Scientific and methodological approach to clusterization of systematically important banks by their operational risk proposition

Gonchar Kateryna
Postgraduate Student, Postgraduate student of The Department of Banking, Odessa National University of Economics, Odesa, E-mail:sivkovak@ukr.net, ORCID ID: https://orcid.org/0000-0002-8350-3134

Cite this article:

Gonchar K. (2022) Scientific and methodological approach to clusterization of systematically important banks by their operational risk proposition. Ed.: V.V. Kovalenko (ed.-in-ch.) and others [Naukovo-metodychnyi pidkhid do klasteryzatsii systemno vazhlyvykh bankiv za yikh skhylnistiu do operatsiinoho ryzyku; za red.: V.V. Kovalenko (gol. red.)], Scientific Bulletin of the Odessa National Economic University (ISSN 2313-4569), Odessa National Economics University, Odessa, No. 3-4 (292-293), pp. 46-52.

Abstract

The purpose of this article is to develop an algorithm for clustering systemically important banks according to their propensity to implement operational risk. Method. The research was conducted using mathematical and statistical methods of analysis of financial controlling indicators of the bank's operational risk, which are related to the main, auxiliary and service business processes of the bank. Results of the article. The article proposes a scientific and practical approach to clustering systemically important banks according to their propensity to operational risk. The article also describes the stages of implementation of the methodological approach to determining the propensity of systemically important banks to operational risk. The cluster and discriminant analysis identified a list of indicators of financial controlling of the bank's operational risk that have the greatest impact on the classification of systemically important banks in the cluster of leaders, middlemen or outsiders in operational risk management. The average values of significant indicators of financial controlling of operational risk of systemically important banks by clusters are calculated. The use of stimulating and disincentives for financial control of operational risk, highlighting only significant factors for the model and assessing their impact in the clustering process has improved the quality of research and increased the likelihood that clustering is performed reliably. It was found that the wrong choice of indicators of financial controlling of operational risk and the number of clusters can lead to disparities in the results, so further research in this area should use indicators of financial controlling of operational risk with high tolerance to the chosen model to obtain more analytically sound conclusions. The scientific novelty of the article is the practical development of stages of implementation of the methodological approach to determining the propensity of systemically important banks to operational risk based on cluster and discriminant analysis. The practical significance of this work is to identify banks that are more vulnerable to operational risk and identify the factors that most affect the outcome.

Keywords

systemically important banks, operational risk, financial controlling of operational risk, clustering of banks.

JEL classification: C400, G210, O300; DOI:10.32680/2409-9260-2022-3-4-292-293-46-52

UD classification: 336.719

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