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Predicting the revocation of a bank license using machine learning algorithms

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dc.contributor.authorDomashova, J. V.
dc.contributor.authorGultiaev, A. A.
dc.contributor.authorДомашова, Дженни Владимировна
dc.date.accessioned2024-11-29T19:33:06Z
dc.date.available2024-11-29T19:33:06Z
dc.date.issued2021
dc.description.abstract© 2020 Elsevier B.V.. All rights reserved.This article presents the results of applying various machine learning methods to predict the revocation of credit organizations' licenses in Russia. The goal of the research is to predict whether the bank's license will be revoked soon. The feature space was analyzed, and additional features were calculated. Different basic classification algorithms, such as logistic regression, support vector machines classifier, decision tree, and bagging ensemble algorithm, were tested to solve the problem. Each algorithm was optimized to perform well with a highly unbalanced data. An enhanced bagging-based algorithm with weighted voting was developed to improve the classification quality. The results of this research can be used both by credit organizations themselves to monitor business conditions and assess risks, and by legal entities that cooperate with them for safe placement of their funds. A software tool in Python that allows solving problems of timely prediction of license revocation based on the developed algorithm was developed.
dc.format.extentС. 164-170
dc.identifier.citationDomashova, J. V. Predicting the revocation of a bank license using machine learning algorithms / Domashova, J.V., Gultiaev, A.A. // Procedia Computer Science. - 2021. - 190. - P. 164-170. - 10.1016/j.procs.2021.06.021
dc.identifier.doi10.1016/j.procs.2021.06.021
dc.identifier.urihttps://www.doi.org/10.1016/j.procs.2021.06.021
dc.identifier.urihttps://www.scopus.com/record/display.uri?eid=2-s2.0-85112637150&origin=resultslist
dc.identifier.urihttps://openrepository.mephi.ru/handle/123456789/24601
dc.relation.ispartofProcedia Computer Science
dc.titlePredicting the revocation of a bank license using machine learning algorithms
dc.typeConference Paper
dspace.entity.typePublication
oaire.citation.volume190
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