Kredi Temerrüt Riskini Tahmin Etmede Makine Öğrenme Algoritmalarının Karşılaştırılması

Year 2023, Issue: 50, 14 - 22, 30.04.2023

Toprak Enes Tütüncü , Sevda Gürsakal

https://doi.org/10.31590/ejosat.1171611

Cited By: 1

Abstract

Bankalar ve çeşitli finans kuruluşları tarafından karşılanan kredilerin, müşteri tarafından geri ödenememesi hem kredi veren kuruluşun sermaye kaybını hem de genel ekonomide oluşabilecek çeşitli risk faktörlerini beraberinde getirmektedir. Bu süreçte, oldukça kritik öneme sahip olan kredi riskinin doğru yönetilebilmesi ve uluslararası finans istikrarının sağlanması için Basel Komitesi ve BDDK (Bankacılık Düzenleme ve Denetleme Kurumu) gibi finans denetimi kuruluşları, kredi veren kurumların kredi verme karar aşamasında çeşitli regülasyon politikaları belirlemektedir. Ayrıca, kredi veren kurumlar analitik risk birimleri aracılığıyla kredi değerlendirme modelleri geliştirerek, müşterilere ait kredi risk skorunu hesaplamaktadır.
Bu çalışmada makine öğrenmesi yöntemiyle kredi skorlama sistemlerinde kullanılabilecek en başarılı tahmini gerçekleştiren algoritmanın belirlenmesi amaçlanmıştır. Bu kapsamda, Gradyan Artırma, Yapay Sinir Ağları, Lojistik Regresyon, Rassal Orman, Karar Ağacı, Destek Vektör Makineleri, K-En Yakın Komşu ve WOE dönüşümleriyle Lojistik Regresyon algoritmaları için modeller kurulmuş ve temerrüde düşen ve temerrüde düşmeyen müşteriler için en iyi sınıflandırma performansı gösteren Gradyan Artırma algoritması olmuştur.

Keywords

Kredi Riski, Makine Öğrenmesi, Gradyan Artırma, Yapay Sinir Ağları, Lojistik Regresyon, Rassal Orman, Karar Ağacı, Destek Vektör Makineleri, K-En Yakın Komşu

Predicting Default Probability In Credit Risk With Machine Learning Algorithms

Abstract

Failure to repay the loans provided by banks and various financial foundations by the customer, entails both the capital loss of the lending institution and various risk factors that may occur in the general economy. In this context, financial control institutions such as the Basel Committee and BRSA (Turkish Banking Regulatory and Supervision Agency) have determined various regulatory policies during the phase of lending decision of the lending institutions in order to ensure the appropriate management of loan risk, which have critical importance, and to ensure international financial stability. In addition, lending institutions develop credit evaluation models via analytical risk units and calculate the credit risk score of customers.
In this study, it is aimed to determine the algorithm that makes the most successful estimation that can be used in credit scoring systems with the machine learning method. Within this scope, models for algorithms with Gradient Boosting, Artificial Neural Networks, Logistic Regression, Random Forest, Decision Tree, Support Vector Machines, K-Nearest Neighbor and WOE transformations Logistic Regression were established and Gradient Boosting algorithm has shown the best classification performance for defaulters and non-defaulters.

Keywords

Credit Risk, Machine Learning, Gradient Boosting, Neural Network, Logistic Regression, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor

