insurance industry can approach fraud detection,” said Pragatee Dhakal, Director of Claims Solutions at CLARA Analytics. “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.”
Key findings from the study include:
The FBI estimates that insurance fraud costs the industry approximately $40 billion annually, excluding medical insurance. These costs ultimately affect policyholders through increased premiums.
The study also highlighted the importance of the “Sentinel Effect” — where the awareness of being monitored leads to improved behavior. Insurers known for effective fraud detection are less likely to be targeted, offering a preventive advantage that extends beyond direct cost savings.
“What’s particularly promising about this approach is that it doesn’t rely on preestablished fraud indicators,” Dhakal added. “By using unsupervised learning techniques, the system can potentially identify novel patterns of fraudulent activity that might not match historical cases.”
The research team employed cohort modeling across claim development periods and mapped frequency connections to providers and attorneys. This methodology allows for a more comprehensive view of potential fraud patterns than traditional indicator-based approaches alone.
CLARA is broadening its network analysis to incorporate insights from medical and legal data, aiming to reveal hidden connections. Leveraging AI-driven insights augmented by agentic reasoning, CLARA’s solution interprets complex claim scenarios and generates actionable intelligence to support decision-making and optimize claims handling.
Industry experts suggest these findings could transform how insurers approach fraud detection, combining human expertise with sophisticated analytics to create more effective prevention systems.
About CLARA Analytics
CLARA Analytics is the leading AI as a service (AIaaS) provider that improves casualty claims outcomes for insurance carriers, MGAs, reinsurers, and self-insured organizations. The company’s platform, CLARAty.ai, applies image recognition, natural language processing, and other AI-based techniques to unlock insights from medical notes, legal demand packages, bills, and other documents surrounding a claim. CLARA’s predictive insights give claim professionals augmented intelligence that helps them reduce claim costs and optimize outcomes for the carrier, customer and claimant. CLARA’s customers include companies from the top 25 global insurance carriers to large third-party administrators and self-insured organizations. Founded in 2017, CLARA Analytics is headquartered in California’s Silicon Valley. For more information, visit www.claraanalytics.com, and follow the company on LinkedIn and @CLARAAnalytics.
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Tags: CLARA Analytics, claims optimization, fraud detection, insurance fraud, claims adjusters, claims managers, insurtech, claims, claims management, artificial intelligence, AI, generative AI, GenAI, machine learning, workers compensation, workers comp, predictive analytics, commercial insurance, general liability
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