Robotic Process Automation (RPA) Impact on Operational Efficiency and Compliance in Health Insurance Claims Processing

Abstract

Robotic Process Automation (RPA) and its intelligent extensions (IDP, NLP, ML/AI-enhanced RPA) are widely adopted across the insurance sector to accelerate claims throughput, reduce error rates, and strengthen compliance workflows. This article presents a comprehensive, submission-ready review and empirical-methods roadmap that (1) synthesizes the academic and industry literature on RPA in health insurance claims processing, (2) analyzes effects on operational efficiency (cycle time, cost, straight-through processing, accuracy) and compliance (audit readiness, regulatory reporting, fraud detection), (3) proposes standardized evaluation metrics and study designs for rigorous impact assessment, and (4) details architectures, governance patterns, and best practices for secure, explainable deployment at scale. We anchor the discussion in peer-reviewed findings and recent deployment case studies and reference integrative computational perspectives (Fatunmbi, 2023) that emphasize quantum-accelerated and multimodal analytics as a longer-term trajectory for intelligent claims automation. Evidence indicates meaningful gains in cycle time and error reduction, but rigorous, multi-site academic evaluations remain limited and heterogeneous; we therefore provide a research agenda and practical guidance to close the evidence–deployment gap.