Suspicious Gambling Activity as an Emerging Money Laundering Typology: Behavioral Indicators in Multi-Jurisdictional Retail Banking Accounts

Abstract

This review examines suspicious gambling-related activity as an increasingly complex financial-crime risk within multi-jurisdictional retail banking, focusing on the behavioral, transactional, regulatory, and technological factors that shape detection and control. The study adopts a structured narrative review approach, synthesizing peer-reviewed scholarship, regulatory perspectives, banking-risk research, and emerging analytical frameworks concerning gambling transactions, anti-money-laundering controls, customer profiling, cross-border exposure, and digital monitoring. The findings show that gambling activity becomes materially significant for financial-crime assessment not through isolated transactions, but through combinations of behavioral and transactional anomalies, including abrupt deviations from established account patterns, rapid fund movement, unusual account turnover, repeated cross-border transfers, opaque counterparties, unexplained sources of funds, and recurring links across multiple jurisdictions. The review further identifies regulatory fragmentation, uneven supervisory capacity, information-sharing constraints, weak data quality, and excessive reliance on static monitoring rules as important barriers to effective detection. Advanced methods, including machine learning, graph analytics, adaptive behavioral baselines, and real-time monitoring, offer substantial potential, but their effectiveness depends on explainability, governance, secure data architecture, analyst competence, and proportionate human oversight. Particular attention is given to avoiding the conflation of lawful gambling intensity with criminality, thereby preserving proportionality, fairness, and evidential rigor in compliance. The study concludes that defensible detection requires an integrated, evidence-led framework combining customer context, transaction history, network relationships, jurisdictional risk, and investigative judgment. It recommends stronger behavioral profiling, improved cross-border intelligence sharing, continuous model validation, interoperable reporting standards, and enhanced analyst training. Future research should prioritize longitudinal validation of red-flag combinations, AI-supported investigation quality, virtual-asset exposure, and the evolving interaction between digital gambling ecosystems and retail banking surveillance.