On predicting the probability of default of credit organizations in the Russian Federation

Objective: to form a model for predicting the default of credit organizations under the modern conditions of the banking sector functioning.
Methods: unidimensional analysis of variance, regression analysis of binary choice models.
Results: in the modern economy, the banking system stability largely affects not only the financial sector, but also the economic and investment climate in the country. Understanding of the banks’ influence on the economy necessitates the formation of appropriate effective forecasting systems that allow identifying problem banks before revoking their licenses is necessary. The existing methodology of the Bank of Russia is characterized by subjectivity and inaccuracy of assessment. The analysis of studies on predicting bank defaults showed various approaches to the methodology of assessing the probability of credit institutions’ bankruptcy, though they have a number of shortcomings. Based on the selection of key factors affecting the bank’s financial stability, the logistic regression model for predicting bankruptcy of banks was formed. The methodology proposed in this article includes five predictors, selected on the basis of the improved methodology for selecting logit regression variables, and complements the existing methodologies.
Scientific novelty: a methodology for assessing the probability of commercial banks’ bankruptcy in the Russian Federation was developed, which includes five key predictors for assessing the bank’s financial stability: return on assets, unit weight of liquid assets in the balance sheet currency, unit weight of the loan portfolio in the balance sheet currency, share of loans to the real sector in the balance sheet currency, and share of long-term placements in the loan portfolio. The logistic regression model of binary choice proposed in the paper allows distinguishing financially stable credit organizations from problem banks with a forecasting horizon of five months and a classification accuracy of 88,33 %.
Practical significance: the relatively high classification accuracy of the model allows its use by the Bank of Russia in controlling the credit organizations functioning, as well as directly by the credit organization’s management, in order to assess the organization’s financial stability and to predict the default probability, as well as to form the bank’s development strategy.

1. Ahangar, R. G., & Ozturk, C. (Eds.). (2019). Accounting and Finance – New perspectives on banking, financial statements and reporting. IntechOpen. https://doi.org/10.5772/intechopen.79593

2. Al'-Saadi, M. R. (2021). Elaboration of models for predicting financial status and evaluating the bankruptcy probability of credit organizations. In Innovative potential of science development in the modern world: collection of scientific articles by the works of the 5th International scientific-practical conference (pp. 97–102). Ufa. (In Russ.).

3. Bella, F., & Radianto, W. (2021). Bankruptcy prediction of the banking sector using bankometer: comparative study based on company size. In Proceedings of the 2nd International Conference on Business and Management of Technology (ICONBMT 2020). https://doi.org/10.2991/aebmr.k.210510.051

4. Bogdanov, V. V., & Zamyatina, A. A. (2020). A mathematical model for assessing the probability of default of credit organizations in Russia. Economics: Yesterday, Today and Tomorrow, 10(4А), 121–131. (In Russ.).

5. Burova, I. V. (2021). Building a mathematical model of probability bankruptcy of credit institutions on the basis of binary logistic regression. Regionalnye problemy preobrazovaniya ehkonomiki, 3(125), 123–129. (In Russ.). https://doi.org/10.26726/1812-7096-2021-3-123-129

6. Calabrese, R., & Giudici, P. (2013). Estimating bank default with generalised extreme value models. DEM Working Paper Series, 35. http://economiaweb.unipv.it/wp-content/uploads/2017/06/DEMWP0035.pdf

7. Chelyshev, D. S. (2019). Modelling of Russian banks’ probability of default. Business. Education. Law, 2(47), pp. 262–266. (In Russ.).

8. Davnis, V. V. (2005). Prognostic models of expert preferences: monograph. Voronezh: Voronezh State University. (In Russ.).

9. Doronin, B. A., & Urudzhova, L. B. (2019). Elaboration of a new methodology for predicting financial insolvency of a bank. Akademicheskaya publitsistika, 11, 97–99. (In Russ.).

10. Emelyanov, A. M., & Bryukhova, O. O. (2013). Estimating the probability of bankruptcy. Finance and Credit, 27(555), 47–58. (In Russ.).

