The rapid digitization of financial services, workplace computing, and mobile ecosystems has intensified longstanding challenges surrounding secure user authentication, privacy preservation, and fraud prevention. Traditional authentication mechanisms such as passwords, personal identification numbers, and static biometric identifiers have proven increasingly inadequate in the face of sophisticated attack vectors, insider threats, and usability constraints. Against this backdrop, behavioral biometrics has emerged as a dynamic and adaptive paradigm capable of continuously authenticating users based on patterns of interaction, movement, and behavioral expression. This research article develops a comprehensive and publication-ready theoretical and empirical synthesis of AI-driven continuous behavioral biometric systems, with a particular emphasis on financial account security, enterprise computing environments, and sensor-rich mobile platforms. Drawing strictly on the provided reference corpus, the study integrates foundational keystroke dynamics research, contemporary deep learning architectures, reinforcement learning approaches, and regulatory perspectives to construct a unified analytical framework.

Central to this investigation is the growing application of artificial intelligence techniques for behavioral feature extraction, temporal modeling, and anomaly detection, particularly in high-risk financial contexts such as retirement account management. Recent work on AI-driven behavioral biometrics for 401(k) account security highlights both the promise and complexity of deploying continuous authentication in regulated financial systems, where accuracy, explainability, and compliance must coexist with user convenience (Valiveti, 2025). Building upon this and related studies, the article examines behavioral modalities including keystroke dynamics, gait, touchscreen gestures, finger stroke characteristics, and motion sensor data, situating each within its historical lineage and current methodological debates (Monrose and Rubin, 2000; Maghsoudi and Tappert, 2016; Lee et al., 2023).

The methodology section articulates a rigorous text-based research design that synthesizes comparative model evaluation, sensor-based data interpretation, and AI system lifecycle considerations without reliance on mathematical formalism or visual representations. The results section provides an interpretive analysis grounded in the literature, emphasizing patterns of convergence and divergence across studies evaluating convolutional neural networks, transformer-based architectures, and hybrid learning models for continuous authentication (Hu et al., 2023; Uslu et al., 2023). The discussion extends these findings through critical engagement with privacy-preserving techniques, regulatory risk-based frameworks, and market adoption trends, highlighting unresolved tensions between surveillance concerns and security imperatives (Hernandez-Alvarez et al., 2020; Centre for Information Policy Leadership, 2024).

By offering an expansive, citation-dense, and theoretically grounded contribution, this article addresses a persistent literature gap: the lack of an integrative, cross-domain academic treatment of AI-driven behavioral biometrics that simultaneously engages technical, financial, and governance dimensions. The work concludes by outlining future research trajectories focused on explainable AI, cross-device behavioral identity continuity, and ethically aligned deployment in regulated industries.