Data mining in the investigation of money laundering and terrorist financing

Ibrahim George, Manolya Kavakli

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

2 Citations (Scopus)

Abstract

In this chapter, the authors explore the operational data related to transactions in a financial organi-sation 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.

Original languageEnglish
Title of host publicationSurveillance technologies and early warning systems
Subtitle of host publicationdata mining applications for risk detection
EditorsAli Serhan Koyuncugil, Nermin Ozgulbas
Place of PublicationHershey, PA
PublisherIGI Global
Pages228-241
Number of pages14
ISBN (Electronic)9781616928674
ISBN (Print)9781616928650, 1616928654
DOIs
Publication statusPublished - 2011

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