NDLI: A Data Mining-Based Solution for Detecting Suspicious Money Laundering Cases in an Investment Bank

Content Provider	IEEE Xplore Digital Library
Author	Nhien An Le Khac Markos, S. Kechadi, M.-T.
Copyright Year	2010
Abstract	Today, money laundering 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 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 money laundering activities. Within the scope of a collaboration project for the purpose of developing a new solution for the anti-money laundering Units in an international investment bank, we proposed a simple and efficient data mining-based solution for anti-money laundering. In this paper, we present this solution developed as a tool and show some preliminary experiment results with real transaction datasets.
Starting Page	235
Ending Page	240
File Size	537643
Page Count	6
File Format	PDF
ISBN	9781424460816
e-ISBN	9781424460823
DOI	10.1109/DBKDA.2010.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-04-11
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Law Stability Terrorism data mining neural networks Educational institutions Data mining Computer science anti-money laundering Investments Delta modulation clustering Legal factors
Content Type	Text
Resource Type	Article

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