Comprehensive Classification of Credit Scoring Models

Subject Areas : business management

Pardis Fooladi ¹ , Mohsen Amini Khozani ^{2 *} , Zohreh Hajiha ³ , Shadi Shahverdiani ⁴

1 - PhD student, Department of Financial Engineering, Faculty of Humanities, Shahr-e- Qods Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor, Department of Financial Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran
3 - Associate Professor, Accounting Department, East Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Assistant Professor, Department of Financial Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

Received: 2024-07-24 Accepted : 2024-11-30 Published : 2025-02-24

Keywords: Credit scoring, statistical analysis, regression, machine learning, neural networks.,

Abstract :

Credit scoring models have evolved significantly over time, incorporating various techniques to assess the creditworthiness of individuals and businesses. Credit scoring is an important process for credit providers because it determines the probability of default or non-default of applicants and plays an important role in determining people's ability to receive facilities and credit. Traditional credit scoring models, such as those that incorporate the credit history and repayment behavior of borrowers, have been of interest in credit risk management for decades. These models help decision-making processes by evaluating credit, influencing loan approvals, and shaping preventive measures in case of default. Using emerging machine learning frameworks and approaches and alternative data sources, credit scoring methods will become more successful over time in optimizing risk management, improving loan approval processes, reducing investment risk for lenders, and effectively addressing the financial needs of borrowers. In this research, a comprehensive classification of credit scoring models has been presented using bibliometric citation analysis and includes more than 100 cited articles.

References:

