Socio-Economic Problems
of the Modern Period of Ukraine

Collection SEPSPU -- sep2019.04.069

Repository of Institute of Regional Research Repository of Vernadsky Library UDC 336.71; JEL G21, C50
Yurynets, Z., Yurynets, R., Kunanets, N., & Myshchyshyn, I. (2019). Rehresiyna model' otsinyuvannya platospromozhnosti kliyenta ta bankivs'kykh ryzykiv u protsesi kredytuvannya [Regression model of assessment of customer solvency and banking risks in the process of lending]. In Sotsial'no-ekonomichni problemy suchasnoho periodu Ukrayiny [Socio-Economic Problems of the Modern Period of Ukraine]: Vol. 138 (4) (pp. 69-73). DOI: [in Ukrainian].
Sources: 7


Yurynets Zoryna Volodymyrivna

Doctor of Economics, Associate Professor

Professor of the Department of management of the Faculty of Economics of the Ivan Franko National University of Lviv



Yurynets Rostyslav Volodymyrovych

Ph.D. of Physics and Mathematics

Associate Professor of the Department of information systems and networks of the Institute of Computer Science and Information Technology of the Lviv Polytechnic National University



Kunanets Nataliya Eduardivna



Myshchyshyn Ivanna Romanivna

Junior Researcher of the Department of regional ecological policy and environmental management of the Dolishniy Institute of Regional Research of NAS of Ukraine




In the current conditions of economic development, it is important to pay attention to the study of the main types of risks, effective methods of evaluation, monitoring, analysis of banking risks. One of the main approaches to quantitatively assessing the creditworthiness of borrowers is credit scoring. The objective of credit scoring is to optimize management decisions regarding the possibility of providing bank loans. In the article, the scientific and methodological provisions concerning the formation of a regression model for assessing bank risks in the process of granting loans to borrowers has been proposed. The proposed model is based on the use of logistic regression tools, discriminant analysis with the use of expert evaluation. During the formation of a regression model, the relationship between risk factors and probable magnitude of loan risk has been established. In the course of calculations, the coefficient of the individual's solvency has been calculated. Direct computer data preparation, including the calculation of the indicators selected in the process of discriminant analysis, has been carried out in the Excel package environment, followed by their import into the STATISTICA package for analysis in the “Logistic regression” sub-module of the “Nonlinear evaluation” module. The adequacy of the constructed model has been determined using the Macfaden's likelihood ratio index. The calculated value of the Macfaden's likelihood ratio index indicates the adequacy of the constructed model. The ability to issue loans to new clients has been evaluated using a regression model. The conducted calculations show the possibility of granting a loan exclusively to the second and third clients. The offered method allows to conduct assessment of client's solvency and risk prevention at different stages of lending, facilitates the possibility to independently make informed decisions on credit servicing of clients and management of a loan portfolio, optimization of management decisions in banks. In order for a loan-based model to continue to perform its functions, it must be periodically adjusted.


credit risk, regression model, discriminant analysis, banks, credit, credit scoring, risk, expert assessment


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