Payment Integrity

Scan claims for billing errors, duplicate charges, upcoding, unbundling, and waste/abuse patterns

Healthcare payors lose an estimated 3-10% of total claims expenditure to billing errors, fraud, and abuse. Manual auditors can only review a fraction of claims, relying on rules-based systems that miss sophisticated patterns like gradual upcoding, strategic unbundling across multiple visits, or coordinated billing schemes. The sheer volume—hundreds of thousands of claims daily—makes comprehensive human review impossible, allowing billions in improper payments to slip through. This agentic workflow deploys machine learning models alongside rules-based engines to scan every claim in real time. A Pattern Detection Agent identifies statistical anomalies—unusual billing frequency, procedure code clustering, and outlier charge amounts. A Code Validation Agent checks for specific violations including upcoding (billing higher-complexity codes than supported by documentation), unbundling (separating bundled procedures for higher reimbursement), and duplicate billing. A Risk Scorer aggregates findings and prioritizes claims for human investigation, reducing false positives through feedback loops with investigator outcomes. Organizations implementing automated payment integrity report identification of 2-5% additional recoverable dollars on total claims spend, with false positive rates dropping 40-60% compared to rules-only systems. The continuous learning capability means detection accuracy improves over time as investigator feedback refines models. This is essential for any payor, TPA, or self-insured employer seeking to protect against improper payments while maintaining HIPAA and CMS compliance.