Real-Time Compliance Intelligence for Adaptive Financial Crime Detection
DOI:
https://doi.org/10.5281/166gpb45Keywords:
Event-Driven, Compliance Intelligence, Adaptive Detection, Governed Risk Analytics, Regulatory Science, Architectural Layers, Signal Extraction.Abstract
Current financial crime detection systems rely heavily on unsupervised anomaly and novelty detection methods grounded in data science. A central limitation of these models is their inability to remain sensitive to shifting data distributions over time: performance degradation can go undetected, and recalibration requires supervised retraining on ground-truth labels drawn from confirmed financial crime cases—events that are inherently rare and non-recurring. This creates a persistent gap between detection capability and the realities of model maintenance.
To address this gap, we propose an event-driven compliance intelligence framework that balances detection capacity with the expression of risk governance. We demonstrate its practical application through Anti-Money Laundering (AML) risk detection and management at a digital wallet operator.
This work points toward a broader direction for innovation: noise-filtering methods that not only strengthen adaptive financial crime detection but also generate insight-driven predictions capable of steering existing governance controls across the financial crime regime. In particular, we align known money-laundering typologies with the risk detection and management requirements set out by regulatory bodies such as the Financial Action Task Force (FATF), as embodied in its 40 Recommendations. By generalizing the relationship between signals, events, and monitoring through the lens of compliance intelligence, this research contributes to the advancement of regulatory science and regulatory-compliance theories of detection and governance.
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Data Availability Statement
No new empirical datasets were generated or analyzed in this study. Figures A and B use synthetic illustrative data created solely to visualize the conceptual mechanisms described in the paper.
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Copyright (c) 2024 Hiroshi Tanaka (Author)
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