USING BIG DATA IN INTERNAL FRAUD DETECTION

Year 2021, Volume: 3 Issue: 1, 55 - 82, 31.07.2021

Teoman Samet TEMUÇİN Sefa ERBAŞ Anıl AY

Abstract

Internal frauds are one of the most important operational risks threating entities. In addition to significant operational loss, they also cause reputational and prestige loss. Therefore, in addition to preventive proactive controls, the existence of deterrent practices to quickly detect them is of great importance. In this paper, we will tell transformation story of Garanti BBVA Internal Audit Department regarding the detection of internal frauds made through the use of big data capabilities. We will talk about how the previous detection method called as “scenario-based” has been converted into the new detection approach called as “rule-based” with the more effective use of big data capabilities. This new detection method has allowed provision of assurance to a higher number of risky transactions with the same resources, achievement of a significant increase rate in the detection of internal frauds and decrease in the loss incurred due to internal frauds. We hope that this new methodology which has proven its success in the fields of efficiency and effectiveness will also be a source of inspiration for the sector.

Keywords

internal fraud, fraud detection, big data

References

• Association of Certified Fraud Examiners (ACFE), Institute of Internal Auditors, & American Institute of Certified Public Accountants. (2008). Managing the Business Risk of Fraud: A Practical Guide. Association of Certified Fraud Examiners.
• Association of Certified Fraud Examiners (ACFE). (2020) Report to the Nations: 2020 Global Study on Occupational Fraud and Abuse. Retrieved from https://www.acfe.com/report-to-the-nations/2020/
• Association of Government Accountants (AGA). (n.d.). The Fraud Triangle. Retrieved from https://www.agacgfm.org/Intergov/Fraud-Prevention/Fraud-Awareness-Mitigation/Fraud-Triangle.aspx
• Babuşcu, S., Hazar, A., & Iskender, A. (2018). Banka Risk Yönetimi: Basel I - II - III - IV Düzenlemeleri. Bankacılık Akademisi Yayınları.
• Baesens, B., Vlasselaer, V., & Verbeke, W. (2015). Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection. Wiley Publishing.
• Baesens, B., Höppner, S., & Verdonck, T. (2021). Data Engineering for Fraud Detection, Decision Support Systems, 113492.
• Becker, R., Volinsky, C., & Wilks, A. (2010). Fraud Detection in Telecommunications: History and Lessons Learned. Technometrics. 52(1), 20-33.
• Bolton, R. & Hand, D. (2002). Statistical Fraud Detection: A Review, Statistical Science. 17(3), 235-255.
• Cressey, D. R. (1953). Other People's Money: A Study in the Social Psychology of Embezzlement. Free Press.
• Fawcett, T. & Provost, F. (1997). Adaptive Fraud Detection, Data Mining and Knowledge Discovery, 1(3), 291-316.
• Ge, D., Gu, J., Chang, S., & Cai, J. (2020). Credit Card Fraud Detection Using Lightgbm Model, International Conference on E-Commerce and Internet Technology, 232-236.
• International Association of Insurance Supervisors (IAIS). (2011, September 28). Application Paper on Deterring, Preventing, Detecting, Reporting and Remedying Fraud in Insurance. Retrieved from https://iaisweb.org/file/34108/application-paper-on-fraud-in-insurance
• Kolodiziev, O., Mints, A., Sidelov, P., Pleskun, I., & Lozynska, O. (2020). Automatic Machine Learning Algorithms for Fraud Detection in Digital Payment Systems. Eastern-European Journal of Enterprise Technologies, 5(107), 14–26.
• Rosset, S., Murad, U., Neumann, E., Idan, Y, & Pinkas, G. (1999). Discovery of Fraud Rules for Telecommunications - Challenges and Solutions, Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, NY: ACM Press, 409-413.
• Shirgave, S. K., Awati, C. J., More, R., & Patil, S. S. (2019). A Review on Credit Card Fraud Detection Using Machine Learning, International Journal of Scientific and Technology Research, 8(10), 1217-1220.
• Soviany, C. (2018). The Benefits of Using Artificial Intelligence in Payment Fraud Detection: A Case Study, Journal of Payments Strategy and Systems, 12(2), 102-110.
• The Institute of Internal Auditors (IIA), (2017, January 1). International Standards for the Professional Practice of Internal Auditing (Standards). Retrieved from https://na.theiia.org/standards-guidance/mandatory-guidance/Pages/Standards.aspx
• The Institute of Internal Auditors (IIA), (2019, January). Fraud and Internal Audit: Assurance Over Fraud Controls Fundamental to Success. Retrieved from https://na.theiia.org/about-ia/PublicDocuments/Fraud-and-Internal-Audit.pdf
• Wei, Y., Qi, Y., Ma Q., Liu Z., Shen C., & Fang C. (2020). Fraud Detection by Machine Learning, 2nd International Conference on Machine Learning, Big Data and Business Intelligence, 101-115.
• Wolfe, D. & Hermanson, D. (2004). The Fraud Diamond: Considering the Four Elements of Fraud. The CPA Journal 74.12, 38-42.