Karar Ağacı Destekli Hile Tespiti ve Bir Uygulama

Yıl 2023, Cilt: 7 Sayı: 1, 511 - 528, 31.01.2023

Önder Gür

https://doi.org/10.29023/alanyaakademik.1196078

Cited By: 1

Öz

Çalışmada, Sertifikalı Hile Denetçileri Birliği’nin (ACFE) hile ağacında yer alan ve işletmelerde sıklıkla karşılaşılan hileli ödemelerin verdiği zararı azaltmak için makine öğrenmesi yönteminin kullanıldığı bir uygulama ile hile tespit sürecine katkının sağlanması amaçlanmıştır. Bu amaçla, elde edilmek istenen çıktılar için Python’da bir uygulama sistemi tasarlanmıştır. Çalışmada, bir bankaya ait normal işlemler ile hileli işlemlerin yer aldığı yapay veri setinden yararlanılmıştır. Yöntem olarak kullanılmasına karar verilen Karar Ağacı tekniğiyle önce sınıf etiketleri bilinen bir veri setiyle ana model oluşturulmuş, sonra etiketsiz bir veri seti üzerinde modelin test edilmesi sağlanmıştır. Karar ağacı tekniğinin modeli, %97,1 doğruluk, %98,4 f1-skor, %98,9 kesinlik ve %98 duyarlılık değerlerini elde etmiştir. Çalışma, karar ağacı tekniğinin tahmin aşamasında ürettiği hatalı sınıf etiketlerinin azaltılması açısından iyileştirmeye açık olup, diğer tekniklerle karşılaştırılarak da geliştirilebilir.

Anahtar Kelimeler

Hile Tespiti, Hileli Ödemeler, Makine Öğrenmesi, Kara Ağacı

Decision Tree Supported Fraud Detection and an Application

Öz

In the study, it is aimed to contribute to the fraud detection process with an application in which machine learning method is used to reduce the damage caused by fraudulent disbursements, which is included in the fraud tree of the Association of Certified Fraud Examiners (ACFE). To achieve the desired outcomes, a Python application system is developed for this purpose. In the study, an artificial data set containing normal transactions and fraudulent transactions of a bank, was used. Using the Decision Tree technique, which was selected as the chosen method, the main model was developed using a data set with known class labels, and then the model was evaluated using unlabeled data. The model of the decision tree technique achieved 97,1% accuracy, 98,4% f1-score, 98,9% precision and 98% sensitivity. The study is open to improvement in terms of reducing the erroneous class labels produced by the decision tree technique during the estimation phase and can be improved by comparing it with other techniques.

