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Assessing the Credit Risks in the Risk Management System of Banking Structures
Kolomiiets Y. Y., Kochorba V. Y.

Kolomiiets, Yuliia Yu., and Kochorba, Valeriia Yu. (2024) “Assessing the Credit Risks in the Risk Management System of Banking Structures.” Business Inform 1:320–332.
https://doi.org/10.32983/2222-4459-2024-1-320-332

Section: Finance, Money Circulation and Credit

UDC 336.77

Abstract:
The effective lending activity of the bank depends on the qualitative assessment of the creditworthiness of borrowers. This process determines not only the success of an individual banking operation, but also the entire credit policy of the bank. Modern banks use different approaches to creditworthiness analysis, because each loan agreement has its own specifics. The article summarizes and forms the main elements of the bank’s risk management system, substantiates the significant impact of such factors as the use of the latest technologies, stress management, and the culture of risk management. It is determined that the main and additional elements of the risk management system should take into account the features of the organizational and financial structure of each bank. In order to improve the methodologies for assessing the risks of banking activities on the basis of modeling methods and machine learning, the article proposes a conceptual model for assessing the credit risk of a commercial bank, which allows to implement the following stages: to form an information space for such an assessment and to carry out a scoring assessment of the creditworthiness of borrowers. As a result of the implementation of the first stage of the study, a set of observation objects is formed, assessed using a set of indicators; a binary characteristic of the client’s creditworthiness has been determined, which in turn determines the sign of the client’s attractiveness for lending. On the basis of the aggregate of selected factors, a comprehensive scoring model for assessing the borrower’s creditworthiness has been implemented, which allows to determine the rules for categorizing variables. A logistic regression has been constructed by the method of step-by-step exclusion and its significance has been assessed. Each client is assigned a score using a scoring card, on the basis of which a credit rating forecast for new borrowers is also calculated. The results of the research can be used by banks to improve the risk management system, as well as help banks to increase their efficiency and competitiveness.

Keywords: banking risk, risk assessment, information space, risk management, scoring, creditworthiness rating, logistic regression.

Fig.: 13. Tabl.: 4. Bibl.: 12.

Kolomiiets Yuliia Yu. – Postgraduate Student, Department of Finance, Banking and Insurance, V. N. Karazin Kharkiv National University (4 Svobody Square, Kharkіv, 61022, Ukraine)
Email: [email protected]
Kochorba Valeriia Yu. – Candidate of Sciences (Economics), Associate Professor, Deputy Director, Educational and Scientific Institute «Karazin Banking Institute» of V. N. Karazin Kharkiv National University (55 Peremohy Ave., Kharkiv, 61174, Ukraine)
Email: [email protected]

List of references in article

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Sadchykova, I., and Onopriienko, A. “Otsinka kredytnoho ryzyku komertsiinoho banku v umovakh koronakryzy“ [Assessment of the Credit Risk of a Commercial Bank Under the Conditions of the Corona Crisis]. Problemy i perspektyvy ekonomiky ta upravlinnia, no. 2 (2022): 115-124. DOI: https://doi.org/10.25140/2411-5215-2022-2(30)-115-124
Systema ryzyk-menedzhmentu v bankakh: teoretychni ta metodolohichni aspekty [Risk Management System in Banks: Theoretical and Methodological Aspects]. Odesa: ONEU, 2017.

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