THE ARTIFICIAL INTELLIGENCE IN MODELING ECONOMIC PROCESSES

Анотація

In the context of a constantly changing market environment, artificial intelligence acts as a tool
that fundamentally changes the approaches to analyzing and forecasting economic processes. The
prospect of integrating modern machine learning algorithms into business models opens new horizons
for increasing the accuracy, adaptability, and speed of decision-making, and optimizing their
resources.
The use of methods such as machine learning, neural networks, deep learning, and evolutionary
algorithms allows for modeling complex non-linear relationships, identifying hidden patterns and
trends in economic data. We will systematize the directions of application for the main
methodological approaches of artificial intelligence in the modeling of economic processes, which
are currently gaining widespread use in fig. 1.
Among the technical aspects of applying artificial intelligence in modeling economic processes,
several critical directions can be identified that highlight both methodological depth and practical
applicability. First, the implementation of models on Python-based platforms such as TensorFlow,
PyTorch, and Scikit-learn provides a flexible environment for constructing, training, and deploying
machine learning algorithms, enabling researchers and practitioners to address complex economic
problems with scalable solutions. Second, the application of Natural Language Processing (NLP)
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methods facilitates the analysis of economic texts, policy documents, and financial news, thereby
enriching models with qualitative insights and improving the accuracy of forecasts in dynamic
information environments. Third, the utilization of cloud services ensures access to substantial
computational power, which is indispensable for processing large-scale datasets and conducting realtime simulations of economic scenarios. Finally, the application of rigorous model quality evaluation
metrics – including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), classification
accuracy, and the F1-score—provides a systematic framework for assessing the reliability and
robustness of AI-driven models. Collectively, these technical components establish a foundation for
integrating artificial intelligence into economic modeling, enhancing the precision, adaptability, and
strategic relevance of decision-making processes in both corporate and governmental contexts.