Artificial Intelligence in Economic Analysis: Methods and Impact
Keywords:
Artificial Intelligence, Economic Analysis, Machine Learning, Predictive Analytics, Economic Forecasting, Big Data, Policy Evaluation.Abstract
Artificial Intelligence (AI) is transforming economic analysis by integrating advanced computational techniques into traditional theoretical and empirical frameworks. The growth of big data, digital transactions, and real-time economic indicators has increased the complexity of economic research, making AI-driven tools increasingly important. This paper examines the integration of AI methodologies, including machine learning, deep learning, natural language processing, and predictive analytics, into economic modeling, forecasting, and policy evaluation.
Drawing on contemporary literature and case studies from institutions such as the International Monetary Fund and the World Bank, the study highlights how AI enhances predictive accuracy, identifies nonlinear relationships, and processes large volumes of data more efficiently than conventional econometric approaches. Evidence suggests that AI-driven systems improve risk assessment, reduce analytical costs, and support data-driven decision-making across public and private sectors.
The paper also discusses key challenges, including data quality, algorithmic bias, transparency, interpretability, privacy concerns, and ethical governance. It concludes that AI should complement rather than replace traditional economic reasoning and emphasizes the need for robust regulatory frameworks, interdisciplinary collaboration, and ethical standards to ensure the responsible and sustainable use of AI in economic analysis.
