Governance-By-Design For AI-Based Insurance Fraud Detection: Auditability, Accountability, And Regulatory Traceability

Abstract

The growth of artificial intelligence applications in insurance fraud detection has triggered more regulatory attention to the issue of algorithmic decision-making systems, which have shown a lot of shortcomings in the traditional post-hoc regulatory framework and manual audit services when responding to the multiplicity and speed of AI-based decisions. One concept that will be introduced as a guiding principle of architecture is that governance-by-design should be ingrained into AI fraud detection systems as a component of the design and not as an afterthought. The framework below deals with governance issues of critical challenges caused by model obscurity, distributed system structures, and accountability ambiguity in hybrid human-machine decision systems. Organizations can establish a core position of governance as the central consideration of the system design to make sure that all system components that create, transform, or operate on fraud evaluations possess detailed provenance documents and embrace authorized interrogation of logic and data association. Architectural designs such as governance microservices, event-based audit trails, and policy enforcement points are dedicated audit trail management infrastructure, providing consistency in audit trail management across all elements of the system. The governance-by-design framework allows insurance organizations to implement ethical promises in a tangible technical manner, in support of trust relationships that are critical to the operation of an insurance market, in response to the changing regulatory demands of transparency and human supervision of consequential automated decisions.