Anahtar Kelimeler

Fraud Dedection, Fraudulent, Disbursements, Machine Learning, Decision Tree

Kaynakça

• ACFE, Association of Certified Fraud Examiners, “2022 Global Study on Occupational Fraud and Abuse”, Report to The Nation, https://acfepublic.s3.us-west-2.amazonaws.com/2022+Report+to+the+Nations.pdf , 23.10.2022
• AKPINAR, H. (2017). Data, Veri Madenciliği Veri Analizi. 2.Baskı. Papatya Yayıncılık Eğitim, İstanbul.
• AKSOY, B. (2021).“Finansal Tablo Hilelerinin Makine Öğrenmesi Yöntemleri ve Lojistik Regresyon Kullanılarak Tahmin Edilmesi: Borsa İstanbul Örneği”, Maliye ve Finans Yazıları, 115:29-60.
• ALBRECHT, W.S., ALBRECHT, C.O., ALBRECHT, C.C., & ZIMBELMAN M. F. (2011). Fraud Examination. Fourth Edition. South-Western, United States.
• ATA, H.A., & SEYREK, İ.H. (2009). “The use of data mining techniques in detecting fraudulent fınancial statements: an applicatıon on manufacturing firms”, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 14(2):157-170.
• BHATIA, P. (2019). Data Mining and Data Warehousing Principles and Practical Techniques, First Published. Cambridge University Press, United Kingdom.
• BOZKURT, N. (2009). İşletmelerin Kara Deliği Hile. 3.Baskı. Alfa Yayınları, İstanbul.
• CHEN, S. (2016). “Detection of fraudulent financial statements using the hybrid data mining approach”, SpringerPlus, 5(89):1-16.
• CODERRE, D. (2009). Computer-Aided Fraud Prevention and Detection: A Step-by-Step Guide. Wiley & Sons, New Jersey.
• CRAJA, P., KIM, A., & LESSMANN, S. (2020). “Deep learning for detecting financial statement fraud”, Decision Support Systems, 139:1-13.
• DUTTA, I., DUTTA, S., & RAAHEMI, B. (2017). “Detecting Financial Restatements Using Data Mining Techniques”, Expert Systems With Applications, 90:374-393.
• FERNANDEZ, G. (2003). Data Mining Using SAS Applications. Chapman & Hall/ CRC Press, United States.
• GAGANIS, C. (2009). “Classifıcation Techniques for the Identification of Falsified Financial Statements: A Comparative Analysis”, Intelligent Systems In Accounting, Finance And Management, 16:207–229.
• GOLDEN, T.W., SKALAK, S.L., CLAYTON, M. M., & PILL, J. S. (2011). A Guide to Forensic Accounting Investigation. Second Edition. John Wiley & Sons, New Jersey.
• HACIHASANOGLU, T., ASLAN, T., & DALKILIC, E. (2021). Hile: İşletme Bilimi Perspektifinden Genel Bir Bakış. Paradigma Akademi, Çanakkale.
• JAN, C. (2018). “An effective financial statements fraud detection model for the sustainable development of financial markets: evidence from Taiwan”, Sustainability, 10(513):1-14.
• KAGGLE, Synthetic Data From A Financial Payment System, 2017, https://www.kaggle.com/ealaxi/banksim1, 16.02.2022
• KANTARDZIC, M. (2020). Data Mining: Concepts, Models, Methods, and Algorithms. Third Edition. John Wiley & Sons, New Jersey.
• KIRLIOĞLU, H., & CEYHAN, I.F. (2014). “Mali Tablo Denetiminde Ön Analitik İnceleme Tekniği Olarak Veri Madenciliğinin Kullanımı: Borsa İstanbul Uygulaması”, Akademik Yaklaşımlar Dergisi, 5(1):13-36.
• KIRKOS, E., SPATHIS, C., & MANOLOPOULOS, Y. (2007). “Data mining techniques for the detection of fraudulent financial statements”, Expert Systems with Applications, 32:995-1003.
• KOTU, V., & DESHPANDE, B. (2019). Data Science Concepts and Practice. Second Edition. Morgan Kaufmann Publishers, United States.
• KUDYBA, S. (2014). Big Data, Mining, and Analytics: Components of Strategic Decision Making. CRC Press, Taylor & Francis Group, Florida.
• KURIEN, K.L., & CHIKKAMANNUR, A.A. (2019). “Benford’s Law and Deep Learning Autoencoders: An approach for Fraud Detection of Credit card Transactions in Social Media”, 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology, India: IEEE, 1030-1035.
• LÆGREID, I. (2007).“Automatic Fraud Detection — Does it Work?”, Annals of Actuarial Science, 2(2):271–288.
• LAKSHMI, S.V.S.S., & KAVILA, S.D. (2018). “Machine Learning For Credit Card Fraud Detection System”, International Journal of Applied Engineering Research, 13(4):16819-16824.
• LAYTON, R. (2015). Learning Data Mining with Python. First Edition. Packt Publishing Ltd., Birmingham.
• LIOU, F.M. (2008). “Fraudulent Financial Reporting Detection and Business Failure Prediction Models: A Comparison”, Managerial Auditing Journal, 23(7):650-662.
• MEMIS, S., ENGINOGLU, S., & ERKAN, U. (2019). “A Data Classification Method in Machine Learning Based on Normalised Hamming Pseudo-Similarity of Fuzzy Parameterized Fuzzy Soft Matrices”, Bilge International Journal of Science and Technology Research, Özel Sayı (3):1-8.
• NGAI, E.W.T., HU, Y., WONG, Y.H., CHEN, Y., & SUN, X. (2011). “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literatüre”, Decision Support Systems, 50:559–569.
• NISBET, R., MINER, G., & YALE, K. (2018). Handbook Of Statistical Analysis And Data Mining Applications. Second Edition. Academic Press – Elsevier, London.
• OLSON, D. L. (2008). DELEN, D. Advanced Data Mining Techniques. Springer-Verlag, Berlin.
• PYPL, Programlama Dilleri Popülerlik İndeksi, https://pypl.github.io/PYPL.html, 15.09.2022
• SHMUELI, G., BRUCE, P.C., STEPHENS, M.L., & PATEL, N.R. (2017). Data Mining For Business Analytics: Concepts, Techniques, And Applications With JMP Pro. First Edition. John Wiley & Sons, New Jersey.
• SINGLETON, T.W., & SINGLETON, A.J. (2010). Fraud Auditing and Forensic Accounting. Fourth Edition. John Wiley & Sons, New Jersey.
• TATAR, B., & KIYMIK, H. (2021). “Finansal Tablolarda Hile Riskinin Tespit Edilmesinde Veri Madenciliği Yöntemlerinin Kullanılmasına Yönelik Bir Araştırma”, Journal of Yasar University, 16(64):1700-1719.
• WEST, J., & BHATTACHARYA, M. (2016). “Intelligent Financial Fraud Detection: A Comprehensive Review”, Computers & Security, 57:47-66.
• ZHOU, W., & KAPOOR, G. (2011). “Detecting evolutionary financial statement fraud” Decision Support Systems, 50:570–575.