Cognitive model of the national financial market: creating process and prospects for assessing the security of its functioning

Loktionova, E.A.

doi:10.26794/2587-5671-2022-26-1-126-143

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Kleiner proposed a normative model for the distribution of the role functions of subsystems over the stages of the crisis cycle of the economy. The problem of developing a cognitive model of the national financial market, considering the peculiarities of its construction and the possibility of using it to assess the security of its functioning, was studied by Loktionova (Loktionova, 2022).…”

Section: Introductionmentioning

confidence: 99%

Cognitive Model of Financial Stability of the Domestic Economy Based on Artificial Intelligence in Conditions of Uncertainty and Risk

Lomakin¹,

Maramygin²,

Катаев³

et al. 2022

IJTech

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The study's relevance lies in the fact that the important problem is ensuring the sustainability of the development of the Russian economy. Its financial system is influenced by many factors, among which are increased risks and market uncertainty, aggravation of the global military-political confrontation between the largest world powers, and technological innovations associated with the emergence of a new technological order "Industry 4.0". The purpose of the work is to study the financial stability of the state as a complex system based on a cognitive model that involves the use of an artificial intelligence system and a VaR model. The novelty of the study lies in the proposed approach, which involves the formation of a cognitive model of economic processes, which includes the Decision Tree artificial intelligence system and the VaR model, both generate GDP forecasts, then their forecast values are compared. A hypothesis has been put forward and proved that with the help of a cognitive model that includes an A.I. system and a VaR model, it is possible to obtain a forecast of the volume of Russia's GDP, the dynamics of which allows us to assess the sustainability of the development of the country's economy.

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Section: Introductionmentioning

confidence: 99%

Cognitive Model of Financial Stability of the Domestic Economy Based on Artificial Intelligence in Conditions of Uncertainty and Risk

Lomakin¹,

Maramygin²,

Катаев³

et al. 2022

IJTech

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An analysis of the Russian financial market basis

Smirnov¹

2023

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Subject. This article analyzes the basis of the Russian financial market. Objectives. The article aims to clarify the basis of the Russian financial market. Methods. For the study, I used a systems approach based on a statistical analysis. Results. The article reveals a decrease in the reaction of inflation to an increase in the interest key rate and its growth with a decrease in the key interest rate. The article also finds that the Bank of Russia is injecting the digital ruble in order to increase its regulatory capacity. The study outlines a provision on the transfer of hedging of monetary policy risks to the level of relations between the Ministry of Finance of Russia and credit institutions, increasing their likelihood by selling yuan from the Russian National Wealth Fund and introducing a digital ruble. Conclusions and Relevance. The results of the study regarding the basis of the Russian financial market orient the expert community at the time of marginal income fixing and can be useful for the expert community in terms of the Russian financial market's development.

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RF Deep Learning Model "Random Forest" for Forecasting Organizational Profit in the Digital Economy

Lomakin,

Maramygin,

Polozhentsev

et al. 2023

The World Economics

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The relevance of the study lies in the fact that in modern conditions, artificial intelligence systems are increasingly used to support management decisions regarding the formation of an organization’s development strategy and other tasks. The purpose of the study is to forecast the profit of a credit institution using the Random Forest Regressor method to support management decision-making in a digital ecosystem. Among the methods in the presented work, the deep learning model "Random Forest" (RF — random forest), a multivariate regression model based on artificial intelligence, and the Graphviz program were used. Moreover, for the 7-factor DL model, the "Mean Absolute Error" (MAE) was 13.299, the "Root Mean Square Error" (MSE) reached 176.889, and the "Root Mean Square Error" (RMSE) amounted to 31289.71832. Moreover, in the 10-factor model, the above parameters had values respectively: 21.95, 20.532 and 421.563024. That is, judging by the value (MAE), the 7-factor model turned out to be more accurate. The scientific novelty lies in the fact that a machine learning model has been developed based on the "Deep Learning Random Forest" method, which provides support for management decisions, allowing one to obtain a reliable forecast of the bank’s net profit. The solution to this problem has important practical significance. Rospatent certificate for the computer software program No. 2023666284 dated July 28, 2023 was received. The hypothesis was put forward and proven that the use of the Random Forest DL artificial intelligence system makes it possible to obtain a forecast of the net profit of a credit institution. As a result of the study, conclusions were formulated. The processes of increasing digitalization are rapidly changing the financial world. An important role in modern conditions is played by the use of modern artificial intelligence systems to provide support for management decisions. The developed machine learning model based on the "Machine Learning Random Forest" method allows us to obtain a forecast of Sberbank’s net profit for the next year. Among the directions for further scientific research, it should be noted the identification of patterns and factors influencing the behavior of the resulting attribute — the net profit of a commercial bank.

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Cognitive model of the national financial market: creating process and prospects for assessing the security of its functioning

Cited by 3 publications

References 18 publications

Cognitive Model of Financial Stability of the Domestic Economy Based on Artificial Intelligence in Conditions of Uncertainty and Risk

Cognitive Model of Financial Stability of the Domestic Economy Based on Artificial Intelligence in Conditions of Uncertainty and Risk

An analysis of the Russian financial market basis

RF Deep Learning Model "Random Forest" for Forecasting Organizational Profit in the Digital Economy

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