Enhancing Retirement Account Security Through AI-Driven Behavioral Biometrics: A Socio-Technical and Ethical Analysis

Abstract

The accelerating digitalization of retirement finance has fundamentally reshaped how long-term savings systems are accessed, managed, and protected. Among these systems, employer-sponsored defined contribution retirement accounts have become increasingly exposed to sophisticated cyber threats, fraud vectors, and identity-based attacks due to their high asset concentration and frequent digital interaction. Traditional security mechanisms such as static passwords, rule-based fraud detection, and tokenized authentication, while historically effective, have proven insufficient against adaptive adversaries operating within complex socio-technical environments. In response, artificial intelligence–driven behavioral biometrics has emerged as a transformative paradigm capable of continuously authenticating users based on dynamic behavioral patterns rather than static credentials. This article develops a comprehensive, publication-ready theoretical and empirical synthesis of AI-driven behavioral biometric systems as applied to retirement account security, with particular emphasis on defined contribution plans. Grounded strictly in existing scholarly literature, the study integrates perspectives from financial technology, cybersecurity, machine learning, privacy engineering, and algorithmic fairness to construct a unified analytical framework.

The article advances three interrelated contributions. First, it situates behavioral biometrics within the historical evolution of financial security architectures, tracing the shift from credential-centric models to adaptive, context-aware risk systems informed by big data analytics and artificial intelligence (Nguyen et al., 2022). Second, it critically examines how behavioral biometric models—such as keystroke dynamics, mouse movement analysis, interaction cadence, and device usage patterns—can be operationalized to enhance account takeover prevention, with specific reference to retirement account contexts where transaction behaviors differ markedly from retail payments or e-commerce environments (Valiveti, 2025). Third, it interrogates the ethical, regulatory, and fairness implications of deploying AI-driven behavioral monitoring in high-stakes financial systems, engaging with debates on algorithmic bias, transparency, and user consent (Bellamy et al., 2018).