Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems?
Mieke Jans, Nadine Lybaert, Koen Vanhoof
Abstract Fraud is a million dollar business and it’s increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by ’chance’ means. The second best detection method is internal control. As a result, it would be advisable to search for improvement of internal control systems. Taking into consideration the promising success stories of companies selling data mining software, along with the positive results of research in this area, we evaluate the use of data mining techniques for the purpose of fraud detection. Are we talking about real success stories, or salesmanship? For answering this, first a theoretical background is given about fraud, internal control, data mining and supervised versus unsupervised learning. Starting from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, starting from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an international service-sector company. After mapping out the purchasing process, ’hot spots’ are identified, resulting in a series of known frauds and unknown frauds as object of the study.

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Introduction

Fraud is a million dollar business and it is increasing every year. ”45% of companies worldwide have fallen victim to economic crime in 2004 and 2005. The average damage to the companies from tangible frauds (i.e. asset misappropriation, false pretences, and counterfeiting) was US$ 1.7 million.” according to the ’Global economic crime survey 2005’ of PriceWaterhouseCoopers. Journal headlines and news topics indicate the same trend of increasing fraudulent behavior. Given these numbers, it is remarkable