Modeling of Bank Performance Indicators Based on Business Intelligence and Data Analysis

Given the high level of uncertainty in the economic situation, ensuring the stable operation of the banking industry requires the expansion of mathematical tools for modelling the development of the situation and decision-making. The purpose of our study is to investigate the factors that influence the productivity of the banking industry and its profitability based on business intelligence and data analytics. The goal is achieved by solving the following tasks: systematization of theoretical and practical principles of using business intelligence in the banking industry; development of economic and mathematical models of the relationship between efficiency, risk and liquidity of banks; modelling the state of the banking industry of Ukraine. Based on a comprehensive literature review and collected data, a number of parameters were analyzed and theoretical models were developed to study the impact of key factors on the efficiency of Ukrainian banks, which allowed for the expansion of the use of business intelligence and data analytics in the banking industry. The study used correlation and regression analysis, decision-making methods, and a statistical package for data analytics. The study revealed correlations between banks' performance and a number of indicators that characterize their financial stability, liquidity, solvency, credit and currency risks. This study shows that it is very important to successfully plan business analytics and data analytics in order to get all the benefits of this technology and its application in the current conditions of the banking industry.