The Impact of AI on Economic Modelling

Purpose: The aim of the article is to examine how artificial intelligence is changing economic modeling, with particular emphasis on its impact on traditional methods, practical applications, and development prospects. Design/Methodology/Approach: The paper analyzes the key benefits of implementing AI in economics, such as improved forecast accuracy, the ability to process large data sets, reduced model creation time, and real-time analysis. It also discusses the challenges and limitations, including issues with model interpretability and dependency on data quality. Findings: The development of AI opens up new possibilities that can complement or replace traditional approaches, introducing greater flexibility and precision in modeling economic phenomena. Practical Implications: Artificial Intelligence (AI) is an interdisciplinary field of research aimed at designing systems capable of learning, analyzing data, and making decisions. Currently, AI is applied in various areas such as medicine, engineering, logistics, and economics, offering modern tools that support analysis and forecasting. Thanks to advanced machine learning and deep learning algorithms, it is possible to process vast data sets and detect patterns that were previously difficult to identify. In traditional economic modeling, econometric techniques such as linear regression or time series models (e.g. ARIMA) play a key role. Originality/Value: Despite their effectiveness in many applications, these methods have limitations due to the need to adopt theoretical assumptions and the difficulty of analyzing complex, nonlinear data.