Abstract :

Traditional Rule-based Anti-money Laundering (AML) Monitoring Systems Generate Large Numbers Of Falsepositive Alerts And Are Rigid When New Laundering Strategies Emerge. This Paper Presents A Hybrid AML Framework That Combines Seven Engineered Rule-based Risk Signals With Variational-autoencoder (VAE) Behavioural Embeddings And Graph Neural Network (GNN) Transaction-network Embeddings. The Framework Was Evaluated On 54,258 Real-world SWIFTbased Cross-border Payment Records From An East African Commercial Bank. After Cleaning, Currency Conversion, Log Transformation, Deep Representation Learning, Feature Fusion, Correlation Filtering, And Mutual-information Selection, The Fused Feature Space Was Reduced From 23 To 9 Informative Attributes. Isolation Forest, Local Outlier Factor, And One-Class Support Vector Machine (OCSVM) Were Compared Under A Semi-supervised Novelty-detection Protocol. OCSVM Achieved 99.63% Precision Within The Top 5% Of Ranked Alerts, While LOF Obtained The Highest ROC-AUC Of 0.8459. The Framework Identified 536 Novel Anomalies Not Captured By Rule-based Heuristics And Rejected 1,275 Rule-generated Alerts. SHAP Explanations Expose The Influence Of Rule, VAE, And GNN Features, While Institutional Validation Showed Approximately 1,000 Transactions Per Second On Standard Intel Core I7/16 GB Hardware.

Published:

01-8-2026

Issue:

Vol. 26 No. 8 (2026)

Page Nos:

1133-1138

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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