Data Mining in the Investigation of Money Laundering and Terrorist Financing

Data Mining in the Investigation of Money Laundering and Terrorist Financing

Ibrahim George (Macquarie University, Australia) and Manolya Kavakli (Macquarie University, Australia)
DOI: 10.4018/978-1-61692-865-0.ch012
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Abstract

In this chapter, the authors explore the operational data related to transactions in a financial organisation to find out the suitable techniques to assess the origin and purpose of these transactions and to detect if they are relevant to money laundering. The authors‘ purpose is to provide an AML/CTF compliance report that provides AUSTRAC with information about reporting entities‘ compliance with the Anti-Money Laundering and Counter-Terrorism Financing Act 2006. Their aim is to look into the Money Laundering activities and try to identify the most critical classifiers that can be used in building a decision tree. The tree has been tested using a sample of the data and passing it through the relevant paths/scenarios on the tree. The success rate is 92%, however, the tree needs to be enhanced so that it can be used solely to identify the suspicious transactions. The authors propose that a decision tree using the classifiers identified in this chapter can be incorporated into financial applications to enable organizations to identify the High Risk transactions and monitor or report them accordingly.
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Background

Money Laundering

Money laundering involves moving illicit funds, which may be linked to drug trafficking or organized crime, through a series of transactions or accounts to disguise origin or ownership. There are many countries suffering from the consequences of money laundering. China, for example, is facing severe challenge on money laundering with an estimated 200 billion RMB laundered annually (Wang & Yang, 2007) Money laundering is the process undertaken to conceal the true origin and ownership of the profits of criminal activities. These profits can be the proceeds from crimes such as:

  • Drug trafficking;

  • Fraud;

  • Tax evasion;

  • Illegally trading in weapons;

  • Enforced prostitution;

  • Slavery; and

  • People smuggling.

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