A Leading US Life and Annuity Insurance Provider Increased Fraud Detection Rate by 500 Basis Points Using AI-Driven Fraud Analytics Platform

Challenges

The insurer faced rising fraudulent claims, relying heavily on manual, rule-based investigations driven by expert judgment. This approach lacked:

• Scalability, limiting the ability to process large claim volumes.
• Speed, delaying fraud detection and increasing loss exposure.
• Accuracy, resulting in missed fraud patterns.
  The business needed a scientific, AI-driven approach to detect and mitigate fraud proactively.

Solution

Coforge implemented a machine learning–based fraud analytics platform to modernize detection and strengthen predictive capabilities:

• Deployed advanced ML models to replace static rule-based systems with automated, adaptive fraud detection.
• Leveraged feature engineering techniques including segmentation to detect fraud patterns and supervised learning for model enhancement.
• Incorporated cluster-specific analysis to measure detection strengths, identify weaknesses, and refine algorithms for higher precision.
• Enabled continuous learning, ensuring the model evolved with emerging fraud tactics.

The Impact

• +500 basis points increase in fraud detection rate.
• Shift from manual, rule-based investigations to fully automated ML-based detection.
• Faster, more accurate identification of fraudulent claims, reducing operational losses, and improving investigator productivity.