AI-System for Predicting the Russia's GDP Volume Based on Dynamics of Factors in the Transport Sector

Lomakin, Nikolay; Kulachinskaya, Anastasia; Tudevdagva, Uranchimeg; Radionova, E. A.; Mogharbel, Natalya

doi:10.1007/978-3-030-84845-3_8

Cited by 1 publication

(1 citation statement)

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“…Answering the question of what is new in this topic, it should be noted that the authors recently studied a correlation-regression model for analyzing overdue debts and an AI system for predicting the financial risk of Russian commercial banks; the scientific novelty of this study is to fill the gap by studying the patterns regarding the sustainability of the economy based on the Random Forest DL model (Lomakin et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

Forecast of Stability of the Economy of the Russian Federation with the AI-System “Decision Tree” in a Cognitive Model

Lomakin,

Kulachinskaya,

Tsygankova

et al. 2023

IJTech

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Increased use of modern mathematical algorithms based on artificial intelligence determined the relevance of this study, which is important for predicting the sustainable development of the country's economy in general and its banking sector in particular. To achieve the purpose of the research, the presented work used methods such as monographic, analytical, statistical, cognitive model, and artificial intelligence system "Random Forest". The aim of the study is to prove or disprove the hypothesis that, using a cognitive model, using the Random Forest ML model, it is possible to obtain an accurate forecast of the value of the "sustainability coefficient", reflecting the stability of the domestic economy. The scientific novelty of the study is due to the fact that the author's approach is proposed for indicating the crisis state of the economy through the calculation and neural network forecasting by the machine learning model "Random Forest" of the "Stability Coefficient" of the economy, which is calculated as the quotient of dividing the profit index of the banking system to the GDP growth index. The possibility of practical application in the banking sector determines the practical significance of the conducted scientific research since the approach proposed by the authors regarding the formation of a forecast of the "sustainability coefficient" can be successfully used to support managerial decision-making at the strategic level in the banking system. A hypothesis was put forward and proven that based on the use of a digital cognitive model and the Random Forest ML system, a forecast of economic stability can be successfully generated.

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Section: Introductionmentioning

confidence: 99%