Edgar Alonso Lopez-Rojas
School of Computing, Blekinge Institute of Technology, Sweden
Stefan Axelsson
School of Computing, Blekinge Institute of Technology, Sweden
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Published in: The 27th annual workshop of the Swedish Artificial Intelligence Society (SAIS); 14-15 May 2012; Örebro; Sweden
Linköping Electronic Conference Proceedings 71:5, p. 33-40
Published: 2012-05-14
ISBN:
ISSN: 1650-3686 (print), 1650-3740 (online)
Criminals use money laundering to make the proceeds from their illegal activities look legitimate in the eyes of the rest of society. Current countermeasures taken by financial organizations are based on legal requirements and very basic statistical analysis. Machine Learning offers a number of ways to detect anomalous transactions. These methods can be based on supervised and unsupervised learning algorithms that improve the performance of detection of such criminal activity.
In this study we present an analysis of the difficulties and considerations of applying machine learning techniques to this problem. We discuss the pros and cons of using synthetic data and problems and advantages inherent in the generation of such a data set. We do this using a case study and suggest an approach based on Multi-Agent Based Simulations (MABS).
Machine Learning; Anti-Money Laundering; Money Laundering; Anomaly Detection; Synthetic Data; Multi-Agent Based Simulation
[1] Naoki Abe; Bianca Zadrozny; and John Langford. Out- lier detection by active learning. Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’06; page 504; 2006.
[2] E.L. Barse; H. Kvarnstrom; and E. Johnson. Syn- thesizing test data for fraud detection systems. 19th Annual Computer Security Applications Conference; 2003. Proceedings.; pages 384-394; 2003.
[3] B. Bartlett. The negative e ects of money laun-dering on economic development. Asian Develop- ment Bank Regional Technical Assistance Project No; (5967); 2002.
[4] RJ Bolton and DJ Hand. Unsupervised pro ling meth- ods for fraud detection. Conference on credit scoring and credit control; 2001.
[5] R.J. Bolton and D.J. Hand. Statistical fraud detection: A review. Statistical Science; 17(3):235-249; 2002.
[6] L Breiman. Random forests. Machine learning; pages 5-32; 2001.
[7] NV Chawla; KW Bowyer; L.O. Hall; and W.P. Kegelmeyer. SMOTE: synthetic minority over- sampling technique. Journal of Arti cial Intelligence Research2; 16:321-357; 2.
[8] Zengan Gao and Mao Ye. A framework for data mining-based anti-money laundering research. Journal of Money Laundering Control; 10(2):170-179; 2007.
[9] John Hunt. The new frontier of money laundering: how terrorist organizations use cyberlaundering to fund their activities; and how governments are trying to stop them. Information & Communications Technol- ogy Law; 20(2):133-152; June 2011.
[10] L I U Junqiang. Optimal Anonymization for Transac- tion. Chinese Journal of Electronics; 20(2); 2011.
[11] P.J. Lin; B. Samadi; Alan Cipolone; D.R. Jeske; Sean Cox; C. Rendon; Douglas Holt; and Rui Xiao. De- velopment of a synthetic data set generator for build- ing and testing information discovery systems. In In- formation Technology: New Generations; 2006. ITNG 2006. Third International Conference on; pages 707-712. IEEE; 2006.
[12] C M Macal and M J North. Tutorial on agent- based modelling and simulation. Journal of Simulation; 4(3):151-162; September 2010.
[13] Dan Magnusson. The costs of implementing the anti- money laundering regulations in Sweden. Journal of Money Laundering Control; 12(2):101-112; 2009.
[14] J Pavon; M Arroyo; S Hassan; and C Sansores. Agent-based modelling and simulation for the analysis of so- cial patterns. Pattern Recognition Letters; 29(8):1039-1048; June 2008.
[15] Clifton Phua; Vincent Lee; Kate Smith; and Ross Gayler. A comprehensive survey of data mining- based fraud detection research. Arxiv preprint arXiv:1009.6119; 2010.
[16] J.R. Quinlan. Simplifying decision trees. International journal of man-machine studies; 27(3):221-234; 1987.
[17] Agus Sudjianto; Sheela Nair; Ming Yuan; Aijun Zhang; Daniel Kern; and Fernando Cela-Daz. Statistical Methods for Fighting Financial Crimes. Technomet- rics; 52(1):5-19; February 2010.
[18] Yabo Xu; Ke Wang; Ada Wai-chee Fu; Hong Kong; and Philip S Yu. Anonymizing Transaction Databases for Publication. International Journal; pages 767-775; 2008.
[19] Dianmin Yue; Xiaodan Wu; Yunfeng Wang; Yue Li; and Chao-Hsien Chu. A Review of Data Mining-Based Financial Fraud Detection Research. In 2007 Interna- tional Conference on Wireless Communications; Net- working and Mobile Computing; pages 5514-5517. Ieee; September 2007.
[20] ZM Zhang and JJ Salerno. Applying data mining in investigating money laundering crimes. discovery and data mining; (Mlc):747; 2003.
