How Coforge Enabled Real-Time Claim Risk Detection for a US Health Insurer
Overview
A leading US-based supplemental health insurer partnered with Coforge to proactively combat rising claim fraud amid increasing transaction volumes. The client’s existing manual and rule-based detection systems were no longer sufficient—delaying identification of suspicious activity and contributing to financial losses. Coforge implemented a scalable, AI-powered fraud detection solution that enabled real-time risk scoring and early anomaly detection. By leveraging advanced techniques such as ensemble modeling and Graph Neural Networks, the insurer gained the ability to flag high-risk claims instantly—shifting from retrospective fraud analysis to proactive, real-time intervention.
A leading US-based supplemental health insurer faced a surge in fraudulent claims amid rising transaction volumes. Manual and rule-based systems lagged in accuracy and speed—resulting in delayed detection, missed red flags, and financial losses. The client sought a scalable, AI-driven solution to proactively identify suspicious claims in real-time and reduce reliance on retrospective analysis.
Solution
Coforge implemented a real-time fraud detection engine leveraging ensemble models and Graph Convolutional Networks (GraphCN). Key features included distance-based similarity detection, class separation via feature engineering, and dynamic risk scoring using claim relationships. A scalable data architecture was established to centralize information and run real-time inference across all claim transactions. Dashboards were developed for automated daily and weekly fraud analytics reporting.
Key Highlights
Used Graph Neural Networks to model inter-claim relationships for anomaly detection
Built an ensemble model that differentiated inseparable fraud classes with higher accuracy
Delivered a real-time dashboard for fraud alerting and case tagging
Enabled early fraud discovery by analyzing contextual data from devices, agents, and providers
