Case Study

Health Insurer Improves Fraud Detection and Identifies High-Risk Claims With AI

Industry

Healthcare and Life Sciences, Healthcare Payer

Location

USA

Our Contributions

AM/ML, Payer Business Process Optimization

A supplemental health insurer was looking for a way to proactively combat insurance claim fraud amid increasing transaction volumes. Their existing detection systems could no longer keep up with the volume or velocity of claims, so they turned to Coforge for an answer.

The Challenge

A leading US supplemental health insurer was facing a surge in fraudulent claims along with a dramatic rise in transaction volumes, which made it increasingly difficult to detect the signal against the noise.

Manual investigations couldn’t keep up, and rule-based systems were not intelligent enough to detect increasingly sophisticated fraud schemes.

The result was slower detection, missed red flags, and ultimately, financial losses. They needed a scalable, AI-driven solution that could proactively identify suspicious claims in real-time and reduce their reliance on retrospective analysis.

Our Approach

We implemented a scalable, AI-powered fraud detection solution built on a scalable data architecture. Using a combination of advanced analytics and AI techniques, it enables real-time risk scoring to classify claims and flag anomalies far faster than before. We also developed robust dashboards that provide automated daily and weekly fraud analytics reporting.

Data Architecture

Centralizes all information and enables the insurer to run real-time inference and generate dynamic risk scores for all claims as they are submitted.

Ensemble Modeling

Multiple AI models were trained to analyze incoming claims and compare their individual risk predictions to eliminate bias and errors, reduce variance, and arrive at a consensus on the riskiest claims.

Graph Convolutional Networks (GCN)

By representing entities like policyholders, claims, and providers in a graph, GCNs can uncover hidden relationships such as shared addresses or phone numbers to identify fraud more effectively than analyzing individual transactions.

Impact to Date

Faster identification of high-risk claims

Improvement in fraud detection rate