Adaptive Detection for Financial Crime Risk
Keywords:
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 techniques rooted in data science. A persistent challenge is maintaining model sensitivity and relevance as data distributions evolve over time. Because financial crime cases are rare and non-recurring, performance degradation often goes undetected, and models typically require supervised retraining using ground-truth labels that are difficult to obtain. To address this gap, this study proposes an event-driven compliance intelligence framework that balances detection capability with risk governance requirements, with practical implementation centered on Anti-Money Laundering (AML) risk detection and management for a digital wallet operator.
Building on recent advances in noise-filtering methods, the framework supports adaptive financial crime detection systems capable of generating insight-driven predictions that inform and strengthen existing governance controls within a financial crime risk regime. In particular, it aligns established money-laundering typologies with the risk detection and management objectives of AML regulation, as envisioned by regulatory bodies such as the Financial Action Task Force (FATF) through its 40 Recommendations. By generalizing the relationship between signals, events, and monitoring through a compliance intelligence lens, this work contributes to advancing regulatory science and reinforcing regulatory-compliance theories of detection and governance.
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Copyright (c) 2026 Vinod Battapothu (Author)
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