Generative AI in Healthcare Claims Processing and Fraud Detection: Transforming Insurance Workflows

Abstract

This article explores the transformative role of Generative Artificial Intelligence (GenAI) in revolutionizing healthcare claims processing and fraud detection systems. The integration of large language models and advanced machine learning techniques represents a paradigm shift from traditional rule-based approaches to dynamic, intelligent systems capable of processing unstructured data, understanding contextual nuances, and detecting sophisticated fraud patterns. The article examines a comprehensive architectural framework comprising five interconnected layers that enable efficient claims processing while significantly improving accuracy and reducing manual intervention. The article further analyzes how GenAI enhances fraud detection through pattern recognition, synthetic scenario generation, network analysis, temporal pattern detection, and multi-modal approaches. Addressing regulatory compliance and ethical considerations, the article emphasizes the importance of privacy protection, explainability, bias mitigation, and robust validation processes. Implementation challenges, including data quality issues, model maintenance requirements, workforce transformation needs, and return on investment considerations, are examined, providing strategic insights for organizations navigating the transition to GenAI-powered claims management systems.