Business process anomaly detection using ontology-based process modelling and Multi-Level Class Association Rule Learning

Many companies in the world have used the business process management system (BPMS). This system is used to manage and analyze the running business process in the company. Every business process has a possibility to have changes in its realization. Those changes generate some variations of the business process. The variations, can be in line with the company's principles and or become an anomaly for the company. These anomalies can cause frauds which make some losses for the company. In order to reduce the losses, business process anomaly detection method is needed. This paper proposed ontology-based process modeling to model and capture the business process anomalies and the method of multi-level class association rule learning (ML-CARL) to detect fraud in business process. From the experiment which have been done in this paper, the accuracy of 0.99 was obtained from the ML-CARL method. It could be concluded that ontology-based process modeling and the ML-CARL method can detect business process anomalies well.