This article examines the integration of artificial intelligence with cloud computing to transform anti-money laundering compliance in financial institutions. Traditional rule-based AML systems have proven inadequate against sophisticated financial crimes, generating excessive false positives while missing complex schemes. Graph Neural Networks offer unprecedented capability to analyze transaction networks by modeling relationships between entities and detecting anomalous patterns. Behavioral analytics complements this approach by focusing on temporal patterns of individual customers, enabling dynamic risk profiling based on transactional behavior rather than static attributes. The cloud infrastructure supporting these analytics provides the necessary computational scalability, data integration capabilities, and real-time processing essential for modern AML operations. Implementation considerations include model explainability, regulatory compliance, and data protection requirements. The article explores emerging trends including federated learning for cross-institutional collaboration and advanced natural language processing for unstructured data analysis. This technological convergence represents not merely an incremental improvement but a fundamental transformation in AML capabilities, enabling financial institutions to implement sophisticated detection algorithms at scale while maintaining regulatory compliance and operational efficiency.
Keywords: Cloud Computing, anti-money laundering, behavioral analytics, financial crime detection, graph neural networks
