Scaling Claims Overpayment Detection
to Reduce Financial Leakage
A healthcare enterprise struggling with post-payment
overpayment detection shifted to ML-driven claims
monitoring for faster financial control.
For a healthcare enterprise, identifying claims overpayments too late in the process was driving financial leakage. Post-payment reviews increased recovery costs and limited the ability to prevent errors before they occurred.
Delayed Overpayment Detection
Overpayments were identified after payment, reducing opportunities for early intervention
High Recovery Costs
Post-payment recovery efforts resulted in approximately 10% additional cost
Scalability Constraints
Existing processes struggled to handle high volumes at the speed required
Instead of relying on recovery after the fact, the organization shifted detection earlier in the claims lifecycle. A machine learning–based approach enabled overpayment identification during adjudication, improving both speed and control.
ML-based Overpayment Detection
Identified potential overpayments during adjudication instead of post-payment review
High-volume Processing Capability
Enabled continuous, large-scale processing of claims for faster identification
Real-time Detection Framework
Shifted detection earlier in the claims lifecycle to reduce downstream recovery effort
50,000 claims processed every 15 minutes
Enabled high-speed, large-scale overpayment detection
USD 11 million in overpayments identified
Strengthened financial control and visibility
USD 5 million in net savings within one year
Reduced overall financial leakage