11. Erimizina, M. I., & Erimizina, E. N. (2017). Methodological bases for estimating the probability of bankruptcy. Symbol of Science, 04-1, 94–100 (In Russ.).

12. Garcia-Almanza, A. L., Alexandrova-Kabadjova, B., & Martinez-Jaramillo, S. (2022). Bankruptcy prediction for banks: an artificial to improve understandability. In Artificial Intelligence, Evolutionary Computing and Metaheuristics (Vol. 427, pp. 633–656).

13. Golovan, S. A., Evdokimov, A. M., Karminskii, A. M., & Peresetskii, A. A. (2003). Models of the probability of default of the Russian banks. Preliminary division of the banks into clusters. Moscow: New Economic School. (In Russ.).

14. Golovan, S. V., Evdokimov, M. A., Karminskii, A. M., & Peresetskii, A. A. (2004). Models of the probability of default of the Russian banks. II. Influence of macroeconomic factors on the banks’ stability. Preprint# WP/2004/043 R. Moscow: New Economic School. (In Russ.).

15. Ishak, A. A., & Mongid, A. (2020). Determinants of bankruptcy banking after the global financial crisis (GFC): theoretical review. IPTEK Journal of Proceedings Series, 1, 243–248. http://dx.doi.org/10.12962/j23546026.y2020i1.7858

16. Ivanov, V. V., & Fedorova, Yu. I. (2015). Problems of selecting indicators for estimating a default of a credit organization. In Theoretical and practical aspects of modern science development: works of the 15th International scientific-practical conference (pp. 83–89). Nauchno-informatsionnyi izdatel'skii tsentr “Institut strategicheskikh issledovanii” (In Russ.).

17. Jo, K. Choi, G., Jeong, J., & Ahn, K. (2022). Forecasting bank default with the Merton Model: the case of US bank. In International Conference in Computational Science – ICCS 2022. 15 June 2022 (pp. 682–691). https://doi.org/10.1007/978-3-031-08751-6_49

18. Kahn, C., & Papanikolaou, N. (2013, April). What problem banks reveal about future financial distress: Evidence from the late 2000s financial crisis. SSRN Electronic Journal. https://www.researchgate.net/publication/228202404_What_Problem_Banks_Reveal_About_Future_Financial_Distress_Evidence_from_the_Late_2000s_Financial_Crisis

19. Kalitskaya, V. V., Rykalina, O. A., & Balandina, Ya. A. (2022). Methodologies for assessing the probability of bankruptcy and inconvention of credit organizations and enterprises. StudNet, 6, 6609–6620. (In Russ.).

20. Kaminskii, A. M., Kostrov, A. V., & Murzenkov, T. N. (2012). Modeling the probability of default of the Russian banks with econometric methods. Moscow. (In Russ.).

21. Kasyanova, M. A., Pekarskaya, O. A., & Saitgareeva, L. F. (2020). On predicting a bankruptcy of a credit organization. Uspekhi sovremennoi nauki i obrazovaniya, 4(5), 26–31. (In Russ.).

22. Klaas, Ya. A., & Klaas, T. A. (2018). Identification of Risk Factors of Bankruptcy of Credit Institutions and Their Modelling. Finance and Credit, 24(1), 19–32. (In Russ.). https://doi.org/10.24891/fc.24.1.19

23. Kolari, J., Glennon, D., Shin, H. & Caputo, M. (2020, January). Predicting large U.S. Commercial Failures. OCC Economics Working Paper.

24. Kosorukova, I., & Bratanov, A. (2021). Assessment of the bank’s financial stability based on a risk-based approach. Theoretical and practical aspects of Management, 2, 117–137. (In Russ.). https://doi.org/10.46486/0234-4505-2021-2-117-137

25. Lanine, G., & Vennet, R. (2006). Failurej8. https://doi.org/10.1016/j.eswa.2005.10.014

26. Macedo, S., & Jose, M. (2019). Bankruptcy prediction models for banks using camel factors. Tilburg University.

27. Martin, D. (1977). Early Warning of Bank Failure: A Logit Regression Approach. Journal of Banking & Finance, 1(3), 249–276.

