Improved forecast assessment for the expected credit losses in credit risk monitoring in commercial banks in the context of international and Russian practices
DOI:
https://doi.org/10.17072/1994-9960-2020-3-445-457Abstract
Current banking sector’s performance raises the issues connected with the IFRS 9 Financial Instruments driven transformation of the forecast assessment for the expected credit losses during monitoring and credit risk assessment in commercial banks. In this regard, it becomes important to conduct a comprehensive systematization of the existing Russian and international practices for monitoring and evaluating credit risk in commercial banks. The purpose of the study is to develop a comprehensive approach to the use of an effective model for the impairment of expected losses in banking activities. The novelty of the study includes the enhancement of the tools for the forecast assessment of the expected credit losses among the commercial banks’ clients to improve the credit risk management efficiency. The results from the implementation of IFRS 9 Financial Instruments in the banking area show that modern conditions maintain the uncertainty of the long-term impact of the credit risk on the commercial banks’ performance. What is more, a huge amount of additional information gives significant difficulties, which contributes into the sophisticated calculations of the future credit losses of the banks. It has been justified that a forecast assessment model for the expected credit losses of the clients during the monitoring and bank’s credit risk assessment should be based on the collective or individual ground. The efficient application of the expected losses impairment in the banking performance has been described as a fundamental tool to simulate the expected credit losses to provision for impairment. This model has been shown to be determined by the features of the credit activities and bank portfolio, types of its financial tools, sources of the available information, as well as the applied IT systems. The proposed model validation algorithm for the expected impairment losses could reduce the expected credit losses, decrease the volume of the created assessed reserves, as well as improve the overall commercial bank performance efficiency. Theoretically, the study develops the credit losses risk management in the context of the transformations in the global and Russian banking practices. From the perspective of the practical value, the research gives an opportunity to create an efficient forecast assessment model for the expected credit losses of the commercial banks’ clients, this model contributing into the cost effectiveness of the bank’s credit activities. A promising further research is considered to be aimed at developing the tools for the assessment of the commercial banks’ credit activity results in the context of the adopted changes connected with the introduction of IFRS 9 Financial Instruments in the Russian banking sector.
Keywordsexpected credit losses, credit risk, credit risk management, credit risk analysis, default, bank borrower, commercial bank, bank monitoring, financial stability, bank business model
For citationTravkina E.V. Improved forecast assessment for the expected credit losses in credit risk monitoring in commercial banks in the context of international and Russian practices. Perm University Herald. Economy, 2020, vol. 15, no. 3, pp. 445–457. DOI 10.17072/1994-9960-2020-3-445-457
References1. Demyanyk Y., Hasan I. Financial crises and bank failures: A review of prediction methods. Omega, 2010, vol. 38 (5), pp. 315–324. doi: 10.1016/j.omega.2009.09.007.
2. Mayes D., Stremmel H. The effectiveness of capital adequacy measures in predicting bank distress. SUERF Studies, 2014. Available at: https://www.suerf.org/studies/3991/the-effectiveness-of-capital-adequacy-measures-in-predicting-bank-distress (accessed 01.08.2020).
3. Bernanke B.S. Nonmonetary effects of the financial crisis in the propagation of the Great Depression. American Economic Review, 1983, vol. 73 (3), pp. 257–276.
4. Schularick M., Taylor A. Credit booms gone bust: Monetary policy, leverage cycles, and financial crises, 1870–2008. American Economic Review, 2012, vol. 102, pp. 1029–1061. doi: 10.1257/aer.102.2.1029.
5. Dell’Ariccia G. Asymmetric information and the structure of the banking industry. European Economic Review, 2001, vol. 45 (10), pp. 1957–1980.
6. Rajan R.G. Has financial development made the world riskier? Proceedings – Economic Policy Symposium. Jackson Hole, Federal Reserve Bank of Kansas City, 2005, iss. Aug., pp. 313–369. doi: 10.3386/w11728.
7. Kaminsky G., Reinhart C. The twin crises: The causes of banking and balance-of-payments problems. American Economic Review, 1999, vol. 89 (3), pp. 473–500. doi: 10.1257/aer.89.3.473.
8. Domac I., Martinez-Peria M.S. Banking crises and exchange rate regimes: Is there a Link? Journal of International Economics, 2003, vol. 61, pp. 41–72.
