Digital marketing of the global economic landscape and the financial sustainability of the economy

Abstract

The article considers the theoretical foundations for assessing and forecasting the sustainability of the development of the Russian economy using artificial intelligence systems. The relevance of the study lies in the fact that in modern conditions the question of the formation of methodological approaches for a balanced sustainable development of the economy is particularly acute. The novelty of the study lies in the fact that an approach is proposed that involves the use of a certain system of macroeconomic indicators that reflect the dynamics of development of both the real sector of the economy and the parameters of the development of the fi nancial sector to predict the GDP of the Russian Federation using the "Perceptron" AI system. An analysis was made of both the dynamics of macroeconomic indicators of the real sector of the economy and the parameters of the development of the fi nancial sector of the Russian Federation. The authors considered the advantages of using the VaR model indicator as a measure of risk to assess the fi nancial risk of reducing the GDP of the Russian Federation. The "Perceptron" program was developed using the Deductor platform to predict the dynamics of the RF GDP. A wide range of fi nancial mathematics tools proposed by individual authors for assessing and minimizing fi nancial risk, including quantile hedging, defi cit hedging with minimal risk, and optimal quadratic hedging, are considered. The architecture of the neural network, in addition to the input layer, has two hidden layers, and an output layer with one parameter. The neural network "Perceptron" shows a high accuracy of the forecast.