AI Framework for Fair and Auditable Prior Authorization

Prior authorization is a critical function in health insurance; however, traditional processes regularly cause operational delays, member dissatisfaction, inconsistent denials, provider frustration, delayed access to care, and increased regulatory scrutiny. Current authorization workflows are often overloaded by manual reviews, unclear policies, fragmented clinical documentation, limited decision transparency, and insufficient visibility into denial reasons. As health insurers adopt AI-enabled automation, these challenges become more complex if decisions are not explainable, evidence-based, auditable, and supported by human oversight. This study introduces a secure AI-driven decision-support framework for prior authorization that integrates explainable decision logic, policy-rule traceability, clinical-evidence mapping, bias monitoring, human review checkpoints, and appeal-ready explanations within a single governance model. Unlike automation focused mainly on speed and cost, the proposed framework prioritizes fair, transparent, and accountable decision-making across the authorization lifecycle. Each AI recommendation explains why it was made and links the decision to medical guidelines, policy rules, supporting evidence, prior reviewer actions, and member-specific details. The framework is expected to reduce authorization turnaround time, decrease avoidable denials, reduce appeal overturns, improve provider and member satisfaction, and make decisions more consistent and fairer across clinical and demographic groups.