Have a personal or library account? Click to login
Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights Cover

Navigating the Complexity of Money Laundering: Anti–money Laundering Advancements with AI/ML Insights

Open Access
|Aug 2024

References

  1. Kute D V, Biswajeet P, Nagesh S, & Abdullah A, “Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review”, IEEE access 9: 82300–82317, June 2021.
  2. Abid D, Rahmatullah W A, Alif M A F, & Rinci K H, “Penerapan Metode K-Means Clustering Untuk Analisa Penjualan Komoditas Toko Tani Indonesia”, KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika 3, no. 2: 25–30, Oct 2022.
  3. Kavisha M S, “Anti Money Laundering: Proactive involvement and perception of Internal Auditors in Anti-Money Laundering Compliance Review”, PhD diss., GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD, Feb 2024.
  4. Omri R, “Applying supervised machine learning algorithms for fraud detection in anti-money laundering”, Journal of Modern Issues in Business Research 1, no. 1: 14–26, Dec 2021.
  5. Zhiyuan C, Dinh V K L, Ee N T, Amril N, Ettikan K K, & Kim S L, “Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review”, Knowledge and Information Systems 57: 245–285, Feb 2018.
  6. Ítalo D G, Luiz H A C, & Erick G M, “Graph Neural Networks Applied to Money Laundering Detection in Intelligent Information Systems”, In Proceedings of the XIX Brazilian Symposium on Information Systems, pp. 252–259, May 2023.
  7. Mark W, Giacomo D, Jie C, Daniel K I W, Claudio B, Tom R, & Charles E L, “Anti-money laundering in bitcoin: Experimenting with graph convolutional networks for financial forensics”, arXiv preprint arXiv:1908.02591, July 2019
  8. Rasmus I T J, & Alexandros I, “Fighting money laundering with statistics and machine learning”, IEEE Access 11: 8889–8903, Jan 2023.
  9. Charitou C, Simo D, & Artur D G, “Synthetic data generation for fraud detection using gans”, arXiv preprint arXiv:2109.12546, Sept 2021.
  10. Fredrik J, & Martin J, “Finding Money Launderers Using Heterogeneous Graph Neural Networks”, arXiv preprint arXiv:2307.13499, July 2023.
  11. Xiong K, Binhui P, Yang J, & Tiying L, “A hybrid deep learning model for online fraud detection”, In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 431–434, Jan 2021.
  12. Jingguang H, Yuyun H, Sha L, & Kieran T, “Artificial intelligence for anti-money laundering: a review and extension”, Digital Finance 2, no. 3: 211–239, June 2020.
  13. Ashwini K, Sanjoy D, Vishu T, Rabindra N S, & Ankush G, “Analysis of classifier algorithms to detect anti-money laundering”, Computationally intelligent systems and their applications: 143–152, Apr 2021.
  14. Ahmed N B, Almohammady A, Mohamed S F, & Kamal R R, “Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering”, arXiv preprint arXiv:2403.00777, Apr 2024.
  15. Alkhalili M, Mahmoud H Q, & Fadi A, “Investigation of applying machine learning for watch-list filtering in anti-money laundering”, iEEE Access 9: 18481–18496, Jan 2021.
  16. William G, & Athenia B S, “Anti-money laundering and customer due diligence: empirical evidence from South Africa”, Journal of Money Laundering Control 26, no. 7: 224–238, Dec 2023.
  17. Charanjit S, & Wangwei L, “Can artificial intelligence, RegTech and CharityTech provide effective solutions for anti-money laundering and counter-terror financing initiatives in charitable fundraising”, Journal of Money Laundering Control 24, no. 3: 464–482, July 2021.
  18. Boris K, Evgenii V, Alexander D, & Antoni W, “Interpretable Machine Learning for Financial Applications”, In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, pp. 721–749. Cham: Springer International Publishing, Aug 2023.
  19. Lucas S G, André L K, Platão G T N, Davenilcio L S, & Taciana M, “Anti-money laundering and financial fraud detection: A systematic literature review”, Intelligent Systems in Accounting, Finance and Management 29, no. 2: 71–85, May 2022.
