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International Journal Of Management And Economics Fundamental

Peer Reviewed | Open Access | E-ISSN: 2771-2257
Published Article

AI-Driven Continuous Behavioral Biometrics for Secure Financial Account Authentication: Theoretical Foundations, Methodological Architectures, and Critical Implications

AI-Driven Continuous Behavioral Biometrics for Secure Financial Account Authentication: Theoretical Foundations, Methodological Architectures, and Critical Implications

Authors

  • Theodore M. Fairbanks
    Department of Computer and Information Science University of Toronto, Canada

Keywords:

Behavioral biometrics continuous authentication financial cybersecurity deep learning

Abstract

The accelerating digitization of financial services has fundamentally transformed how individuals interact with retirement savings platforms, particularly defined contribution systems such as 401(k) accounts. While digital access enhances usability and engagement, it also exposes sensitive financial assets to increasingly sophisticated cyber threats. Traditional authentication mechanisms, including passwords, tokens, and static biometric identifiers, have proven insufficient against evolving attack vectors that exploit credential compromise, social engineering, and behavioral mimicry. In response, behavioral biometrics and continuous authentication paradigms have emerged as promising alternatives, leveraging implicit patterns of human interaction to establish and maintain user identity over time. This article presents an extensive, theory-driven research investigation into AI-driven behavioral biometric systems for secure financial account authentication, with a specific emphasis on retirement account security contexts. Grounded strictly in the provided scholarly literature, the study synthesizes advances in machine learning, deep learning, human activity recognition, and mobile sensor analytics to construct a comprehensive conceptual and methodological framework. Particular attention is given to the role of continuous authentication in mitigating insider threats, session hijacking, and post-login attacks, as articulated in recent financial security research (Valiveti, 2025). The article elaborates the historical evolution of biometric authentication, contrasts physiological and behavioral modalities, and critically examines the epistemological assumptions underlying AI-based identity inference. Through an expansive methodological discussion, the study outlines data acquisition strategies, feature extraction pipelines, learning architectures, and evaluation paradigms relevant to financial applications, while also interrogating limitations related to privacy, bias, spoofing resilience, and regulatory compliance. The results section provides a literature-grounded interpretive analysis of empirical findings reported across mobile, voice, and multimodal biometric systems, emphasizing their relevance to high-stakes financial environments. The discussion section offers an in-depth theoretical synthesis, comparing competing scholarly viewpoints, addressing unresolved debates, and articulating future research trajectories. By integrating behavioral biometrics with continuous authentication theory and financial security imperatives, this article contributes a rigorous, publication-ready academic foundation for next-generation account protection systems.

 

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