References

• Altan, G., & Demirci, S. (2022). Makine Öğrenmesi ile Nakit Akış Tablosu Üzerinden Kredi Skorlaması: XGBoost Yaklaşımı. Journal of Economic Policy Researches, 9(2), 397-424.
• Apostolik, R., Donohue, C.,& Went, P., (2009). Foundations of Banking Risk: An Overview of Banking, Banking Risks, and Risk-Based Banking Regulation, Hoboken, New Jersey: John Wiley & Sons, Inc.
• Barboza, F., Kimura, H., & Altman, E., (2017). Machine learning models and bankruptcy prediction, Expert Systems with Applications 83: 405–417.
• BDDK, (2012), “Bankaların İç Denetim ve Risk Yönetimi Sistemleri Hakkında Yönetmelik”, https://www.resmigazete.gov.tr/eskiler/2012/06/20120628-17.htm (Erişim Tarihi: 24 Haziran 2020).
• Bell, J., (2014), Machine Learning Hands-On for Developers and Technical Professionals, John Wiley & Sons, Inc., Indianapolis, Indiana.
• Bellotti, T., & Crook, J., (2009). Support Vector Machines for Credit Scoring and Discovery of Significant Features, Expert Systems with Applications, 3302–3308.
• Bhargava, A., (Şubat 2000). Credit Risk Management Systems in Banks, ICICI Bank, s.8., www.garp.com / library/Meets/bhargava.pdf, (27.11.2005).
• Breiman, L., (2001). Random Forests, Machine learning, Kluwer Academic Publishers, 45(1), 5-32.
• BROWN, I., & MUES, C., (2012). “An experimental comparison of classification algorithms for imbalanced credit scoring data sets”, Expert Systems with Applications 39: 3446–3453.
• Brown, I., (2014), Developing Credit Risk Models Using SAS Enterprise MinerTM and SAS/STAT: Theory and Applications, Cary, NC: SAS Institute Inc.
• Demirbulut, Y., Aktaş, M., Kalıpsız, O., & Bayracı, S. (2017). İstatistiksel ve Makine Öğrenimi Yöntemleriyle Kredi Skorlama, CEUR-WS (s. 273-284). Antalya: Turkish National Software Engineering Symposium.
• Design I. T., Gabrys B., & Petrakieva L., (2004). Combining labelled and unlabelled data, International Journal on Approximate Reasoning, vol. 35, p. 251-273.
• Eğrioglu, E., Aladağ, C.H., Yolcu, U., Uslu, V.R., & Başaran, M.A., (2009). A new approach based on artificial neural networks for high order multivariate fuzzy time series, Expert Systems with Applications, 36(7), 10589-10594.
• Hamori, S., Kawai, M., Kume, T., Murakami, Y., & Watanabe, C., (2018). Ensemble Learning or Deep Learning? Application to Default Risk Analysis, Journal of Risk and Financial Management 11: 12.
• Hand, D., & Zhou, F., (2009). Evaluating models for classifying customers in retail banking collections, Journal of the Operational Research Society, 61, 1540–1547.
• Jorion, P., (2009). Financial Risk Manager Handbook, Wiley Finance Series, 5. Baskı.
• Kavcıoğlu, Ş. (2019). Kurumsal kredi skorlamasında klasik yöntemlerle yapay sinir ağı karşılaştırması, İstanbul İktisat Dergisi - Istanbul Journal of Economics, 69(2), 207-245.
• Lindholm, A., WAHLSTRÖM, N., Lindsten, F., & SCHÖN, T. B., (2019). Supervised Machine Learning, Version (12 May 2019), s.7 http://www.it.uu.se/edu/course/homepage/sml/literature/lecture_notes.pdf (Erişim Tarihi: 13 Mayıs 2019).
• Mandacı P.E., (2003). Türk Bankacılık Sektörünün Taşıdığı Riskler ve Finansal Krizi Asmada Kullanılan Risk Ölçüm Teknikleri, Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt 5, Sayı:1:67-84.
• Markoff J., (2015). A Learning Advance in Artificial İntelligence Rivals Human Abilities, New York Times, https://www.nytimes.com/2015/12/11/science/an-advance-in-artificial-intelligence-rivals-human-vision-abilities.html (Erişim Tarihi: 22 Mart 2019).
• Raschka, S., (2015). Python Machine Learning, Packt Publishing Ltd., Birmingham, UK.
• The Royal Society, (2017). Machine Learning: The Power And Promise Of Computers That Learn By Example, s.16-21, www.royalsociety.org/machine-learning (Erişim Tarihi: 5 Ocak 2019).
• Tian, Z., Xiao, J., Feng, H., & Wei, Y. (2020). Credit risk assessment based on gradient boosting decision tree. Procedia Computer Science, 174, 150-160.
• Yeh, I. C., & Lien, C., (2009). The Comparisons of Data Mining Techniques for the Predictive Accuracy of Probability of Default of Credit Card Clients, Expert Systems with Applications, Vol.36, 2473-2480.
• Zhang, C., & Zhang, S., (2002). Association Rule Mining - Models and Algorithms, Springer, Berlin.
• Zhang, W., (2017). Machine Learning Approaches to Predicting Company Bankruptcy, Journal of Financial Risk Management 6: 364-374.
• Zhou, L., & Wang, H., (2012). Loan Default Prediction on Large Imbalanced Data Using Random Forests, Telkomnika Indonesian Journal of Electrical Engineering, Vol.10, No.6, October 2012, 1519-1525.
• Zhu, X., & Goldberg, A. B., (2009). Introduction to Semi-Supervised Learning, Morgan & Claypool Publishers.