Tab-AML: A Transformer Based Transaction Monitoring Model for Anti-Money Laundering
Authors: Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H., Aksu, G.
Journal: Proceedings 2025 IEEE Conference on Artificial Intelligence Cai 2025
Publication Date: 01/01/2025
Pages: 161-167
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.
https://eprints.bournemouth.ac.uk/41607/
Source: Scopus
Tab-AML: A Transformer Based Transaction Monitoring Model for Anti-Money Laundering
Authors: Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H., Aksu, G.
Journal: 2025 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI
Publication Date: 2025
Pages: 161-167
ISBN: 979-8-3315-2401-2
DOI: 10.1109/CAI64502.2025.00033
https://eprints.bournemouth.ac.uk/41607/
Source: Web of Science
Tab-AML: A Transformer Based Transaction Monitoring Model for Anti-Money Laundering
Authors: Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Aksu, G., Dogan, H.
Conference: IEEE Conference on Artificial Intelligence (IEEE CAI 2025)
Dates: 05/05/2025
Publication Date: 11/06/2025
Abstract:Money laundering signifies a major challenge and risk in the global economic landscape. We introduce Tab- AML, 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 SAML- D dataset, outperforming models such as TabTransformer and XGBoost. These findings highlight the potential of transformer- based models in advancing anti-money laundering efforts.
https://eprints.bournemouth.ac.uk/41607/
Source: Manual
Tab-AML: A Transformer Based Transaction Monitoring Model for Anti-Money Laundering.
Authors: Oztas, B., Cetinkaya, D., Adedoyin, F., Budka, M., Dogan, H., Aksu, G.
Journal: CAI
Publication Date: 2025
Pages: 161-167
Publisher: IEEE
ISBN: 979-8-3315-2400-5
https://eprints.bournemouth.ac.uk/41607/
https://doi.org/10.1109/CAI64502.2025
Source: DBLP