Case Study: Global Insurance Company achieves improved fraud detection and reduced losses with Fraud.net

Global Insurance Company - Customer Case Study

Global Insurance Company faced the costly industry-wide problem of insurance fraud—industry estimates put losses at over $80 billion annually and suggest 10–30% of property & casualty claims are fraudulent, pressure that drives up premiums for customers. To tackle this, the insurer engaged Fraud.net and its machine-learning fraud prevention platform, leveraging adaptive scoring and models tailored to insurance (property & casualty, life and health) to detect sophisticated and emerging fraud patterns.

Fraud.net deployed expertly engineered ML models with built-in insurance nuances and an adaptive scoring engine that delivers a single risk score and continuously learns from new outcomes. The solution improved fraud and loss prevention rates and operational efficiency for Global Insurance Company by surfacing high-risk claims beyond traditional methods, reducing fraudulent payouts and the operational burden of investigations while incrementally improving model accuracy over time.