Data Mining and Economic Crime Risk Management

Data Mining and Economic Crime Risk Management

Mieke Jans (Hasselt University, Belgium), Nadine Lybaert (Hasselt University, Belgium) and Koen Vanhoof (Hasselt University, Belgium)
DOI: 10.4018/978-1-61692-865-0.ch011
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Abstract

Economic crime is a billion dollar business and is substantially present in our current society. Both researchers and practitioners have gone into this problem by looking for ways of fraud mitigation. Data mining is often called in this context. In this chapter, the application of data mining in the field of economic crime, or corporate fraud, is discussed. The classification external versus internal fraud is explained and the major types of fraud within these classifications will be given. Aside from explaining these classifications, some numbers and statistics are provided. After this thorough introduction into fraud, an academic literature review concerning data mining in combination with fraud is given, along with the current solutions for corporate fraud in business practice. At the end, a current state of data mining applications within the field of economic crime, both in the academic world and in business practice, is given.
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Background

What is Economic Crime?

There are many definitions of fraud, depending on the point of view considering. According to The American Heritage Dictionary, (Third Edition), fraud is defined as “a deception deliberately practiced in order to secure unfair or unlawful gain” (p.722). We can conclude that fraud is deception. Whatever industry the fraud is situated in or whatever kind of fraud you visualize, deception is always the core of fraud.

In a nutshell, Davia, et al. (2000) summarize: “Fraud always involves one or more persons who, with intent, act secretly to deprive another of something of value, for their own enrichment”. Also Wells (2005) stresses deception as the linchpin to fraud.

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