Artificial neural network model for managing bank capital

The paper considers the theoretical foundations for the use of artificial intelligence systems for managing bank capital. By growth rates of assets of a commercial bank, one can judge about its success and competitiveness. However, any activity of a bank is associated with the risk of growth, and, depending on a size of the bank, the parameters under consideration will differ. The aim of the study is to develop a neural network model to ensure the capital management of a commercial bank. The results obtained represent an increment in scientific knowledge, since the developed neural network algorithm provides a contribution to the scientific field regarding the use of artificial intelligence systems in solving problems in the banking sector. The hypothesis was advanced and proved that the neural network model "Kohonen Map" can be used to forecast the asset growth. Thus, for example, with the input parameters of the model: asset in 2018 - 26972302745 thousand rubles and asset in 2019 - 27735034904 thousand rubles, the projected value of growth in Sberbank's assets for the next year will be 2.8%, which coincides with the actual growth of the current year. A VaR model has been developed to assess the financial risk of building up a bank asset. The obtained value of X(1) = -4131299748 thousand rubles indicates that in the next year, the growth of assets will not exceed the value of -4131299748 thousand rubles with a 99% probability. And X(5) indicates that over the next five years, the decrease in assets will not fall below -9237867073.53 thousand rubles with a probability of 99%.