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.

401(k) Security, Behavioral Biometrics, Account Takeover Detection, Continuous Authentication, Financial Fraud Prevention, AI in Cybersecurity, Session Monitoring, Risk-Based Authentication, Identity Protection

● Roth, S., & Lee, J. (2021). Behavioral Biometrics in Financial Security. Journal of Digital Risk

● NIST. (2020). Guidelines for Online Identity Verification

● Baweja, K. (2022). Real-Time Fraud Detection Using AI. IEEE Conference on Cybersecurity

● Kumar, V., & Iqbal, M. (2019). LSTM for Continuous Authentication. ACM Transactions on Privacy and Security

● Microsoft Identity Protection (2023). Behavioral Signal Enrichment for Zero Trust Models