Analysis of the stability factors of Ukrainian banks during the 2014–2017 systemic crisis using the Kohonen self-organizing neural networks

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The article proposes an approach to analyzing reliability factors of commercial banks during the 2014–2017 systemic crisis in the Ukrainian banking system, using the Kohonen self-organizing neural networks and maps. As a result of an experimental study, data were obtained on financial factors affecting the stability of a commercial bank in a crisis period.

It has been concluded that during the banking crisis in Ukraine in 2014–2017, the resource base of a bank was the main factor of this bank stability. The most preferred sources of resources were funds from other banks (bankruptcy rate of 5.7%) and legal entities (bankruptcy rate of 8%), and the least stable were funds from individuals (bankruptcy rate of 28.5%).

The relationship between financial stability and the amount of capital and the structure of bank loans is less pronounced. However, one can say that banks that focused on lending to individuals experienced a worse crisis than banks whose main borrowers were legal entities.

The tools considered in the article (the Kohonen self-organizing neural networks and maps) allow for efficiently segmenting data samples according to various criteria, including bank solvency. The “hazardous” zones with a high bankruptcy rate (up to 49.2%) and the “safe” zone with a low rate of bankruptcy (6.3%) were highlighted on the map constructed. These results are of practical value and can be used in analyzing and selecting counterparties in the banking system during a downturn.

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    • Figure 1. SOM (2014–2017). Automatic clustering
    • Figure 2. SOM (2014–2017). Local zones on the bank solvency map
    • Figure 3. SOM (2014–2017). Global zones on the bank solvency map
    • Figure 4. SOM (2014-2017). Kohonen maps for selected indicators of bank liabilities
    • Table 1. Statistical analysis of input data characteristics
    • Table 2. Anomalous values of balance sheet ratios of Ukrainian commercial banks in 2014–2015
    • Table 3. Statistical characteristics of parameter 15 after eliminating anomalous values
    • Table 4. SOM (2014–2017). Analysis of bank reliability by automatically allocated clusters
    • Table 5. SOM (2014–2017). Analysis of the selected clusters’ profiles