Today, money laundering (ML) poses a serious threat not only to fi- nancial institutions but also to the nation. This criminal activity is becoming more and more sophisticated and seems to have moved from the cliche of drug trafficking to financing terrorism and surely not forgetting personal gain. Most international financial institutions have been implementing anti-money launder- ing solutions (AML) to fight investment fraud. However, traditional investiga- tive 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 in- vestment 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.
['Nhien-An Le-Khac', 'Sammer Markos', 'Mohand Tahar Kechadi']
Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank