Strengthening the Role of Financial Intelligence Units (FIUs) in Detecting and Preventing Complex Money Laundering Schemes

Abstract

This study evaluates the technological readiness and policy robustness of Financial Intelligence Units (FIUs) in combating money laundering, focusing on the adoption of advanced analytics tools and the adequacy of Anti-Money Laundering (AML) legal frameworks. Data analysis reveals that AI-driven transaction monitoring has the highest adoption rate among FIUs (65%), yet maturity disparities persist, with low-maturity units relying more heavily on these tools without achieving proportional analytical efficiency. Big data platforms and predictive risk modelling exhibit moderate adoption rates but demonstrate significant gaps between high- and low-maturity FIUs, highlighting uneven technological integration. Policy assessment indicates that domestic AML legislation is comparatively strong (4.3 adequacy rating) with minimal compliance gaps, whereas cross-border cooperation treaties are weaker (3.5 adequacy rating) and suffer from the largest compliance shortfall (25%). Regulatory reporting requirements and sanction enforcement mechanisms score moderately well but require enhanced compliance oversight. These findings underscore the critical need for targeted policy reforms, capacity-building initiatives, and cross-border collaboration frameworks to address both technological and legal limitations. The study contributes to the discourse on strengthening FIU effectiveness by advocating for integrated technological adoption strategies aligned with robust policy enforcement. Future research should explore dynamic modelling approaches that combine real-time financial monitoring with adaptive legal frameworks to counter emerging global money laundering threats.

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