The Application of Artificial Intelligence in economics: A review of current trends and future directions

Abstract

Artificial Intelligence is fundamentally reshaping the practice and mission of economics, creating massive opportunities to augment analytical capacity, increase predictive accuracy, and steer policy decision-making. The present study provides a systematic survey on the trends and trajectories of AI in modern economics, covering both its methodologies, various domain-specific applications as well as potential advantages and challenges. This paper includes Key AI subfields such as ML, DL, NLP and RL (reinforcement learning), but especially also Causal AI due to its advantages of dealing with vast, complex, often unstructured economic data. The report calls attention to the role AI is playing in transforming economic forecasting, shaping behavioral economics, improving econometric models and enhancing public policy such as monetary policy, social programs and climate change mitigation. Although AI offers potential advances in efficiency, speed, and the discovery of finer patterns, its embedding comes with Head 1 3 important challenges concerning data quality, model interpretability, and algorithmic bias. The future of AI in economics will be characterized by the emergence of hybrid econometric-AI models, more long-term macroeconomic applications and further focus on interdisciplinary collaboration as well as robust governance structures for fair and responsible deployment.