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ToolsOztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H. and Aksu, G., 2025. Tab-AML: A Transformer Based Transaction Monitoring Model for Anti-Money Laundering. In: IEEE Conference on Artificial Intelligence (CAI). New York: IEEE, 161-167.
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Official URL: https://doi.org/10.1109/CAI64502.2025
DOI: 10.1109/CAI64502.2025.00033
Abstract
Money laundering signifies a major challenge and risk in the global economic landscape. We introduce TabAML, a deep-learning model for transaction monitoring that achieves high detection accuracy while significantly reducing false positives. Incorporating a dual-masked Transformer encoder with a shared embedding component and Residual Attention Layers, Tab-AML achieved an ROC-AUC of 93.01, a 98% true positive rate, and a 51% false positive rate on the SAMLD dataset, outperforming models such as TabTransformer and XGBoost. These findings highlight the potential of transformer-based models in advancing anti-money laundering efforts.
| Item Type: | Book Section |
|---|---|
| ISBN: | 979-8-3315-2400-5 |
| Additional Information: | 5-7 May 2025, Santa Clara, CA, USA |
| Uncontrolled Keywords: | Anti-Money Laundering; Artificial Intelligence; Machine Learning; Suspicious Transactions; Transaction Monitoring; Transformer Encoder; Deep Learning |
| Group: | Faculty of Media, Science and Technology |
| ID Code: | 41607 |
| Deposited By: | Symplectic RT2 |
| Deposited On: | 02 Dec 2025 15:01 |
| Last Modified: | 02 Dec 2025 15:01 |
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