1) البرزی،محمود؛ پورزرندي،محمدابراهيم؛ خان‌بابايي،محمد. (1389). به‌كارگيري الگوريتم ژنتيك در بهينه‌سازي درختان تصميم‌گيري براي اعتبارسنجي مشتريان بانك‌ها. نشريه مديريت فناوري اطلاعات، دوره 2، 38-23.
2) راعی، رضا؛ سروش، ابوذر. (1391). اعتبارسنجی مشتریان حقوقی کوچک و متوسط بانک‌ها با استفاده از مدل‌های لوجیت و پروبیت. پژوهشنامه اقتصاد، دوره 12، شماره 44، پیاپی 1، 145-131.
3) رجبی پورمیبدی، علیرضا؛ لگزیان، محمد؛ فصاحت، جعفر. (1392). مطالعه تاثیر نوع صنعت بر معیارهای اعتباردهی به مشتریان حقوقی بانک صادرات بااستفاده از DEA. مدیریت تولیدوعملیات، دوره 4، 144-129.
4) Alinezhad, A., & Kashanifar, S. (2018). Customer credit scoring using data envelopment analysis and discriminant analysis in a fuzzy environment (case study: a leasing company affiliated with a private bank). Iranian Journal of Insurance Research, 8(1), 27-40.
5) Aren, S. and Nayman Hamamcı, H. (2022). The impact of financial defence mechanisms and phantasy on risky investment intention. Kybernetes, Vol. 51 No. 1, 141-164.
6) Bahiraie, A. and Arshadi, A. (2012) Bankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach. Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 6(4): 101-132.
7) Black, F. and Scholes, M. (1973). The Pricing of Options and Corporate Liabilities, Journal of Political Economy 81: 637–659.
8)Bücker, M., et al. (2021). Transparency, auditability, and explainability of machine learning models in credit scoring. Journal of the Operational Research Society, 73(1), 70–90.
9) Chai, N., et al (2019). A Multicriteria Approach for Modeling Small Enterprise Credit Rating: Evidence from China. Emerging Markets Finance and Trade, Taylor and Francis Journals, vol. 55(11): 2523-2543.
10) Chang, C. C., et al. (2023). The relationship between sovereign credit rating changes and firm risk. Heliyon, 9(10).
11) Chen, K., et al. (2020). Credit Fraud Detection Based on Hybrid Credit Scoring Model, Procedia Computer Science, Volume 167: 2-8.
13) Dastile, X., Celik, T. & Potsane, M. (2020). Statistical and machine learning models in credit scoring: A systematic literature survey, Applied Soft Computing, V. 91, 106263.
15) Dumitrescu, E, et al. (2022). Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects. European Journal of Operational Research, V. 297, Issue 3: 1178-1192.
17) Goh, R. Y., et al. (2020). Hybrid harmony search–artificial intelligence models in credit scoring. Entropy, 22(9), 989.‏
18) Hayashi Y. (2022). Emerging Trends in Deep Learning for Credit Scoring: A Review. Electronics. 11(19):3181.
20) Jadwal, P.K., Pathak, S. & Jain, S. (2022). Analysis of clustering algorithms for credit risk evaluation using multiple correspondence analysis. Microsyst Technol 28, 2715–2721.
21) Johanna Garzon Rozo, B., Crook, J. & Andreeva, G. (2023). The role of web browsing in credit risk prediction Credit Research Centre. Decision Support Systems 164, 113879.
22) Kumar Trivedi, S. (2020). A study on credit scoring modeling with different feature selection and machine learning approaches, Technology in Society, V. 63.
23) Li, Y. & Chen, W. A. (2020). Comparative Performance Assessment of Ensemble Learning for Credit Scoring. Mathematics. 8(10):1756.
24) Lis, S., et al. (2023). Analyzing Credit Risk Model Problems through NLP-Based Clustering and Machine Learning: Insights from Validation Reports. arXiv preprint arXiv:2306.01618.‏
25) Md Jahidur, R. & Hongtao, Z. (2024). Predicting financial distress using machine learning approaches: Evidence China, Journal of Contemporary Accounting and Economics, 100403, 20, 1, (100403).
26) Moscato, V., Picariello, A. & Sperlí, G. (2021). A benchmark of machine learning approaches for credit score prediction. Expert Systems with Applications, V. 165, 113986.
28) Roy, P. K. and Shaw, K. (2021). A multicriteria credit scoring model for SMEs using hybrid BWM and TOPSIS. Financ Innov 7, 77.
29) Smirnov, V.S. & Stupnikov, S.A. (2023). Deep Learning Approach to Credit Scoring Using Credit History Data. Lobachevskii J Math 44, 198–204.
30) Sukharev, I. (2021). et al. EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data, 2020 IEEE International Con. on Data Mining.
31) Saunders, A., Cornett, M.M. & Erhemjamts, O. (2021). Financial institutions management: A risk management approach. McGraw-Hill.‏ 32) Wang, C. & Zhuoyi X. (2022). A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer. Applied Sciences 12, no. 21: 10995. 33) Xia, Y., et al. (2021). A dynamic credit scoring model based on survival gradient boosting decision tree. Technological and Economic Development of Economy, 27(1), 96-119.‏
34) Xia Zhang, E. (2019) The impact of cash flow management versus accruals management on credit rating performance and usage. Review of Quantitative Finance and Accounting.
35) Xia, Y., et al (2023). Predicting Chain’s Manufacturing SME Credit Risk in Supply Chain Finance Based on Machine Learning Methods. Sustainability, 15(2).
36) Xu, J., and Liu, F. (2020). The Impact of Intellectual Capital on Firm Performance: A Modified and Extended VAIC Model. Journal of Competitiveness, 12(1), 161–176.
37) Xu, Z., et al. (2022). Government Support's signaling effect on credit financing for new-energy enterprises. Energy Policy, 164, 112921.
38) Yousofi Tezerjan, M. & Safi, A. (2021). Azizollah Memariani, ARF: A hybrid model for credit scoring in complex systems, Expert Systems with Applications, V. 185, 115634.
39) Zou, Y. & Changchun G. (2022). Extreme Learning Machine Enhanced Gradient Boosting for Credit Scoring. Algorithms 15, no. 5: 149.