Innovative Digital Model of the Infl uence of the Key Rate on the Profi tability of the Russian Banking System

Abstract

The article considers theoretical approaches to modeling the sustainable development of the Russian banking system in the context of innovative transformations and the formation of digital ecosystems. The relevance of the study lies in the fact that in modern conditions, approaches are increasingly being used to ensure the sustainable development of the banking system based on cognitive modeling, the use of artifi cial intelligence and the formation of digital ecosystems. The scientific novelty lies in the fact that in the study, a deep learning model DL-model "Random Forest" was formed, which allows you to get a stable forecast for the net profi t of the banking system, in the conditions of innovative transformations and the formation of digital ecosystems. The practical signifi cance of the study is that the results obtained can be recommended for implementation in practice to provide support for managerial decision-making regarding the sustainable development of the banking system. The cognitive model was developed in the GraphViz environment using a semantic frame network in the form of graphs in the DOT programming language. An analysis of the dynamics of both macroeconomic indicators of the real sector of the economy and the parameters of the development of the banking sector of the Russian Federation was carried out. The criterion for the success of the predictive properties of the DL-model was the value of the average forecast error (MAE). The proposed DL model uses the best decision tree that has optimal hyperparameter settings, for example, the depth of the tree is six layers, the number of estimators (trees) in the ensemble is ten. In the experiment, the hyperparameters of the neural network did not change, the input parameters to various trees were randomly selected by the algorithm. The DL model showed high forecast accuracy.