Case Study: Leading Personal Lines Insurance Company achieves 20% more fraud detection and $30M potential savings with Mu Sigma

Fraud Detection for a leading Personal Lines Insurance Company

Leading Personal Lines Insurance Company worked with Mu Sigma to improve fraud detection in its claims process. The insurer’s existing, rules-based approach required heavy manual review, produced many false alarms, and missed a significant number of suspicious claims.

Mu Sigma implemented a fraud analytics solution combining text mining, social network analysis, and predictive modeling to create a composite fraud propensity score and support investigators with a claims case tool. The new approach improved fraud identification, caught 90% of actual fraud cases in a sampled set of total claims payments, and in test markets indicated potential savings of over $30M by identifying 20% more fraudulent claims while flagging fewer cases for review.