@article{(Open Science Index):https://publications.waset.org/pdf/12189,
	  title     = {A Heuristics Approach for Fast Detecting Suspicious Money Laundering Cases in an Investment Bank},
	  author    = {Nhien-An Le-Khac and  Sammer Markos and  M-Tahar Kechadi},
	  country	= {},
	  institution	= {},
	  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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {3},
	  number    = {12},
	  year      = {2009},
	  pages     = {2742 - 2746},
	  ee        = {https://publications.waset.org/pdf/12189},
	  url   	= {https://publications.waset.org/vol/36},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 36, 2009},