As mobile device insurance fraud grows in scale and sophistication, traditional rule-based systems and manual investigation models are proving insufficient. This whitepaper outlines how Zensar helped a leading US-based mobile insurance provider modernize fraud detection using a scalable, real-time machine learning framework.
The solution combines data from five diverse internal and external sources to generate a unified, explainable fraud risk score for each claim. By leveraging advanced feature engineering and an optimized XGBoost model, the approach identifies complex fraud patterns across identity signals, behavioral anomalies, claim history, and temporal risk indicators, significantly improving detection accuracy while minimizing false positives.
A standout capability detailed in this paper is the use of Beta Calibration to ensure stable probability scores across model retraining cycles. This innovation enables consistent operational thresholds, predictable investigator workloads, and improved governance without frequent business recalibration. Built on a secure, cloud-native Azure architecture, the platform delivers sub-second fraud scoring, model explainability through SHAP, and audit-ready transparency.
The result is a measurable reduction in manual reviews, increased fraud capture, enhanced investigator efficiency, and a future-ready foundation for real-time, data-driven fraud decisioning.