9. Demirgüç-Kunt A., Feyen E., Levine R. The evolving importance of banks and securities markets. World Bank Economic Review, 2013, vol. 27 (3), pp. 476–490. doi: 10.1093/wber/lhs022.
10. Schumpeter J.A. A Theory of Economic Development. Cambridge, MA, Harvard University Press, 1911. 255 p.
11. Cipullo N., Vinciguerra R. The impact of IFRS 9 and IFRS 7 on liquidity in banks: Theoretical aspects. Procedia – Social and Behavioral Sciences, 2014, vol. 164, pp. 91–97. doi: 10.1016/j.sbspro. 2014.11.055.
12. Jiang C., Wang Z., Zhao H. A prediction-driven mixture cure model and its application in credit scoring. European Journal of Operational Research, 2019, vol. 277, iss. 1, pp. 20–31. doi: 10.1016/j.ejor. 2019.01.072.
13. Liu F., Hua Z., Lim A. Identifying future defaulters: A hierarchical Bayesian method. European Journal of Operational Research, 2015, vol. 241, iss. 1, pp. 202–211. doi: 10.1016/j.ejor.2014.08.008.
14. Osmundsen K.K. Using expected shortfall for credit risk regulation. Journal of International Financial Markets, Institutions and Money, 2018, vol. 57, pp. 80–93. doi: org/10.1016/j.intfin.2018.07.001.
15. Landini S., Uberti M., Casellina S. Credit risk migration rates modelling as open systems II: A simulation model and IFRS9-baseline principles. Structural Change and Economic Dynamics, 2019, vol. 50, pp. 175–189. doi: /10.1016/j.strueco.2019.06.013.
16. Pathiranage N. P.W., Jubb C.A. Does IFRS make analysts more efficient in using fundamental information included in financial statements? Journal of Contemporary Accounting and Economics, 2018, vol. 14, iss. 3, pp. 373-385. doi: 10.1016/j.jcae.2018.10.004.
17. Tanoue Y., Kawada A., Yamashita S. Forecasting loss given default of bank loans with multi-stage model. International Journal of Forecasting, 2017, vol. 33, iss. 2, pp. 513–522. doi: 10.1016/j.ijforecast.2016.11.005.
18. Barton D., Newell R., Wilson G. Dangerous markets: Managing in Financial crisis. Hoboken, New Jersey, John Wiley and Sons, 2002. 320 p.
19. Enchengreen B., Portes R. The anatomy financial crises. National Bureau of Economic Research, 1987, Working Paper No. 2126. Available at: http://www.nber.org/papers/w2126.pdf (accessed 05.01.2020).
20. Minsky H.P. Financial instability revisited: The economics of disaster. In the Board of Governors of the Federal Reserve System (ed.), Reappraisal of the Federal Reserve Discount Mechanism, vol. 3. Washington, DC, Board of Governors of the Federal Reserve System, 1972. 91 p.
21. Travkina E.V. Faktory, obuslavlivayushchie neobkhodimost' provedeniya monitoringa riskov rossiiskogo bankovskogo sektora [Factors determining the need for risk monitoring in the Russian banking sector]. Finansy i kredit [Finance and Credit], 2013, no. 1 (529), pp. 29–33. (In Russian).
22. Larionova I.V. Trigery i bar'ery na puti obespecheniya finansovoi stabil'nosti [Triggers and barriers to financial stability]. Bankovskie uslugi [Banking Services], 2020, no. 2, pp. 20–27. (In Russian). doi: 10.36992/2075-1915_2020_2_20.
23. Travkina E.V. Sovremennye trendy v otsenke i upravlenii kreditnym riskom v deyatel'nosti rossiiskikh kommercheskikh bankov [Current trends in the assessment and management of credit risk in the activities of Russian commercial banks]. Intellekt. Innovatsii. Investitsii [Intelligence. Innovations. Investment], 2019, no. 6, pp. 117–124. (In Russian). doi: 10.25198/2077-7175-2019-6-117.
24. Travkina E.V. Sovremennoe proyavlenie kreditnogo riska v rossiiskoi bankovskoi sfere [Evidence of credit risk in the Russian banking sector]. Vestnik Saratovskogo gosudarstvennogo sotsial'no-ekonomicheskogo universiteta [Bulletin of Saratov State Social Economic University], 2018, no. 3 (72), pp. 138–141. (In Russian).