Fuzzy MADM Approach for Rating of Process-Based Fraud

Solichul Huda, Riyanarto Sarno, Tohari Ahmad

Abstract


Process-Based Fraud (PBF) is fraud enabled by process deviations that occur in business processes. Several studies have proposed PBF detection methods; however, false decisions are still often made because of cases with low deviation. Low deviation is caused by ambiguity in determining fraud attribute values and low frequency of occurrence. This paper proposes a method of detecting PBF with low deviation in order to correctly detect fraudulent cases. Firstly, the fraudulence attributes are established, then a fuzzy approach is utilized to weigh the importance of the fraud attributes. Further, multi-attribute decision making (MADM) is employed to obtain a PBF rating according to attribute values and attribute importance weights. Finally, a decision is made whether the deviation is fraudulent or not, based on the PBF rating. Experimental validation showed that the accuracy and false discovery rate of the method were 0.92 and 0.33, respectively.


Full Text:

PDF

References


Ngai, E.W.T., Hu, Y., Wong Y.H., Chen, Y. & Sun, X., The Application of Data Mining Techniques in Financial Fraud Detection: A Classificationframework and an Academic Review of Literature, Decision Support Systems, 50(3), pp. 559-569, 2010.

Amara, I., Amar, A.B. & Jarboui, A., Detection of Fraud in Financial Statements: French Companies as a Case Study, International Journal of Academic Research in Accounting, Finance and Management Sciences, 3(3), pp. 44-55, 2013.

Jans, M., van der Werf, M.J., Lybaert, N. & Vanhoof, K., A Business Process Mining Application for Internal Transaction Fraud Mitigation, Expert Systems with Applications, 38(10), pp. 13351-13359, 2011.

Kalyani, D.R. & Devi, D.U., Fraud Detection of Credit Payment System by Genetic Algorithm, International Journal of Scientific & Engineering Research, 3(7), pp. 1-6, 2012

Zaslavsky, V. & Strizhak, A., Credit Card Fraud Detection Using Self-Organizing Maps, Information & Security, 18(Cyber crime & cyber security), pp. 48-63, 2006.

Panigrahi, S., Kundu, A., Sural, S. & Majumdar, A.K., Credit Card Fraud Detection: A Fusion Approach Using Dempster-Shafer Theory and Bayesian Learning, Information Fusion, 10(4), pp. 354-363, 2009.

Shen, A., Tong, R. & Deng, Y., Application of Classification Models on Credit Card Fraud Detection, Proceedings of 2007 International Conference on Service System and Service Management, IEEE, Chengdu, China, pp. 1-4, 2007.

Chae, M. Shime, S. Cho, H. & Lee, B., An Empirical Analysis of Fraud Detection in Online Auctions: Credit Card Phantom Transaction, Proceedings of the 40th Annual Hawaii International Conference on System Sciences, IEEE, Waikoloa, USA, pp. 155a, 2007.

Chiu, C. & Tsai, C.Y., A Web Services-Based collaborative Scheme for Credit Card Fraud Detection, Proceedings of 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE '04), IEEE, Taipei, Taiwan, pp. 177-181, 2004.

Stoop, J.J., Process Mining and Fraud Detection, Thesis, Business Information Technology Department, Twente University, Enschede, Netherlands, 2012.

Dewandono, D.R., Process Sequence Mining For Fraud Detection Using CEP, Thesis, Informatics Department, Institut Teknologi Sepuluh Nopember, Surabaya, 2013.

Sarno, R., Dewandono, D.R. Ahmad, T., Naufal, M.F. & Sinaga, F., Hybrid Association Rule Learning and Process Mining for Fraud Detection, IAENG International Journal of Computer Science, 42(2), pp. 59-72, 2015.

Huda, S., Sarno, R., Ahmad, T. & Santoso, H.A., Identification of Process-based Fraud in Credit Application, 2014 2nd International Conference on Information and Communication Technology (ICoICT), Telkom University, Bandung, Indonesia, pp. 84-89, 2014.

Sarno, R., Sanjoyo, A.B., Mukhlash, I. & Astuti, M.H., Petri Net Model of ERP Business Process Variations for Small and Medium Enterprises, Journal of Theoretical and Applied Information Technology, 54(1), pp. 31-38, 2013.

Jans, M., Alles, M. & Vasarhelyi, M., The Case for Process Mining in Auditing: Sources of Value Added and Areas of Application, International Journal of Accounting Information Systems, 14 (1) pp. 1-20 , 2013.

Van der Aalst, W.M.P., Discovery, Conformance and Enhancement of Business Processes, Springer, pp. 7-8, December 2010.

Van der Aalst, W.M.P. & de Medeiros, A.K.A., Process Mining and Security: Detecting Anomalous Process Executions and Checking Process Conformance, Electronic Notes in Theoretical Computer Science, 121(Security Issues with Petri Nets and other Computational Models 2004), pp. 3-21, 2005.

Accorsi, R. & Stocker, T., On the Exploitation of Process Mining for Security Audits: The Conformance Checking Case, Proceedings of the 28th Annual ACM Symposium on Applied Computing, Riva del Garda Congress, Trento, Italy, pp. 1709-1716, 2012.

Vats, S., Vats, G., Vaish, R. & Kumar, V., Selection of Optimal Toll Collection System for India : A Subjective-Fuzzy Decision Making Approach, Applied Soft Computing, 21, pp. 444-452, 2014.

Zadeh, L.A., Fuzzy Sets, Information and Control, 8(3), pp. 338-353, 1965.

Barreiros, M.P., Grilo, A. & Cruz-Machado, V., Cabrita, M.R., Applying Fuzzy sets For ERP Systems Selection Within The Construction Industry, 2010 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM),IEEE, Singapore, pp. 320-324, 2010.

Shemshadi, A., Shirazi, H., Toreihi, M. & Tarokh, M.J., A Fuzzy VIKOR Method for Supplier Selection Based on Entropy Measure for Objective Weighting, Expert Systems with Applications, 38(10), pp. 12160-12167, 2011.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2015.9.2.1

Refbacks

  • There are currently no refbacks.


Contact Information:

ITB Journal Publisher, LPPM – ITB, 

Center for Research and Community Services (CRCS) Building Floor 7th, 
Jl. Ganesha No. 10 Bandung 40132, Indonesia,

Tel. +62-22-86010080,

Fax.: +62-22-86010051;

e-mail: jictra@lppm.itb.ac.id.