SCORING CARDS FOR DIFFERENT TYPES OF CREDIT PRODUCTS

The development of credit scoring is one of the key topics of attention in credit risk management in financial companies. However, a single approach to produce rating cards is frequently worthless since loan products differ in risk and financing time and often there is insufficient information on borrowers. The paper addresses the features of creating score cards for consumer credit, refinancing, small and medium businesses, auto loans, mortgage loans, fintech and P2P lending. Thus, the present work can be considered as the above comparative analysis of the most important elements influencing the probability of default of the borrower in the settlement by segments, together with the consideration of machine learning techniques and the use of alternative data sources that can improve the accuracy of the forecast. Depending on the usual credit product, the analysis lets one create recommendations for choosing the optimal approach of creating scoring cards, so enhancing the accuracy of the borrower’s creditworthiness projection and reducing the degree of default risk.

1. Addo, P.M., Guegan, D., & Hassani, B. Credit risk analysis using machine and deep learning models. Risks, 6 (2), 38 (2018). https://doi.org/10.3390/risks6020038.

2. Brown, I., & Mues, C. An experimental comparison of classification algorithms for imbalanced credit scoring datasets. Expert Systems with Applications, 39 (3), 3446–3453 (2012). https://doi.org/10.1016/j.eswa.2011.09.033.

3. Grier, D.A. Credit Scoring and Credit Control. Oxford University Press (2012).

4. Henley, W.E., & Hand, D.J. A k-nearest-neighbour classifier for assessing consumer credit risk. The Statistician, 45 (1), 77–95 (1996). https://doi.org/10.2307/2988486.

5. Munkhdalai, L., Namsrai, O.B., Lee, J.Y., & Ryu, K.H. An empirical comparison of machine-learning methods on bank client credit assessments. Sustainability, 11 (3), 699 (2019). https://doi.org/10.3390/su11030699.

6. Oguz, K., Ugur, O., & Kestel, A.S. The impact of feature selection and transformation on machine learning methods in determining credit scoring. (2023). arXiv preprint, arXiv:2303.05427. Retrieved from https://arxiv.org/abs/2303.05427.

7. Tatarintsev, M.A., Nikitin, P.V., Gorokhova, R.I., & Dolgov, V.I. Comparative analysis of machine learning technologies for credit scoring tasks. Fundamental Research, 2, 7–12 (2022). https://doi.org/10.17513/fr.43419.

8. XGBoost Documentation. (n.d.). Random Forests™ in XGBoost. Retrieved from https://xgboost.readthedocs.io/en/stable/tutorials/rf.html.

9. Kadiev, A.D., & Chibisova, A.V. Neural network methods for credit scoring. Mathematical Modeling and Numerical Methods, 4, 81–92 (2022). URL: https://mmcm.bmstu.ru/articles/download_article/295/.

10. Hu, L., Chen, D., Wong, J., Yang, H., Wang, K., Sudjianto, A., & Nair, V. N. (2020). Supervised machine learning techniques: An overview with applications to banking. arXiv preprint, arXiv:2008.04059. URL: https://arxiv.org/abs/2008.04059.

11. Shukhardin, A.V. (2024). Development of a credit scoring system based on machine learning models. Ural Federal University Electronic Archive. URL: https://elar.urfu.ru/bitstream/10995/140553/1/m_th_a.v.shukhardin_2024.pdf.

12. Arram, A., Ayob, M., AlBadr, M. A.A., Suleiman, A., & Albashish, D. Credit rating prediction using machine learning models: A new dataset. (2023). arXiv preprint, arXiv:2310.02956. URL: https://arxiv.org/abs/2310.02956.

13. Rastorguev, A.V., & Mikishanina, E.V. Building a scoring model for assessing borrower creditworthiness. Electronic Scientific Journal “Vector of Economics, 5”. (2022). URL: https://www.vectoreconomy.ru/images/publications/2022/5/mathematicalmethods/Rastorguev_Mikishanina.pdf.

14. Dauletkhanuly, Y., & Oyshynova, G. A. The use of machine learning and artificial intelligence by the monetary regulator. Economic Review of the National Bank of the Republic of Kazakhstan, 4 (2023). URL: https://nationalbank.kz/file/download/98995.

15. Central Bank of the Russian Federation. The impact of negative news on public perception of inflation. Working Papers of the Bank of Russia, 111 (2022). URL: https://www.cbr.ru/content/document/file/144918/wp_111.pdf. [in Russian].

16. Biecek, P., Chlebus, M., Gajda, J., Gosiewska, A., Kozak, A., Ogonowski, D., Sztachelski, J., & Wojewnik, P. (2023). Enabling machine learning algorithms for credit scoring: Explainable artificial intelligence (XAI) methods for clear understanding complex predictive models. arXiv preprint, arXiv:2310.02956. URL: https://arxiv.org/abs/2310.02956.