Lomakin N.I. Volgograd State Technical University, Volgograd, Russian Federation firstname.lastname@example.org
Femelidi Yu.V. Volgograd State Technical University, Volgograd, Russian Federation email@example.com
Subject The article studies the evolution of the credit portfolios of Russian banks during the period under review using the self-organizing map (SOM). Objectives The article aims to prove or refute the hypothesis that by using a neural network, i.e. self-organizing map, it is possible to predict the changes in the market share of bank's credit portfolio. Methods For the study, we used the self-organizing map. Results We have developed and now present a neural network model that helps predict the market share of an advances portfolio in a changing market under economic uncertainty environment. Conclusions and Relevance The application of the self-organizing map is important for obtaining some statistical information on commercial banks in the model clusters, as well as for forecasting the market share of the organization in a changing market environment. The results of the study can be used in the area of bank marketing to generate predictions of the market share of the bank when the size of its portfolio changes.
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