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.