Early fraud detection breakthrough could save insurers billions, according to new research

The research, using unsupervised machine learning, analysed 2,867 claims and flagged 9% as high-risk for SIU referral. Michigan and Arizona showed the highest incidence of potential fraud indicators. The findings suggest that early detection using advanced analytics could save insurers billions and act as a strong fraud deterrent.

“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.”

“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.”