28. Martynenko, N. N., & Tereshchenko, L. G (2019). Bankruptcy diagnostics commercial banks: refinement of the settlement mechanism. Financial Markets and Banks, 1, 45–49. (In Russ.).

29. Matsulevich, R. M. (2014). Methods of predicting the probability of a bankruptcy of a credit organization. Mezhdunarodnyi nauchno-issledovatel'skii zhurnal, 3-3(22), 48–50. (In Russ.)

30. Nagel, S., & Purnanandam, A. (2020). Banks’ risk dynamics and distance to default. The Review of Financial Studies, 33(6), 2421–2467. https://doi.org/10.1093/rfs/hhz125

31. Oreshko, I. I., & Savina, I. A. (2019). Estimating the probability of bankruptcy of the Russian banks. Economica i upravlenie v XXI veke: tendentsii i razvitie, 26, 139–142. (In Russ.).

32. Peresetskii, A. A. (2013). Models of reasons for retrieving licenses of the Russian banks. Impact of unaccounted factors. Applied Econometrics, 2(30), 49–64. (In Russ.).

33. Radionova, M. V., & Pristupina, Yu. V. (2019). Modeling the probability of default of Russian banks. Finance and Credit, 6(786), 1336–1352. (In Russ.). https://doi.org/10.24891/fc.25.6.1336

34. Salakhetdinova, G. A. (2021). Theoretical studies on the tools for assessing the bankruptcy of credit institutions. Audit Journal, 3, 134–137. (In Russ.).

35. Shuba, N. A. (2022). Probability of default of a credit institution: signal indicators of insolvency and their assessment. Nauchnyi al'manakh Tsentral'nogo Chernozem'ya, 1-2, 237–240. (In Russ.).

36. Shumway, T. (2001). Forecasting bankruptcy more accurately: a simple hazard model. The Journal of Business, 74(1), 101–124. https://doi.org/10.1086/209665

37. Sinelnikova-Muryleva, E. V., Gorshkova, T. G., & Makeeva, N. V. (2018). Default Forecasting in the Russian Banking Sector. Economic Policy, 13(2), 8–27. (In Russ.). https://doi.org/10.18288/1994-5124-2018-2-01

38. Solovyeva, N. N. (2020). Predicting the bankruptcy of a commercial bank in the system of financial risks management. In: Economics in theory and practice: topical issues and modern approaches: collection of works of the 4th International scientific-practical conference (pp. 213–215). Penza. (In Russ.).

39. Vinokurova, T. G. (2019). Model of predicting defaults in the banking sector. In Sayapinskiye chteniya: collection of works of the 2nd All-Russia (national) scientific-practical conference (pp. 45–50). (In Russ.).

40. Yashina, N. I., Makarova, S. D., Makarov, I. A., & Otdelkina, A. A. (2017). Forecasting the Commercial Bank Default Based on a Probabilistic Model. Economic Analysis: Theory and Practice, 16(12), 2376–2391. (In Russ.). https://doi.org/10.24891/ea.16.12.2376

41. Zakirova, D. F., & Vafin, I. A. (2018). Study of factors influencing bankruptcy probability of credit organizations. Kazanskii Ehkonomicheskii Vestnik, 1(33), 59–63. (In Russ.).

42. Zakirova, D. F., Panteleev, D. S., & Zakirova, E. F. (2018). Estimating bankruptcy probability of credit organizations. International Transaction Journal of Engineering Management & Applied Sciences & Technologies, 9(4), 307–315.

43. Zakirova, D. F., Zakirova, E. F., & Potapova, E. A. (2019). Formation of a method for determination of a commercial credit bank rating. International Journal of Emerging Technologies, 10(2a), 129–132.

44. Zhao, H., Sinha, A., & Ge, W. (2009). Effects of feature construction on classification performance: An empirical study in bank failure prediction. Expert Systems with Applications, 36, 2633–2644. https://doi.org/10.1016/j.eswa.2008.01.053

45. Zubarev, A., & Bekirova, O. (2020). Analysis of Bank Default Factors in 2013–2019. Economic Policy, 15(3), 106–133. https:// doi.org/10.18288/1994-5124-2020-3-106-133