Predicting the revocation of a bank license using machine learning algorithms

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 c...

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Bibliographic Details
Published in:Procedia computer science 2021, Vol.190, p.164-170
Main Authors: Domashova, Jenny V., Gultiaev, Andrey A.
Format: Article
Language:English
Subjects:
banking license
classification
financial report
licence revocation
machine learnng
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2021.06.021