DATA DRIFT-AWARE MLOPS ARCHITECTURE FOR HIGHVOLUME CLAIMS PROCESSING SYSTEMS

Abstract

The rapid adoption of Machine Learning (ML) technologies in healthcare and insurance claims processing has enabled organizations to automate claim classification, fraud detection, reimbursement prediction, and financial risk analysis. However, high-volume claims processing environments experience continuous changes in data patterns due to evolving healthcare regulations, payer policies, patient behaviors, coding practices, and operational workflows. These changes can introduce data drift, resulting in reduced Machine Learning model accuracy, unreliable predictions, and inefficient claims management. This paper proposes a Data Drift-Aware MLOps Architecture for HighVolume Claims Processing Systems that integrates MLOps automation, continuous data monitoring, drift detection mechanisms, automated model retraining, cloud-native processing, and intelligent analytics. The proposed architecture continuously evaluates incoming claims data distributions, detects significant variations, measures model performance degradation, and initiates adaptive optimization workflows. Automated MLOps pipelines ensure continuous integration, deployment, validation, monitoring, and governance of claims prediction models. Experimental analysis demonstrates that the proposed framework improves model reliability, prediction accuracy, scalability, operational efficiency, and adaptability compared with conventional static Machine Learning deployment approaches. The proposed solution provides a scalable, intelligent, and resilient foundation for nextgeneration claims processing systems requiring continuous learning and reliable AI-driven decision support. Keywords: Data Drift Detection, MLOps, Claims Processing, Machine Learning, Healthcare Analytics, Model Monitoring, Automated Retraining, Cloud Computing, Predictive Analytics, Artificial Intelligence.