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Assessing sustainability factors for credit institutions based on quantitative analyses methods
 
PIIS020736760021495-3-1
DOI10.31857/S020736760021495-3
Publication type Article
Status Published
Authors
Valeria Silnyagina 
Occupation: student
Affiliation: B. N. Yeltsin Ural Federal University
Address: Russian Federation, Ekaterinburg
Denis Gilev
Affiliation: B. N. Yeltsin Ural Federal University
Address: Russian Federation, Ekaterinburg
Journal nameObshchestvo i ekonomika
EditionIssue 8
Pages53-66
Abstract

The purpose of the study was to identify factors that increase the likelihood of revocation of a credit institution’s license by the regulator and to build a predictive model. The authors used the basic techniques of descriptive statistics, that is, the primary processing of empirical data, their systematization and visual representation, the cluster analysis of data, the building of a logistic regression, probit model and random forest algorithm, as well as forecasting based on these models, using the method of committees of three available models. 

Keywordsprobability of license revocation, credit organizations, forecasting, logit model, random forest, committee method
Publication date31.08.2022
Number of characters23306
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