Business Process Anomali Detection using Multi-Level Class Association Rule Learning

Fernandes Sinaga, Riyanarto Sarno

Abstract


Recently, Business Process Management System (BPMS) is widely used by companies in order to manage their business process. The company’s business process has a possibility to have changes which can cause some variations of business process. These variations might be contain some anomalies. Any anomalies that can make some losses for the company can be regarded as a fraud. There were some research have done to detect anomalies in business process. But, there is some issues that still need improvement especially on the accuracy. This paper proposed Multi-Level Class Association Rule Learning method (ML-CARL) to detect business process anomalies accurately. This method is supported by the process mining method which is used to analyze the anomalies in process. From the experiment, ML-CARL method can detect anomalies with an accuracy of 0.99 and better than ARL method in previous research. It can be concluded that ML-CARL method can increase the accuracy of business process anomaly detection.

Keywords


Business process; Anomaly detection; Process mining; Multi-level class association rule learning

Full Text:

PDF

References


R. Sarno, A.B. Sanjoyo, I. Mukhlash and H.M. Astuti, "Petri Net Model of ERP Business Process Variations for Small and Medium Enterprises," Journal of Theoretical and Applied Information Technology, vol. 54 No.1, August 2013, pp. 31-38.

J. Stoop, "A case study on the theoretical and practical value of using process mining for the detection of fraudulent behavior in the procurement process," in Process Mining and Fraud Detection, Netherlands, Twente University, 2012, pp. 22-63.

"Report to the Nations on Occupational Fraud and Abuse," ACFE, 2014, p.19.

M. Jans, N. Lybaert, K. Vanhoof, and J. M. van der Werf, "A business process mining application for internal transaction fraud mitigation," Expert Systems with Applications, vol. 38, 2011, pp. 13351-13359.

Wells, J.T. Occupational Fraud and Abuse. Dexter, MI: Obsidian., 1997, p.221.

F. Ogwueleka, Data Mining Application in Credit Card Fraud Detection System, Nigeria: Department of Computer Science, University of Abuja, 2011, pp.311-322

Ngai, E.W.T.,Yong Hu, Wong, Y.H., Chen Y.,Sun, X, "The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature," Decision Support Systems, vol. 50, 2011, pp. 559-569.

Bhattacharyya, S., Sanjeev J., Tharakunnel, K., Westland, J.C, "Data mining for credit card fraud: A comparative study," Decision Support Systems, vol. 50, 2011, pp. 602-613.

D. Sanchez, M. Vila, L. Cerda, and J. Serrano, "Association rules applied to credit card fraud detection," Expert Systems with Applications, pp. 3630–3640, 2009.

R. Sarno, R. D. Dewandono, T. Ahmad, M. F. Naufal and F. Sinaga, Hybrid Association Rule Learning and Process Mining for Fraud Detection, IAENG International Journal of Computer Science, 2015, pp. 59-72




DOI: http://dx.doi.org/10.12962/j23546026.y2015i1.1135

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.