Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems?

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 im- provement 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 con- trol, data mining and supervised versus unsupervised learning. Start- ing from this background, it is interesting to investigate the use of data mining techniques for detection of asset misappropriation, start- ing from unsupervised data. In this study, procurement fraud stands as an example of asset misappropriation. Data are provided by an in- ternational 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.