A Methodological Approach to Forecasting the Stability of Commercial Banks by An Ai-System in The Context of Deepening Social Inequality

The article considers a methodological approach to forecasting the stability of commercial banks in the context of deepening social inequality. The relevance lies in the fact that in modern conditions, which are characterized by further differentiation of the population in terms of income, increasing market uncertainty and risk, the issues of sustainable development of the banking sector require further research. It should be noted that this increases the risks of reducing the effectiveness of banking activities. Maintaining the banking sector is the most important task of the country’s national security. The scientific novelty lies in the fact that the study hypothesized that the forecast of the assets of the banking system for the next month can be formed based on the use of the DL Random Forest Regression deep learning model, which uses data from the Gini coefficient variation series, the assets of commercial banks, as well as a number of macroeconomic indicators over a number of years. The process of «impoverishment» of the population can largely lead to a decrease in the growth rate of the banking sector. It is important to identify the optimal level of the Gini coefficient. The result obtained is an increment of scientific knowledge, and can be used as a methodological approach based on the use of artificial intelligence to ensure the sustainable development of commercial banks, which determines its practical significance. The purpose of the study is to use the dynamics of the Gini coefficient, which reflects the process of stratification of the population by income level, as well as a number of macroeconomic indicators to obtain a forecast of the asset value of commercial banks for the next year, using the developed AI model DL Random Forest. In the course of the research, the following tasks were set and solved to achieve the goal: to study the theoretical aspects of the stability of commercial banks, to develop a dataset of the AI model DL Random Forest, to generate a forecast value of the assets of the banking system for the next year. The neural network showed high forecast accuracy/