Investigation of the scoring model for bank borrowers
Keywords:credit scoring, creditworthiness, solvency, creditworthiness assessment, borrowers, scoring card, classification of features, creation methods, machine learning, model testing
In the paper we investigate scoring models as a tool for credit risk management, their importance, types, features and applications. We consider the history of scoring models and the development of the modern concepts of creditworthiness and scoring, examine scoring types and their features, methods (logistic regression, decision trees, linear programming, decision trees, and others), strengths and weaknesses of each considered method and stages of building scoring models; we stress the importance of scoring cards for building scoring models, indicate the main quantitative and qualitative features, their classification used for and describe the procedure of creating scoring cards. In the paper we consider the factors needed to be considered for more effective scoring model building process; we indicate the main formulas used to assess the creditworthiness of borrowers and improve the accuracy of scoring models, including Population Stability Index (PSI), R-Square Coefficient, Kolmogorov-Smirnov Coefficient, GINI Coefficient, and others; the practical application of scoring models in banks of Ukraine and their steps after applying scoring models are described on the example of a typical borrower.
Pages of the article in the issue: 44 - 53
Language of the article: Ukrainian
Regulation on the determination by banks of Ukraine of the amount of credit risk for active banking operations, approved by the resolution of the NBU Board dated June 30, 2016. No. 351, with changes.
VDOVENKO L. O. Economic essence and the meaning of creditworthiness of enterprises // Accounting and finance. – 2012. – No. 1.
BUCHKO I. E. Scoring as a method of reducing the bank's credit risk // Bulletin of the University of Banking of the National Bank of Ukraine. – 2013. – No. 2.
BOHDAN POPOVYCH. Application of AI in Credit Scoring Modeling - Springer Gabler, 1st ed., 2022.
Regulations on the organization of the risk management system in banks of Ukraine and banking groups, approved by the resolution of the NBU Board dated June 11, 2018. No. 64, with changes.
KUZNETSOVA N. V. Comparative analysis of the characteristics of lending risk assessment models / ed. N. V. Kuznetsova, p. I. Bidyuk / Scientific news of NTUU "KPI", 2010. No. 1.
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Copyright (c) 2023 Volodymyr Zubchenko, A. V. Avramenko
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