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Data Mining to Combat Money Laundering

DM Direct Special Report, October 2, 2007 Issue

By Rajesh Natarajan and Subhrangshu Sanyal and G. S. Vidyashankar

Money laundering generally involves a series of multiple transactions used to disguise the source of financial assets. This enables those assets to be used without compromising the criminals who are seeking to use the funds. Through money laundering, the criminal tries to transform the monetary proceeds derived from illicit activities into funds with an apparently legal source. Worldwide value of laundered funds in a year ranges between $500 billion to $1 trillion, according to the United Nations Office on Drugs and Crime. Weak financial regulatory systems, lax enforcement, gaps in the information systems of financial institutions and corruption are key factors that make certain jurisdictions particularly attractive for laundering illicit proceeds.

In this article we present the risks of money laundering and the need for anti-money laundering (AML). Challenges in detecting occurrences of money laundering using traditional methods and the limitations of the same are outlined. We also examine how data mining can deal with the complexities of the modern money laundering operations. Finally, the advantages of data mining and its challenges are elaborated.

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