Detecting Money Laundering: Exploratory Anomaly Detection of Money Laundering Typologies from FINTRAC Disclosures

This study presents the findings of a Crime Reduction Research Program (CRRP) project that examines the utility of lightweight, typology-informed analytical techniques for anti-money laundering (AML) detection in resource-limited law enforcement settings. The project, initially aimed at developing an integrated middleware system to analyze structured and unstructured financial intelligence, was re-scoped due to organizational restructuring following the Cullen Commission and pandemic-related capacity shifts. The revised study is a standalone exploratory analysis of FINTRAC Suspicious Transaction Reports and related disclosure data for Project ATHENA (2016–2019), conducted in partnership with the Combined Forces Special Enforcement Unit – British Columbia, the Royal Canadian Mounted Police, and the Ministry of Public Safety and Solicitor General.Drawing on Benford’s Law, feature-engineered indicators, and visual–analytic triage methods, the study evaluates whether accessible, explainable anomaly-detection techniques can improve the early-stage prioritization of pre-flagged financial intelligence. The analysis shows that deviations from expected digit distributions, venue-based clustering, round-dollar transaction patterns, and the innovative measure of casino versatility offer relevant behavioural signals aligned with established laundering typologies, especially those linked to the Vancouver Model. These techniques revealed non-obvious transactional patterns, supported effective data reduction, and allowed for more intuitive interpretation by frontline analysts without dependence on complex enterprise systems.The findings expose critical, persistent structural gaps in Canada’s AML intelligence cycle, particularly in analytical capacity, data accessibility, and inter-agency coordination. This study demonstrates the transformative potential of modular, scalable screening tools designed for practitioners. More broadly, it strengthens the evidence base for innovative socio-technical strategies in the fight against illicit finance, proving that lean, theory-driven computational methods can drive breakthroughs in financial integrity investigations, even amid institutional instability and evolving enforcement priorities.