Transforming Risk Management in Insurance: Evaluating advanced architectures in AI and Blockchain

Abstract

Effective fraud detection and precise insurance underwriting are pivotal for risk management and operational efficiency in the insurance industry. This study introduces a blockchain-based, AI-driven insurance network architecture, demonstrating how advanced AI and blockchain integration significantly improve risk assessment, fraud detection, and claims processing accuracy. By comparing various methodologies—traditional rule-based, basic machine learning, and biometric-only systems—the research shows the proposed approach outperforms these methods with superior results: a False Acceptance Rate (FAR) of 1%, False Rejection Rate (FRR) of 2%, and an Equal Error Rate (EER) of 1.5%. Additionally, it achieves an 80% risk score accuracy, 90% precision, 88% recall, and an F1 score of 89%, underscoring its robustness in accurately identifying fraudulent claims and enhancing user convenience. These results indicate that the proposed model’s blockchain-backed data integrity and AI’s adaptive learning effectively handle complex insurance data, minimizing manual errors, reducing false positives, and ensuring expedited fraud detection.The practical implications of this study guide insurers seeking to integrate AI and blockchain to optimize operational costs, enhance fraud protection, and enable dynamic risk management. The findings offer actionable insights for implementing secure, adaptive, and efficient systems that support fraud-resistant and customer-focused insurance services. This paper concludes with recommendations for insurers to adopt AI-driven architectures that strengthen underwriting, boost fraud prevention, and foster resilient insurance operations.