The accelerating digitalization of financial services has fundamentally reshaped the nature, scale, and complexity of transactional fraud, thereby creating a critical need for advanced analytical mechanisms capable of operating in highly dynamic, data-rich environments. Traditional rule-based systems and static statistical methods, while historically foundational to financial risk management, have demonstrated increasing inadequacy in addressing the evolving sophistication of fraudulent behaviors, particularly in online and real-time transaction ecosystems. In this context, machine learning has emerged not merely as a technological enhancement but as a paradigm shift in the conceptualization of financial security. This study develops a comprehensive, theoretically grounded, and empirically informed examination of machine learning–driven fraud detection architectures, with particular emphasis on supervised and deep learning frameworks, while situating these models within the broader literature on data mining, artificial intelligence, and computational learning theory.
Building on the architectural principles articulated in contemporary transaction systems research, including the integrated fraud detection framework proposed by Modadugu et al. (2025), this article conceptualizes fraud detection as a multilayered socio-technical system in which algorithmic intelligence, institutional risk governance, and data infrastructures interact to produce security outcomes. Rather than treating algorithms as isolated technical artifacts, this research positions them as embedded within organizational and regulatory contexts that shape both their design and performance. The theoretical foundation of this study draws upon classical supervised learning theory, probabilistic modeling, and deep neural architectures, integrating insights from foundational works in machine learning, data mining, and artificial intelligence to create a coherent explanatory framework.
The discussion advances a critical evaluation of current scholarly debates concerning transparency, bias, scalability, and real-time deployment, arguing that the future of fraud detection lies in architecturally integrated, continuously learning systems rather than in isolated algorithmic solutions. By embedding machine learning within a systems-level perspective of financial security, this study contributes a theoretically expansive and practically relevant understanding of how intelligent computational models can enhance trust, stability, and resilience in global financial infrastructures.

Machine learning, , financial fraud detection, supervised learning, deep neural networks,

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