Anti-Money Laundering (AML) Systems Using AI

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2025PI8G4N

    Published 06-04-2025

  • Anti-Money Laundering (AML), Artificial Intelligence (AI), Machine Learning, Financial Crime Detection, Fraud Analytics, Transaction Monitoring, Customer Risk Profiling, Suspicious Activity Detection, Regulatory Compliance, Deep Learning

    Issue

    Vol. 8 No. 1 (2025)

    Section

    Articles

    How to Cite

    Seshagiri, N. (2025). Anti-Money Laundering (AML) Systems Using AI. International Journal of Commerce, Finance and Digital Economy, 8(1), 01-17. https://doi.org/10.67228/3071642X/IJCFDE-2025PI8G4N

  • Abstract

    Anti-Money Laundering (AML) has emerged as a critical component of global financial security due to the increasing sophistication of financial crimes, digital banking platforms, cryptocurrency transactions, and cross-border payment systems. Traditional rule-based AML systems often generate excessive false-positive alerts, require significant manual intervention, and struggle to identify evolving laundering patterns. Recent advancements in Artificial Intelligence (AI) have significantly transformed AML by enabling intelligent transaction monitoring, customer risk profiling, anomaly detection, and predictive analytics. AI technologies such as Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Graph Neural Networks (GNNs) improve the capability of financial institutions to detect suspicious activities with greater accuracy while reducing operational costs and investigation time. This paper presents a comprehensive review of AI-driven AML systems, highlighting modern detection techniques, system architecture, data processing workflows, and regulatory compliance mechanisms. The study discusses the integration of supervised and unsupervised learning models for identifying hidden financial relationships and adaptive criminal behaviors. Furthermore, the paper examines challenges including data imbalance, explainability, privacy preservation, adversarial attacks, and evolving regulatory requirements. The proposed AI-based AML framework aims to improve detection efficiency, minimize false alarms, strengthen financial transparency, and enhance decision-making for regulatory authorities. The findings demonstrate that AI-powered AML systems provide a scalable, intelligent, and proactive solution for combating financial crimes in modern banking ecosystems.

  • References

    [1] Chen, Z., Zheng, Z., Cui, J., Ngai, E. C. H., Zheng, P., & Zhou, Y. (2018). Detecting money laundering transactions with machine learning methods. IEEE Access, 6, 64144–64154. https://doi.org/10.1109/ACCESS.2018.2878540

    [2] Weber, M., Domeniconi, G., Chen, J., Weidele, D. K. I., Bellei, C., Robinson, T., & Leiserson, C. E. (2019). Anti-money laundering in Bitcoin: Experimenting with graph convolutional networks for financial forensics. Proceedings of the KDD Workshop on Anomaly Detection in Finance.

    [3] Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569. https://doi.org/10.1016/j.dss.2010.08.006

    [4] Van Vlasselaer, V., Eliassi-Rad, T., Akoglu, L., Snoeck, M., & Baesens, B. (2017). GOTCHA! Network-based fraud detection for social security fraud. Management Science, 63(9), 3090–3110.

    [5] Baesens, B., Van Vlasselaer, V., & Verbeke, W. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. John Wiley & Sons.

    [6] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

    [7] Hamilton, W. L. (2020). Graph Representation Learning. Morgan & Claypool Publishers.

    [8] Molnar, C. (2022). Interpretable Machine Learning (2nd ed.). https://christophm.github.io/interpretable-ml-book/

    [9] European Banking Authority. (2021). Guidelines on customer due diligence and factors institutions should consider when assessing money laundering and terrorist financing risk. European Banking Authority.

    [10] Financial Action Task Force (FATF). (2023). International Standards on Combating Money Laundering and the Financing of Terrorism & Proliferation (FATF Recommendations). https://www.fatf-gafi.org

    [11] Financial Crimes Enforcement Network (FinCEN). (2021). Anti-Money Laundering Act of 2020. U.S. Department of the Treasury.

    [12] McMahan, B., Moore, E., Ramage, D., Hampson, S., & Aguera y Arcas, B. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), 1273–1282.

    [13] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems (NeurIPS), 30, 4765–4774.

    [14] Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR).

    [15] Aggarwal, C. C. (2017). Outlier Analysis (2nd ed.). Springer. https://doi.org/10.1007/978-3-319-47578-3.

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