Systematic Detection of Layering Instances for Real-Time Anomaly Detection of Financial Crimes

Financial crimes, including money laundering, fraud, and terrorism financing, remain persistent threats to financial systems due to the increasing sophistication of perpetrators and their extensive use of layering (tumbling) techniques to obscure transaction trails. Conventional machine learning–based anomaly detection systems often exhibit high false negative rates, particularly in streaming financial environments where transaction behaviors evolve dynamically. This study proposes a Systematic Detection Learning framework for real-time identification of layering activities in financial transaction data. The framework employs a user-centric, step-wise analytical process that systematically structures transaction attributes to extract recurring behavioral patterns associated with layering. Using SFinDSet for Systematic Detection of Financial Crimes, a publicly available financial crime dataset hosted on Kaggle, the proposed model is evaluated against established anomaly detection, classification and clustering techniques, including Isolation Forest, One-Class Support Vector Machine (O-C SVM), and Online k-Means. Performance evaluation focuses on the detection of layering instances, identification of unique layerers, and consistency across models. Experimental results show that the Systematic Detection approach identifies 7,694 confirmed layering instances and 441 unique layerers, thus outperforming Isolation Forest (with 99.54% consistency), Online k-Means (with 78.91%), and O-C SVM (27.43%). The results demonstrate that the proposed framework significantly reduces false negatives while maintaining high detection accuracy. By leveraging structured domain knowledge alongside adaptive learning, the Systematic Detection model provides a robust and interpretable benchmark for layering detection in streaming financial data. This research contributes an effective and scalable framework that can be integrated with machine learning techniques to enhance real-time financial crime detection and mitigation.

Systematic Detection, Financial Crime Detection, Kaggle SFinDSet, Layering and Tumbling, Anomaly Detection

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