Leveraging Supervised Machine Learning Algorithms for Enhanced Anomaly Detection in Anti-Money Laundering (AML) Transaction Monitoring Systems: A Comparative Analysis of Performance and Explainability
Keywords:
Anti-Money Laundering (AML), Transaction Monitoring System (TMS), Supervised Machine Learning, Anomaly Detection, Feature Engineering, Model ExplainabilityAbstract
Financial crime evolves often, requiring AML compliance enhancements. The TMS detects money laundering. Traditional rule-based TMS recognizes patterns but not new washing methods. ML can evaluate complex connections in large datasets and find surprising abnormalities, thus I enjoy it. Research studies how supervised ML systems detect AML transaction monitoring abnormalities.
The essay begins with AML regulation framework and FI AML compliance. Rule-based TMS are static, false positive-prone, and cannot identify novel laundering typologies.
Supervised ML follows AML. We teach categorization, feature engineering, and model validation. Next, top supervised ML models for AML transaction monitoring are compared. SVMs, RFs, and GBMs are compared for anomaly detection. Accuracy, generalizability, interpretability, and processing efficiency are assessed.
AML using ML involves data quality and feature engineering. The study suggests choosing and structuring transaction data to enhance model performance. Customer profiling, transaction characteristics (amount, frequency, destination), and network analysis may employ raw transaction data.
Research examines AML model explainability. Although "black-box" and unregulated, ML models recognize patterns effectively. Interpretable ML methods like LIME and SHAP may explain human assessment and system trust model predictions.
Supervised ML methods are compared using a real-world AML transaction dataset. We examine dataset selection, pre-processing, and assessment. This section covers model training and validation for reliable, generalizable findings.
Results of empirical comparisons follow. ML algorithms are assessed by accuracy, precision, recall, F1-score, and ROC Curve area. The accuracy-interpretability trade-off highlights FI technique choice. The essay concludes with study results, limits, and prospects. The comparative research shows that supervised ML systems detect AML transaction monitoring abnormalities better. Understanding research limitations including poor real-world AML data. Research suggests unsupervised and deep learning architectures may improve AML transaction monitoring.
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