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.
|Title of host publication||Surveillance technologies and early warning systems|
|Subtitle of host publication||data mining applications for risk detection|
|Editors||Ali Serhan Koyuncugil, Nermin Ozgulbas|
|Place of Publication||Hershey, PA|
|Number of pages||14|
|ISBN (Print)||9781616928650, 1616928654|
|Publication status||Published - 2011|