Machine Learning Detection of Revenue Leakage From Clinical Coding, Claims, and Reimbursement Discrepancies

Abstract

Healthcare revenue cycles increasingly depend on accurate clinical documentation, coding, claims processing, and reimbursement, yet their complexity creates substantial opportunities for revenue leakage. Coding inaccuracies, incomplete documentation, claim denials, underpayments, contractual inconsistencies, and reimbursement discrepancies can reduce healthcare organizations’ financial performance while increasing administrative burden. Conventional audit approaches are often retrospective, rule-based, and resource-intensive, limiting their ability to identify complex or evolving patterns across large volumes of clinical and financial data. This study examines a machine learning framework for detecting revenue leakage by integrating clinical coding, claims, payment, denial, and reimbursement information. The framework applies supervised and anomaly-detection techniques to identify discrepancies between expected and received reimbursement, coding irregularities, recurrent denial patterns, and financially significant claim anomalies. Predictive features incorporate diagnosis and procedure codes, billed charges, payer characteristics, reimbursement amounts, denial reasons, and historical payment behavior. Model performance can be evaluated using precision, recall, F1-score, ROC-AUC, and financial recovery indicators. The proposed approach supports risk-based claim prioritization, earlier identification of revenue loss, targeted auditing, and data-driven revenue-cycle management across healthcare reimbursement systems.