Exploring financial parameters and innovative orientation of banks as criteria for selecting financial partners for enterprises

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The article substantiates aspects that are fundamental for the economic justification of the bank selection by enterprises as the main stage in the partnership formation. It also defines the development of a bank selection procedure taking into account the financial parameters and innovative orientation of banks.
The proposed procedure for selecting banks includes two blocks. The first block is the comparison of banks in terms of reliability, which is determined based on indicators for assessing their financial risks and confidence of enterprises. The second block is the comparison of the most reliable banks according to the operational capabilities of Internet banking systems (in terms of functionality) and the intensity of promotion of innovative products and services that meet the needs of enterprises.
The proposed bank selection procedure is mainly based on applying the classification functions that allow the reliability-based differentiation as well as comparison of banks. The proposals submitted with the aim to develop classification functions, as well as the bank selection procedure as a whole, were tested based on the financial statements and general information on the Ukrainian banks’ activity as of January 1, 2019. The following results were obtained while testing the developed procedure: It defines the features specific to dividing the analyzed Ukrainian banks into groups (70% are high-reliability banks, 7% are medium-reliability banks, and 23% are low-reliability banks). It also highlights the fact that reliable banks are focused on innovating and developing remote services for their business clients.

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    • Table 1. Financial parameters for determining bank reliability
    • Table 2. Dispersion analysis of Ukrainian bank clusters by their reliability levels (before excluding certain financial parameters that are not significant for clustering)
    • Table 3. Dispersion analysis of Ukrainian bank clusters by their reliability levels (after excluding certain financial parameters that are not significant for clustering)
    • Table 4. Primary clustering results (the primary classification matrix)
    • Table 5. The primary classification matrix transformation as a result of discriminant analysis (two adjustment stages were performed based on Squared Mahalanobis Distances and Posterior Probabilities)
    • Table 6. Discriminant analysis summary
    • Table 7. The results of the integrated assessment of the Ukrainian banks reliability within the existing clusters
    • Table 8. Results of comparison of Ukrainian banks on the functionality of Internet banking systems for servicing enterprises
    • Table 9. Comparison results of Ukrainian banks on the intensity of innovation promotion in the field of enterprise servicing
    • Conceptualization
      Oleksii M. Hutsaliuk, Oksana V. Yaroshevska, Olha Yu. Kotsiurba
    • Formal Analysis
      Oleksii M. Hutsaliuk, Oksana V. Yaroshevska, Olha Yu. Kotsiurba, Alla S. Navolokina
    • Investigation
      Oleksii M. Hutsaliuk, Oksana V. Yaroshevska, Alla S. Navolokina
    • Methodology
      Oleksii M. Hutsaliuk, Olha Yu. Kotsiurba, Alla S. Navolokina
    • Supervision
      Oleksii M. Hutsaliuk
    • Writing – original draft
      Oleksii M. Hutsaliuk, Oksana V. Yaroshevska, Olha Yu. Kotsiurba, Alla S. Navolokina
    • Writing – review & editing
      Oleksii M. Hutsaliuk, Oksana V. Yaroshevska, Olha Yu. Kotsiurba, Alla S. Navolokina
    • Data curation
      Oksana V. Yaroshevska, Alla S. Navolokina