HIERARCHICAL CLUSTERIZATION AND DEEP LEARNING MODEL RANDOM FOREST OF BANKS’ STABILITY UNDER RISK CONDITIONS

Abstract

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.

Keywords

• machine learning
• deep learning model
• hierarchical clustering