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 based in Ireland, we propose a new data mining-based approach for AML. In this paper, we present this approach and some preliminary results associated with this method when applied to transaction datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Baker, R.: The biggest loophole in the free-market system. Washington Quarterly 22, 29–46 (1999)
Brabazon, A., O’Neill, M.: Biologically inspired algorithms for financial modelling. Springer, Heidelberg (2006)
Han, J., Kamber, M.: Data Mining: Concept and Techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2005)
Jain, R., Kasturi, R., Schunck, B.G.: Machine Vision. Prentice Hall, Englewood Cliffs (1995)
Kingdon, J.: AI Fights Money Laundering. IEEE Transactions on Intelligent Systems, 87–89 (2004)
Scholkopf, B.: A short tutorial on kernels, Microsoft Research, Rech. Rep.: MSR-TR-200-6t (2000)
Scholkopf, B., Plattz, J.: Estimating the support of a high dimensional distribution. Neural Computing 13(7), 1443–1472 (2001)
Steinhaus, H.: Sur la division des corp materiels en parties. Bull. Acad. Polon. Sci., C1. III IV, 801–804 (1956)
Tang, J., Yin, J.: Developing an intelligent data discriminating system of anti-money laundering based on SVM. In: Proceedings of the Four International Conference on Machine Learning and Cybernetics, Guangzhou, August 2005, pp. 3453–3457 (2005)
Tang, J.: A Framework on Developing an Intelligent Discriminating System of Anti Money Laundering. In: International Conference on Financial and Banking, Czech Rep. (2005)
Vapnik, V.: The Nature of Satistical Learning Theory. Springer, NewYork (1995)
Vidyashankar, G.S., Natarajan, R., Sanyal, S.: Mining your way to combat money laundering. DM Review Special Report (October 2007)
Watkins, R.C., et al.: Exploring Data Mining technologies as Tool to Investigate Money Laundering. Journal of Policing Practice and Research: An International Journal 4(2), 163–178 (2003)
Wilson, D.R., Martinez, T.R.: Improved Heterogeneous distance functions. Journal of Artificial Intelligence Research 6(1), 1–34 (1997)
Zang, Z., Salermo, J.J., Yu, P.S.: Applying Data mining in Investigating Money Laundering Crimes. In: SIGKDD 2003, Washington, DC, USA, August 2003, pp. 747–752 (2003)
Genzman, L.: Responding to organized crime: Laws and law enforcement. In: Abadinsky, H. (ed.) Organized crime, p. 342. Wadsworth, Belmont
Le-Khac, N.-A., Markos, S., O’Neill, M., Brabazon, A., Kechadi, M.-T.: An Efficient Search Tool for an Anti-Money Laundering Application of an Multi-National Bank’s Dataset. In: The 2009 International Conference on Information and Knowledge Engineering (IKE 2009), LA, USA, July 13-16 (to appear, 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Le-Khac, NA., Markos, S., Kechadi, MT. (2010). Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank. In: Goel, S. (eds) Digital Forensics and Cyber Crime. ICDF2C 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11534-9_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-11534-9_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11533-2
Online ISBN: 978-3-642-11534-9
eBook Packages: Computer ScienceComputer Science (R0)