<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The article is devoted to developing a definition of the indicator of the bank’s competitiveness which based on the theory of fuzzy sets and neural networks techniques. Uncertainties that have a place when considering and analyzing the components of evaluating the success and effectiveness of the bank have been considered and analyzed. The sequence of construction and structure for generalizing parameter of bank competitiveness are presented and grounded. Stages of obtaining an integrated assessment of bank competitiveness by sequential application of fuzzy logic and neural networks approaches are determined and described. Corresponding fuzzy terms, membership functions and fuzzy inference rules are described. Overall sequence and steps to resolve the problem are processed. The practical implementation of the summary fuzzy inference of the bank’s competitiveness is given. In particular, numerical calculations on the proposed model for Ukrainian commercial bank “Khreshchatyk” was carried out. Comparison of obtained evaluation results for the competitiveness of specified bank with available data and other scientific information sources showed their compliance with factual situation. In this way, the expediency of application fuzzy modeling has been confirmed to determine the generalized indicators of bank competitiveness. Adequacy and accuracy of the proposed model and the results of calculations were proved. The proposed approach is quite general. This or similar model can be successfully used in other tasks of building and generalized evaluation of integrated indicators for the presence of several local, individual parameters for different economic processes and tasks.</p></abstract>

The article is devoted to developing a definition of the indicator of the bank’s competitiveness which based on the theory of fuzzy sets and neural networks techniques. Uncertainties that have a place when considering and analyzing the components of evaluating the success and effectiveness of the bank have been considered and analyzed. The sequence of construction and structure for generalizing parameter of bank competitiveness are presented and grounded. Stages of obtaining an integrated assessment of bank competitiveness by sequential application of fuzzy logic and neural networks approaches are determined and described. Corresponding fuzzy terms, membership functions and fuzzy inference rules are described. Overall sequence and steps to resolve the problem are processed. The practical implementation of the summary fuzzy inference of the bank’s competitiveness is given. In particular, numerical calculations on the proposed model for Ukrainian commercial bank “Khreshchatyk” was carried out. Comparison of obtained evaluation results for the competitiveness of specified bank with available data and other scientific information sources showed their compliance with factual situation. In this way, the expediency of application fuzzy modeling has been confirmed to determine the generalized indicators of bank competitiveness. Adequacy and accuracy of the proposed model and the results of calculations were proved. The proposed approach is quite general. This or similar model can be successfully used in other tasks of building and generalized evaluation of integrated indicators for the presence of several local, individual parameters for different economic processes and tasks.

Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

Journal of Central Banking Theory and Practice

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach"}

The article is devoted to developing a definition of the indicator of the bank’s competitiveness which based on the theory of fuzzy sets and neural networks...

Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

Published Online: Jan 03, 2019

Page range: 163 - 182

DOI: https://doi.org/10.2478/jcbtp-2019-0008

Keywords
bank competitiveness, fuzzy modeling, bank indicators, uncertainty in economics, bank service estimations, neural networks modeling, constructing general indicators

© 2019 Lyudmyla Malyarets et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach

Published Online: Jan 03, 2019

Page range: 163 - 182

DOI: https://doi.org/10.2478/jcbtp-2019-0008

Keywordsbank competitiveness, fuzzy modeling, bank indicators, uncertainty in economics, bank service estimations, neural networks modeling, constructing general indicators

© 2019 Lyudmyla Malyarets et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
bank competitiveness, fuzzy modeling, bank indicators, uncertainty in economics, bank service estimations, neural networks modeling, constructing general indicators