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

Abstract

Health insurance claims processing represents a critical yet resource-intensive operational function within the healthcare ecosystem. Manual processes are prone to errors, delays, and compliance risks, highlighting the need for automation strategies that enhance efficiency, accuracy, and regulatory adherence. Robotics Process Automation (RPA) has emerged as a transformative solution, leveraging software bots to execute structured, rule-based tasks across heterogeneous systems. This paper investigates the impact of RPA on operational efficiency and compliance in health insurance claims processing, integrating theoretical frameworks, empirical evidence, and industry best practices. We explore the underlying mechanisms by which RPA reduces cycle times, minimizes human error, and strengthens auditability, while discussing integration with complementary artificial intelligence (AI) tools for predictive analytics, fraud detection, and intelligent document processing. Implications for process optimization, workforce transformation, and governance frameworks are examined, establishing a foundation for future research and scalable deployment in healthcare financial operations.