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Certain theoretical aspects of the stability of Russian banks under risk conditions have been studied. The relevance is due to the fact that in conditions of market uncertainty and risk, approaches to ensure the stability of banks using artificial intelligence are increasingly being used. The goal is to identify patterns between the characteristics of Assets and ROA (Return on Assets), an indicator of return on assets, and obtain a forecast value of Sberbank’s net profit. The result of the study was hierarchical clustering, as well as the generated Deep Learning model Random Forest, which calculated the predicted value of the Sberbank’s net profit. The novelty lies in the fact that the work puts forward and proves the hypothesis that using the Random Forest Deep learning model, a forecast of the net profit of commercial banks can be obtained, which predetermines the stability and dynamics of their development. The conclusions from the study boil down to the fact that a Deep Learning model Random Forest was developed to forecast the amount of net profit, which for Sberbank for 2023 amounted to 38,631 billion rubles, which coincided with its actual value. The area of application of the results obtained is commercial banks.

Theoretical foundations for the development of environmental protection measures in Russia and the world, the issues of investing in environmental protection are studied. The scientific novelty lies in the fact that based on the use of a variational series of the studied parameters, a machine learning model “Decision Tree” was developed, with the help of which a predictive value of the amount of investment in environmental protection measures was formed. The relevance lies in the fact that to determine the volume of investment in environmental measures in the country, an artificial intelligence system was used – machine learning “Decision tree” (ML “Decision tree”), which made it possible to successfully solve a complex problem due to the influence of many factors on the parameter under study. The use of the proposed approach is especially relevant in the light of the May Decrees of the President of the Russian Federation. In 2018, in the Decrees of the President, strategic principles for the development of the country were formulated, aimed at the formation of a “digital economy”, designed to ensure further movement in accordance with the “Strategy for Scientific and Technical Development of Russia” in the main direction, according to which, as a foundation for the implementation of innovative changes in the state will use digital technologies. With the help of the artificial intelligence system, a forecast was formed for such a parameter as the volume of investment in the country in environmental measures for the next year, namely 6.2221 million rubles.

The article analyzes current trends in the use of artificial intelligence, cognitive technologies, Big Data and other innovations in order to predict the stability of the Russian banking system. The current trends in the development of banking innovations in the context of digitalization of the economy are summarized. Among the research methods used, the Random Forest artificial intelligence system, formed in the Google Collab cloud service in Python, should be noted. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF – random forest) machine learning model is proposed. The results of the study indicate that, with the average value of the profit of banks for the period 2010-2021. 1126.0416 billion rubles, the mean absolute error (Mean Absolute Error) amounted to 129.96666 billion rubles, or 11.5%, which can be regarded as a good result in conditions of market uncertainty. The analysis showed that current trends are global in nature and lead to qualitative changes in the flow of business processes between consumers of banking services and credit institutions. Purpose: is to form a model that predicts the value of the target variable – the profit of the banking system using the Random Forest deep learning model, which is an ensemble of “decision trees”. The scientific novelty is to put forward and prove the hypothesis that with the help of the formed artificial intelligence system “Random Forest”, a profit forecast for commercial banks of the Russian Federation can be obtained. Method or methodology of the work. The work used such research methods as: monographic, analytical, artificial intelligence system – “Random Forest”, as well as comparative analysis. Calculations were performed in XL tables, Google Collab cloud service in Python. Results. The developed AI-system “Random Forest”, designed to predict the profits of banks, is presented. It is of practical importance to use the results of the Random Forest neural network to predict the development of credit institutions, which makes it possible to solve a major national economic problem - ensuring the stability of the banking system of the Russian Federation. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF) machine learning model is proposed, which is a machine learning algorithm that consists in using an ensemble (set) of decision trees. The scope of the results: the financial sector, the banking sector, forecasting and ensuring the sustainable activity of credit institutions in the Russian Federation.

Theoretical foundations of GDP dynamics under conditions of market uncertainty and the formation of a digital economy are studied. Forecasting GDP is important because it allows to ensure balance, sustainability in the development of various sectors of the national economy, and its economic security, which determines the practical significance of the study. Purpose. To put forward and prove the hypothesis that with the help of the neural network model “perceptron” and the semantic model of knowledge representation, it is possible to obtain a forecast of Russia’s GDP for the next year. The scientific novelty lies in the fact that forecasts of the RF GDP were generated using a semantic knowledge representation model and the Perceptron AI system. Method or methodology of the work. The work used such methods as monographic, analytical, artificial intelligence system - perceptron, Mind histogram, as well as comparative analysis. Calculations were performed in tables XL perceptron was formed on the Deductor platform. We used a semantic knowledge representation model developed in the DOT language using the Graphviz 2.38 program. Results. Theoretical foundations of the use of neural networks in economic forecasts have been studied. A semantic model of knowledge representation has been formed regarding forecasting the GDP of the Russian Federation by a neural network. The developed AI-system “perceptron” is presented, designed to forecast Russia’s GDP based on input parameters representing a set of data reflecting the development of the real sector of the economy and the financial sector of Russia for 2011–2021. The scope of the results: economics, the financial sector, forecasting and planning of economic and financial activities.

In the course of the study, a nonlinear dependence of the GDP dynamics on the increase in freight turnover was revealed. A regression model has been developed that determines the value of GDP depending on the dynamics of freight turnover. It should be stated that in Russia there is a rather low level of application of digital innovations, in comparison with foreign companies in developed countries, and continues to remain at a low level. Purpose. A hypothesis was put forward and proved that using a mathematical model, it is possible to obtain a forecast of Russia’s GDP for the next year based on the use of both a linear regression model reflecting the dependence of the GDP value on the dynamics of freight turnover, and using the AI-model “perceptron”, which includes the aggregate data reflecting the development of the economy. Method or methodology of the work. The work used research methods such as: monographic, analytical, linear regression and nonlinear mathematical model, as well as analysis, study and generalization. Results. A developed AI model (artificial intelligence model) “Perceptron” is presented, designed to predict Russia’s GDP based on input parameters representing a set of data reflecting the development of the real sector of the Russian economy in 2011–2019, including the dynamics of freight turnover. The experience of using artificial intelligence systems for forecasting time series, including the RF GDP, is considered. Field of application of the results: economics, financial sphere, forecasting and planning of economic and financial activities, economic security