Reimagining Anti-Money Laundering through Machine Learning and Explainable AI: A Theoretical and Empirical Examination of Evolving Financial Crime Paradigms

Abstract

This article presents an extensive, publication-ready synthesis and theoretical elaboration on the application of artificial intelligence (AI), machine learning (ML), deep learning (DL), and automation to Anti-Money Laundering (AML) systems and financial crime prevention. Drawing strictly on the provided literature, the work constructs a rigorous intellectual map that interrogates existing methodologies, evaluates empirical findings, and proposes refined conceptual frameworks for deploying intelligent systems in AML operations. The abstracted synthesis identifies core problematics—data heterogeneity, label scarcity, adversarial behavior, explainability, regulatory alignment, operational scalability, and socio-technical risk—which recur across the literature reviewed (Labib et al., 2020; Tiwari et al., 2020; Al-Shabandar et al., 2019; Lokanan, 2019; Kute et al., 2021; Milon, 2024). The study articulates a theoretically grounded methodological approach emphasizing hybrid systems that combine supervised, unsupervised, and semi-supervised learning with rule-based engines and graph analysis. It analyzes empirical patterns reported in the sources—improvements in detection precision and recall, reductions in false positives post-automation, and gains in investigative efficiency—while critically examining the trade-offs in interpretability, model drift, and regulatory acceptability (Al-Shabandar et al., 2019; Kute et al., 2021; Basu & Tetteh, 2024). The discussion provides in-depth commentary on governance, model stewardship, human-in-the-loop controls, and the necessity for robust evaluation metrics beyond traditional statistical measures. The article concludes with a research agenda and policy recommendations aimed at harmonizing technical innovation with ethical, legal, and operational realities in AML practice. This synthesis intends to serve as a bridging document for academics, regulators, and practitioners seeking a theoretically rich and actionable perspective on AI-enabled AML.