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• 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.