Fraud Prevention in Insurance: Biometric Identity Verification and AI-Based Risk Assessment

The pervasive issue of fraud within the insurance industry incurs significant financial losses and erodes trust between insurers and policyholders. Conventional fraud detection methods, heavily reliant on manual verification and simple heuristic rules, are increasingly inadequate against sophisticated fraudulent schemes. This paper proposes an innovative fraud prevention framework that integrates biometric identity verification with artificial intelligence (AI)-based risk assessment to combat insurance fraud effectively. By harnessing advanced biometric technologies, including fingerprint, facial, and iris recognition, alongside machine learning algorithms for dynamic risk evaluation, this framework aims to substantially enhance the accuracy and reliability of fraud detection mechanisms. We explore the synergistic potential of biometrics and AI to provide a robust, real-time verification and risk assessment solution, tailored to the unique needs of the insurance sector. Through a comprehensive analysis of the proposed framework’s implementation, including algorithmic approaches and mathematical models, we demonstrate its efficacy in identifying and preventing fraudulent activities. Preliminary results underscore the framework’s capability to not only detect but also deter fraud, promising a transformative impact on insurance operations. This study contributes to the burgeoning field of digital fraud prevention, offering a scalable, efficient, and secure approach to safeguarding against insurance fraud.