Real-Time Overpayment Detection at Point of Payment: Methods and Architecture to Identify Likely Overpayments before Claims are Paid

Abstract

Overpayments resulting from errors in healthcare claims processing are one of the major causes of financial loss for payer organizations, government programs, and private insurers. Traditional retrospective audits usually locate the overpayments several weeks or months after the funds have already been disbursed and this leads to the recovery processes that are costly, provider abrasion, and the inefficiencies of administration. The real-time or near-real-time identification of overpayment at the POP intends to eliminate the occurrence of such errors by the direct integration of advanced analytics, rule engines, machine learning models, and scenario-based risk scoring into the claim adjudication workflow. The present document discusses a complete system for detecting overpayments in real-time as well as the foundational technical infrastructure, data models, streaming pipelines, business rules, supervised and unsupervised anomaly detection methods, alerting manners, and the junctions with the payer claims systems. The case study illustrates the payer environment-based deployment of a real-time detection engine encompassing system performance, model accuracy, financial impact, and operational considerations. Findings show that real-time detection may bring down overpayments to nearly 40–60% of the levels that retrospective methods can achieve, thereby significantly enhancing payment integrity. The study ends with the suggestions for the next steps including real-time provider behavioral analytics, federated learning for cross-payer intelligence, and integration with national fraud detection networks.