CLARA Analytics reveals breakthrough in early insurance fraud detection

CLARA Analytics, a provider of artificial intelligence technology for commercial insurance claims optimisation, has released new research demonstrating that advanced machine learning can identify signs of potential fraud just two weeks after a claim is filed—dramatically ahead of traditional fraud detection timelines.

Published in November 2024, the study analysed 2,867 property and casualty insurance claims filed between 2020 and 2024.

The team used unsupervised machine learning to uncover patterns in cost and treatment data and to trace links between attorneys and medical providers that could indicate questionable activity.

The analysis showed that this data-driven approach can identify high-risk claims far earlier than conventional methods, potentially saving insurers significant resources and enabling faster investigative responses.

The study found that nine percent of open claims were flagged as strong candidates for referral to Special Investigation Units. States such as Michigan and Arizona showed higher concentrations of fraud indicators, and the model’s early predictions often aligned closely with actual referrals made later by human adjusters.

However, unlike traditional processes, these AI-driven insights emerged within two weeks of the initial report, allowing for a more timely and proactive response.

The research also highlighted the value of network analysis in uncovering complex relationships within claims. By examining how often specific attorneys and providers appear together across multiple claims, the model was able to identify potentially collusive patterns that may suggest coordinated fraud. These insights can be difficult, if not impossible, to detect using conventional indicator-based systems.

CLARA Analytics is continuing to expand its capabilities by incorporating more detailed medical and legal data into its models. Through AI-driven analysis and reasoning, the company aims to provide insurers with a clearer, faster understanding of suspicious claim patterns and improve overall claims management.

With insurance fraud costing the industry an estimated $40 billion annually—excluding health insurance—these findings suggest that a more intelligent, analytics-based approach could significantly change how fraud is detected and addressed, combining the strengths of technology and human expertise to build more effective prevention systems.

“This research represents a significant advancement in how the insurance industry can approach fraud detection,” commented 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.”

“What’s particularly promising about this approach is that it doesn’t rely on pre established 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.”