Commenced in January 2007
Paper Count: 31106
A Heuristics Approach for Fast Detecting Suspicious Money Laundering Cases in an Investment Bank
Abstract:Today, money laundering (ML) poses a serious threat not only to financial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliché of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money laundering solutions (AML) to fight investment fraud. However, traditional investigative techniques consume numerous man-hours. Recently, data mining approaches have been developed and are considered as well-suited techniques for detecting ML activities. Within the scope of a collaboration project for the purpose of developing a new solution for the AML Units in an international investment bank, we proposed a data mining-based solution for AML. In this paper, we present a heuristics approach to improve the performance for this solution. We also show some preliminary results associated with this method on analysing transaction datasets.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078725Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 3195
 L. Genzman, Responding to organized crime: Laws and law enforcement. Organized crime, In H.Abadinsky (Ed.) Belmont, CA: Wadsworth, pp. 342.
 R. Baker, The biggest loophole in the free-market system. Washington Quarterly, 22, 1999, pp. 29-46.
 R. C. Watkins et al, Exploring Data Mining technologies as Tool to Investigate Money Laundering. Journal of Policing Practice and Research: An International Journal. Vol. 4, No. 2, January 2003, pp. 163-178.
 J. Han and M. Kamber, Data Mining: Concept and Techniques. Morgan Kaufmann publishers, 2nd Eds., Nov. 2005.
 J. Tang, J. Yin, Developing an intelligent data discriminating system of anti-money laundering based on SVM, Proceedings of the Four International Conference on Machine Learning and Cybernetics, Guangzhou, Aug. 2005: pp.3453-3457.
 Z. Zang, J.J. Salermo and P. S. Yu, Applying Data mining in Investigating Money Laundering Crimes, SIGKDD-03, August 2003, Washington DC, USA. pp: 747-752.
 N-A. Le-Khac, S. Markos, M. O'Neill, A. Brabazon and M-T. Kechadi, An Efficient Search Tool for an Anti-Money Laundering Application of an Multi-National Bank's Dataset, The 2009 International Conference on Information and Knowledge Engineering, July 13-16, 2009 (IKE 2009), LA, USA.
 N-A. Le-Khac, S. Markos and M-T. Kechadi, Towards a new Data Mining-based approach for Anti Money laundering in an international investment bank. a NY, USA (to appear).
 R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, Prentice Hall, 1995.
 B. Scholkopf, A short tutorial on kernels. Microsoft Research, Tech Rep: MSR-TR-200-6t, 2000.
 J. Kingdon, AI Fights Money Laundering, IEEE Transactions on Intelligent Systems, 2004, pp. 87-89.
 B. Scholkopf and J. Plattz, Estimating the support of a high dimensional distribution, Neural Computing, Vol. 13, No. 7, 2001: pp1443-1472.
 D.R Wilson and T. R. Martinez, Improved Heterogeneous distance functions. Journal of Artificial Intelligence Research, Vol. 6, No. 1, 1997: pp 1-34.
 J. Tang, A Framework on Developing an Intelligent Discriminating System of Anti Money Laundering, International Conference on Financial and Banking, Czech Rep., 2005
 G.S. Vidyashankar, R. Natarajan and S. Sanyal, Mining your way to combat money laundering. DM Review Special Report, Oct 2007.
 G. Gan, C. Ma and J. Wu, Data Clustering: Theory, Algorithms and Applications. Siam publishers 2007, pp 161-182