Research Article
Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank
@INPROCEEDINGS{10.1007/978-3-642-11534-9_8, author={Nhien-An Le-Khac and Sammer Markos and Mohand-Tahar Kechadi}, title={Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank}, proceedings={Digital Forensics and Cyber Crime. First International ICST Conference, ICDF2C 2009, Albany, NY, USA, September 30-October 2, 2009, Revised Selected Papers}, proceedings_a={ICDF2C}, year={2012}, month={5}, keywords={data mining anti money laundering clustering neural network}, doi={10.1007/978-3-642-11534-9_8} }- Nhien-An Le-Khac
Sammer Markos
Mohand-Tahar Kechadi
Year: 2012
Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank
ICDF2C
Springer
DOI: 10.1007/978-3-642-11534-9_8
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.