SYSTEMATIZATION AND CLASSIFICATION OF METHODS FOR ECONOMIC ANALYSIS OF INTERNATIONAL INVESTMENTS BASED ON ARTIFICIAL INTELLIGENCE
Abstract
The article undertakes a systematization and classification of modern approaches to the economic analysis of international investments, taking into account the potential of artificial intelligence technologies. The rationale for integrating intelligent systems into the processes of evaluating and forecasting investment activity is substantiated in the context of globalization, economic digitalization, and increasing uncertainty in financial markets. The proposed framework enables the systematization of the existing methodological toolkit according to the degree of automation, analytical depth, and adaptability of models to complex dynamic processes. A comparative assessment of selected groups of methods is conducted using criteria such as forecasting accuracy, data-processing speed, and the stability of results under volatile external conditions. The study demonstrates that the use of machine-learning technologies, deep neural networks, and optimization algorithms enhances forecast reliability, expands the ability to detect hidden patterns, and reduces the risks of subjective analytical interpretations. It is emphasized that combining traditional econometric methods with AI-driven algorithms forms a new type of analytical reasoning focused on big-data processing and the development of adaptive predictive models. It is noted that the proposed classification can serve as a methodological foundation for building intelligent decision-support systems in international business and in public governance of investment flows. The practical significance of the results lies in establishing a scientifically grounded basis for improving state policy mechanisms in the field of international investment, strengthening national economic competitiveness, and developing digital infrastructures for managing investment processes. Additionally, the study substantiates the potential of hybrid models as an instrument for promptly assessing global financial trends, which is particularly important in conditions of high market volatility. The findings create a foundation for further development of applied solutions in the domain of intelligent analytics of international investments.
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