Artificial Intelligence and Cloud-Enabled Anti-Money Laundering: A Comprehensive Framework for Detection, Compliance, and Policy Optimization
Keywords:
Anti-Money Laundering, Artificial Intelligence, Cloud Computing, Anomaly Detection, Compliance OptimizationAbstract
Background: The accelerating sophistication of money laundering schemes, combined with the proliferation of fintech, digital remittances, and high-velocity transaction systems, has rendered legacy anti-money laundering (AML) controls insufficient. Modern AML demands an integrated approach that leverages advances in artificial intelligence (AI), cloud computing, machine learning (ML), and robust governance structures to detect, investigate, and prevent illicit financial flows in near real time (Agorbia-Atta & Atalor, 2024; Faccia et al., 2020).
Objective: This article develops a comprehensive, publication-quality framework for AI and cloud-enabled AML systems that harmonizes technical detection methods, explainability and legal constraints, operational processes, and policy optimization to strengthen financial institutions’ compliance posture and investigative effectiveness.
Methods: Drawing strictly from the provided literature, the study synthesizes evidence from systematic reviews, empirical studies, technical reports, and legal analyses to construct a layered methodological approach. The framework integrates supervised and unsupervised ML techniques, deep learning anomaly detection, ensemble modeling, transaction monitoring architectures, cloud deployment strategies, automated robotic process automation (RPA) for compliance workflows, and policy optimization via reinforcement learning and rule-tuning methodologies (Alsuwailem & Saudagar, 2020; Dalal & Rele, 2018; Paula et al., 2016; Agorbia-Atta & Atalor, 2024; Singh, 2025).
Results: The paper presents (1) a taxonomy of laundering vectors in the digital era and their signal characteristics; (2) a modular system architecture combining data ingestion, feature engineering, model training, scoring, explainability modules, and case management; (3) algorithmic strategies to balance detection sensitivity and false positive rates; (4) cloud deployment and operationalization recommendations to support scalability and cross-border data handling; and (5) a policy optimization blueprint for aligning ML outputs with regulatory expectations. Each element is elaborated in detail, with practical recommendations for tuning, governance, auditability, and human–machine collaboration.
Conclusions: AI and cloud technologies offer transformative potential for AML, but realization depends on careful system design, legal and ethical safeguards, model transparency, continual policy feedback loops, and investment in investigatory capacity. The proposed framework reconciles technical capabilities with regulatory obligations and provides a roadmap for institutions seeking to modernize AML operations while minimizing harms from erroneous interventions.
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