Human–AI Collaboration in Claims Adjudication: A Framework for Enhanced Quality and Efficiency

The integration of artificial intelligence and machine learning technologies with human expertise represents a transformative approach to insurance claims adjudication. Claims processing constitutes one of the most critical yet operationally complex functions within the insurance industry, traditionally characterized by high manual intervention rates, extended processing timelines, and elevated error frequencies. This research synthesizes evidence from industry implementations, performance metrics, and peer-reviewed studies through 2022 to establish a comprehensive framework for human–AI collaboration in claims adjudication. Empirical findings demonstrate that hybrid human-AI systems achieve 96.1% detection accuracy in fraud identification, reduce claims processing time by 86.4% across all stages, and generate a 216.7% return on investment within the first operational year. The framework delineates four hierarchical levels of collaboration: automated data processing and feature extraction, AI-driven analysis and risk scoring, human-AI collaborative decision-making, and adjudication outcomes. Integration of natural language processing for document analysis, computer vision for damage assessment, and advanced machine learning algorithms within a structured governance framework yields measurable improvements in operational efficiency, claim quality, and customer satisfaction.