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

• Published in: 2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications pp. 235 - 240
• Main Authors: An Le Khac, Nhien, Markos, Sammer, Kechadi, M-Tahar
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2010
• Subjects: anti-money laundering; clustering; Computer science; Data mining; Delta modulation; Drugs; Educational institutions; Investments; Law; Legal factors; neural networks; Stability; Terrorism
• ISBN: 9781424460816; 1424460816
• DOI: 10.1109/DBKDA.2010.27

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.