Increasing Accuracy of Process-based Fraud Detection Using a Behavior Model

Abstract

Process-based fraud (PBF) is fraud caused by deviation from a business process model. Some studies have proposed methods for PBF detection; however, these are still not able to fully detect the occurrence of fraud. In this context, we propose a new method of PBF detection which carries out the behavior of the originators (users who perform events) to adjust the levels of fraud occured in the events. In this research, we propose a method of PBF detection with behavior model in order to increase accuracy. This is done firstly by analyzing the business processes that correspond to those in the standard operating system (SOP). Secondly, by calculating the event execution performed by the originator and his/her relations within the organization, whose behavior is then analyzed. Thirdly, by using the number of deviations and the originator behavior to calculate the attribute value. By using attribute importance weights, an attribute rating of each originator is kept. Finally, Multi Attribute Decision Making is used to decide the PBF rating of a case, on the basis of which it is decided whether fraud occurred or not. The experimental results show that this behavior model is able to reduce false positive and false negative, therefore, the method can increase the accuracy level by 0.03.