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- W207858996 abstract "In banking, credit risk management at one of the key tasks assessing the creditworthiness of borrowers. The results of the assessment of individual risk are the basis for risk analysis of total loans. Assessment of risk of loan default by the borrower on a particular practice is carried out in two main approaches based on the subjective opinions of experts or through automated scoring systems. The basis for constructing the scoring system may take various statistical models. These models may be obtained by linear regression, logistic regression, discriminant analysis, decision trees, neural networks, etc. However, logistic regression is the most commonly used in practice to construct a mathematical model scorecard. The present work is devoted to the different approaches and techniques to build scorecards based on logistic regression, as well as problems that may arise in the construction of scoring models. The article considers the econometric modeling technique of default probability on the credits on the basis of logistic regression model. The attention is focused on methodical aspects of model creation. The main problems of model creation are illustrated by practical calculations. Transfer technique of received coefficients of logistic regression model to the scorecard is shown. The example of the scorecard creation is given. Copyright conclusions and recommendations can be used by experts on risk management in commercial banks in constructing scoring systems and checking their work." @default.
- W207858996 created "2016-06-24" @default.
- W207858996 creator A5059278638 @default.
- W207858996 date "2014-01-01" @default.
- W207858996 modified "2023-09-23" @default.
- W207858996 title "Построение скоринговых карт с использованием модели логистической регрессии" @default.
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- W207858996 hasPublicationYear "2014" @default.
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