AI-Augmented Authentication: Machine Learning Framework for Adaptive Fraud Detection in Enterprise Identity Systems

Abstract

Static rule-based authentication cannot keep pace with adaptive, AI-driven cyber-fraud tactics that exploit behavioral and contextual vulnerabilities. This article proposes an AI-Augmented Authentication (AIAA) framework that applies supervised and unsupervised machine-learning models to enhance risk-based authentication decisions. Drawing on production-scale IAM datasets, the approach employs behavioral biometrics, device fingerprinting, and geo-velocity features to classify login attempts and predict session-level anomalies in real time. AIAA integrates seamlessly with identity orchestration platforms such as ForgeRock AM, providing explainable risk scores that trigger dynamic multi-factor challenges. Experimental evaluation demonstrates up to 60% reduction in phishing-related account takeovers and 30% faster fraud detection compared to rule engines. The article positions AI-augmented authentication as a cornerstone of future Zero Trust strategies for financial and healthcare enterprises.