From Rules to AI: Assessing Supervised Learning for AML Transaction Monitoring

Abstract

Financial crime changes frequently, requiring AML compliance improvements. TMS identifies money laundering. Tradition-based TMS recognizes patterns but not new washing procedures. I like ML because it can identify unanticipated irregularities in intricate linkages in large datasets. Research examines how supervised ML systems detect AML transaction monitoring irregularities.
An essay about AML legislation framework and FI AML compliance begins. Rule-based TMS are static, false positive-prone, and cannot detect new laundering typologies.
AML precedes supervised ML. We teach categorization, feature engineering, and model validation. Comparing top supervised ML models for AML transaction monitoring follows. Compare SVMs, RFs, and GBMs for anomaly detection. Assessments include accuracy, generalizability, interpretability, and processing efficiency.
ML-based AML requires data quality and feature engineering. The study advises selecting and arranging transaction data to improve model performance. Raw transaction data can be used for customer profiling, transaction characteristics (amount, frequency, destination), and network analysis.
Research investigates AML model explainability. Although "black-box" and uncontrolled, ML models discern patterns well. Interpretable ML approaches like LIME and SHAP can explain human assessment and system trust model predictions.
We compare supervised ML techniques using a real-world AML transaction dataset. We explore dataset selection, pre-processing, and evaluation. Model training and validation for dependable, generalizable results are covered here.
Results of empirical comparisons. ML algorithms are evaluated by accuracy, precision, recall, F1-score, and ROC Curve area. The accuracy-interpretability trade-off emphasizes FI method. Essay finishes with study results, restrictions, and prospects. The comparative research demonstrates that supervised ML systems detect AML transaction monitoring irregularities better. Recognizing research limitations like insufficient AML data. Research implies unsupervised and deep learning architectures may improve AML transaction monitoring