Predictive Revenue Cycle Analytics Using AI-Driven Claims Optimization: Transforming Healthcare Financial Performance

Abstract

Healthcare organizations face persistent inefficiencies in revenue cycle management (RCM) due to fragmented processes and complex payer systems. This paper presents a systematic literature review of Artificial Intelligence (AI)-driven predictive analytics for optimizing claims processing and improving healthcare financial performance. Drawing from 2017–2025 literature, the study examines the application of machine learning and graph neural networks (GNNs) for claims denial prediction, fraud detection, and multi-source data integration. Results indicate that AI-enabled predictive models enhance revenue capture, reduce operational costs, and increase cash flow efficiency, transforming RCM from reactive to proactive management. However, challenges persist regarding algorithmic bias, data privacy, and workforce readiness. The paper concludes with a framework for ethical AI implementation and recommendations for healthcare stakeholders to ensure equitable and sustainable financial transformation.