Analytical Methods for Assessing and Forecasting Financial Standing of Credit Institutions
https://doi.org/10.26794/2587-5671-2019-23-1-79-95
Abstract
The objective of the article is to propose a new approach to assessing and forecasting fnancial condition of credit institutions and to early detection of those that have high risks of license revocation. An integrated reliability index of credit institutions has been revealed by the method of the main components of the factor analysis. Credit institutions have been clustered by means of the k-average method. It has been established that acting credit institutions at a relatively small Euclidean distance from the mathematical expectation of credit institutions, liquidated at a given moment of time, bear potential risks of engaging in illegal activities, money laundering and terrorist fnancing. Constructed regression models allow forecasting deterioration of credit institutions by the nature of the change in the integrated reliability index. The author concludes that this approach makes it possible to identify potentially problematic credit institutions requiring appropriate measures from the Central Bank of the Russian Federation through prudential supervision functions.
About the Author
Y. M. Beketnova
Financial university
Russian Federation
Russian Federation
Yuliya M. Beketnova — Cand. Sci. (Eng.), Associate Professor, Department of Information Security
Moscow
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Review
For citations:
Beketnova Y.M. Analytical Methods for Assessing and Forecasting Financial Standing of Credit Institutions. Finance: Theory and Practice. 2019;23(1):79-95. (In Russ.) https://doi.org/10.26794/2587-5671-2019-23-1-79-95
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