ANALYSIS OF BANKS’ FINANCIAL STABILITY: STATISTICAL METHODS AND PRACTICES

The article systematizes the statistical tools for analysing the financial stability of Ukrainian banks, considering the conditions of their operation. The study is conducted in the following logical sequence: clarification of the concept of «bank financial stability» as an object of statistical analysis; formation of a system of financial stability indicators for banks; determination of statistical analysis methods and development of the recommendations for their application to assess aspects of bank activities that influence their financial stability. Bank financial stability is proposed to be viewed as a complex, multidimensional latent characteristic, determined by a combination of internal and external factors, not subject to direct measurement, and manifested through other parameters of bank functioning. The article generalizes individual indicators of banks’ financial stability, including regulatory approaches and additional indicators, based on key parameters such as capitalization, asset and liability quality, operational efficiency, and liquidity. The applicability of composite indicators, which allow to do a comprehensive assessment of the bank’s financial stability, is determined. The systematization of the literature sources and approaches has shown that using the statistical methods allows for more accurate assessment and forecasting of banks’ financial stability parameters and timely identification of potential issues that may lead to increasing crisis potential. The statistical tools for analysing banks’ financial stability are systematized into the following categories: descriptive statistics, time series analysis, regression analysis, cluster analysis, factor analysis, machine learning, and artificial intelligence. The study empirically confirms and theoretically proves that integrating big data and modern analytical methods can significantly improve the accuracy of assessing banks’ financial stability in an operational environment characterized by uncertainty, nonlinearity, and information asymmetry. The research results can be helpful for banking institutions and researchers dealing with financial stability and risk management issues.