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Detecting Gains from Service Criminal Activities Using Generic Algorithms for Money Laundering Detection

Detecting Gains from Service Criminal Activities Using Generic Algorithms for Money Laundering Detection
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Author(s): Christoph Blodig (University of Cooperative Education Heidenheim, Germany), Tino Jahnke (University of Cooperative Education Heidenheim, Germany) and Juergen Seitz (University of Cooperative Education Heidenheim, Germany)
Copyright: 2005
Pages: 5
Source title: Managing Modern Organizations Through Information Technology
Source Editor(s): Mehdi Khosrow-Pour (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-822-2.ch137

Abstract

This paper covers the field of money laundering detection in Germany. The demand for the approach has been initially developed by the BMW Cash Group, Munich where severe criminal activities like money laundering need to be monitored automatically. Therefore, a statistical rule-based approach is discussed in the first part of the paper. In order to enhance the monitoring concept a genetic algorithms approach is applied to design a real-time monitoring framework.

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