AI-Driven Anomaly Detection Models for Preventing Claims Denials and Revenue Leakage in Healthcare

Abstract

Despite the increased use of new automation technologies, healthcare organizations continue to see declining revenues due to increased billing discrepancies and claims denials. The traditional use of rule-based validation frameworks does not allow for the effective identification of new patterns of denials or the resolution of complicated coding inconsistencies.

Artificial Intelligence (AI) and Machine Learning (ML) technologies provide the ability to significantly improve the detection of anomalies related to either claims and/or revenue before they impact adjudication results. Feature engineering creates contextualized inputs that improve model precision and support compliance requirements. Through this integration, organizations are able to strengthen the accuracy of their claims and minimize the financial impact of revenue loss associated with claim denials. Unsupervised learning techniques discover unknown patterns without requiring labeled training data. Supervised models predict denial probability based on historical adjudication outcomes. Natural language processing analyzes unstructured documentation to identify inconsistencies and gaps. By incorporating and integrating anomaly detection software into the Quality Engineering Pipeline, organizations should be able to detect anomalies in real-time and continuously improve their overall operational accuracy. By adhering to applicable HIPAA regulations and developing ethical governance frameworks for their AI models, organizations have an opportunity to achieve significant cost savings related to preventable denials and manual interventions. First-pass payment accuracy improves substantially while reimbursement cycles accelerate. Future advancements include generative AI for synthetic testing, self-correcting mapping engines, and collaborative human-AI validation systems. AI-powered Quality Engineering represents the future of healthcare claims automation and operational excellence.