  20. Wei-Yu C, Shing-Han L, & Yung-Hsin W, “Research on Natural Language Processing in Financial Risk Detection”, In Cognitive Cities: Second International Conference, IC3 2019, Kyoto, Japan, September 3–6, 2019, Revised Selected Papers 2, pp. 448–455. Springer Singapore, June 2020.
  21. Abdul K L, & Leyla, “Anomaly Detection in Financial Transaction Time Series Data”, June 2023.
  22. Farman A, & Pradeep S, “Big Data Analytics in Financial Econometrics”, Current Studies in Social Sciences 139, Dec 2022.
  23. Nadia P, Mirko Z, Fabio M, Muhammad Z S, & Stefano F, “Detecting anomalous cryptocurrency transactions: an aml/cft application of machine learning-based forensics”, arXiv preprint arXiv:2206.04803, June 2022.
  24. Mark L, “Predicting money laundering using machine learning and artificial neural networks algorithms in banks”, Journal of Applied Security Research 19, no. 1, Jan 2024.
  25. Martin J, Anders L, Ragnar B H, Geir Å, & Johannes L, “Detecting money laundering transactions with machine learning”, Journal of Money Laundering Control 23, no. 1 173–186, Jan 2020.
  26. Wai W L, Mohanad S, Siamak L, & Marius P, “Inspection-L: A Self-Supervised GNN-Based Money Laundering Detection System for Bitcoin”, Mar 2022.
  27. Sizheng W, & Suan L, “Financial Anti-Fraud Based on Dual-Channel Graph Attention Network”, Journal of Theoretical and Applied Electronic Commerce Research 19, no. 1: 297–314, Feb 2024.
  28. Alotibi J, Badriah A, Tahani A, Hosam A, & Abdullah B, “Money Laundering Detection using Machine Learning and Deep Learning”, International Journal of Advanced Computer Science and Applications 13, no. 10, Jan 2022.
  29. Zhenfeng S, Muhammad N A, & Akib J, “Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface”, Remote Sensing 16, no. 4: 665, Feb 2024.
  30. Nevine L, Mohammed A R, & Amr E M S, “Survey of machine learning approaches of anti-money laundering techniques to counter terrorism finance”, In Internet of Things—Applications and Future: Proceedings of ITAF 2019, pp. 73–87. Singapore: Springer Singapore, Apr 2020.
  31. Haobo Z, Junyuan H, Fan D, Steve D, Liangjie X, & Jiayu Z, “A privacy-preserving hybrid federated learning framework for financial crime detection”, arXiv preprint arXiv:2302.03654, Feb 2023.
  32. Li Y, & Abdallah S, “On hyperparameter optimization of machine learning algorithms: Theory and practice”, Neurocomputing 415 295–316, Nov 2020.
  33. Guy S H, Hong K K, Ronil V C, Amir H R, Shiwei H, Mark B, Michael J L, & Hamed A, “Peering into the black box of artificial intelligence: evaluation metrics of machine learning methods”, American Journal of Roentgenology 212, no. 1 38–43, Jan 2019.
  34. Abhishek V T, “Comparative assessment of regression models based on model evaluation metrics”, International Research Journal of Engineering and Technology (IRJET) 8, no. 09 2395–0056, Sep 2021.
  35. Željko Đ V, “Classification model evaluation metrics”, International Journal of Advanced Computer Science and Applications 12, no. 6 599–606, July 2021.
  36. Alaa T, “Classification assessment methods”, Applied computing and informatics 17, no. 1 168–192, July 2020.
  37. Xiao W, Meiqi Z, Deyu B, Peng C, Chuan S, & Jian P, “Am-gcn: Adaptive multi-channel graph convolutional networks”, In Proceedings of the 26th ACM SIGKDD International conference on knowledge discovery & data mining, pp. 1243–1253, Aug 2020.
  38. Sugiyarto S, M. Yahya F A, Anggi S, Dyiyah K E A, & Aris T, “Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer”, HighTech and Innovation Journal, 4(3), 531–542, Sept 2023.
  39. Surono S, Yahya M F A, Anggi S, Dyiyah K E A, & Aris T, “Comparison of CNN Classification Model using Machine Learning with Bayesian Optimizer”, HighTech and Innovation Journal, 4(3), 531–542, Sept 2023.
  40. Dibs H, Abu Dabous S, Shaaban M, & Marzouk M, “Multi-Fusion Algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis”, Remote Sensing, 13(11), 2098, June 2023.
Language: English
Submitted on: Apr 4, 2024
Published on: Aug 6, 2024
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2024 Hitarth Gandhi, Kevin Tandon, Shilpa Gite, Biswajeet Pradhan, Abdullah Alamri, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.