PREDICTIVE CLAIMS INTELLIGENCE USING MLOPS PIPELINES AND ENTERPRISE HEALTHCARE DATA

Abstract

Healthcare payer organizations process millions of insurance claims every day, creating significant challenges in fraud detection, payment accuracy, cost optimization, and regulatory compliance. Conventional rule-based claim processing systems often fail to adapt to evolving healthcare policies, complex coding standards, and changing fraud patterns. Recent advances in Machine Learning Operations (MLOps) enable continuous deployment, monitoring, governance, and lifecycle management of predictive models operating on enterprise healthcare data. This paper presents a Predictive Claims Intelligence framework that integrates scalable MLOps pipelines with enterprise healthcare datasets to automate claim prediction, anomaly detection, denial prevention, and reimbursement optimization. The proposed methodology incorporates secure data ingestion, feature engineering, automated model training, continuous validation, deployment, monitoring, and feedback-driven model retraining. The framework supports explainable AI, governance, and regulatory compliance while improving operational efficiency. The proposed architecture enables healthcare organizations to reduce manual intervention, accelerate claims adjudication, enhance predictive accuracy, and support data-driven decision-making. The study demonstrates that enterprise MLOps significantly improves healthcare claims intelligence and establishes a scalable foundation for intelligent healthcare revenue cycle management. Keywords— Healthcare Claims, MLOps, Enterprise Healthcare Data, Predictive Analytics, Claims Intelligence, Machine Learning, Revenue Cycle Management, Explainable AI, Fraud Detection, Healthcare Analytics.