AI-Driven Behavioral Biometrics for 401(k) Account Security

Abstract

As cyber threats evolve, attackers increasingly target financial retirement accounts like 401(k)s, exploiting their high-value nature and weak user-level security controls. Traditional defenses—passwords, OTPs, and device fingerprinting—have proven insufficient in detecting sophisticated account takeovers. This paper presents a behavioral biometrics framework that uses artificial intelligence to continuously authenticate users based on typing patterns, mouse movements, login behavior, and navigation habits. Instead of static credentials, the system builds a behavioral profile for each user and detects anomalies in real-time. Our framework aims to catch suspicious access attempts without interrupting legitimate users. By integrating seamlessly into existing financial platforms, this solution offers a balance of strong security and low user friction. We evaluate the framework in a simulated environment using behavioral data from anonymized user sessions, achieving high accuracy in detecting imposters while minimizing false alarms.