Leveraging Deep Learning for Economic Forecasting and Decision-Making: Opportunities and Challenges

EasyChair Preprint 15813

Abstract

The integration of deep learning (DL) techniques into economics has ushered in a transformative era for predictive modeling, decision-making, and policy analysis. This paper explores the role of deep learning in addressing key challenges in economic forecasting, resource allocation, and market prediction. It delves into various deep learning architectures such as neural networks, convolutional networks, and recurrent networks, and their application to economic data sets. We present a detailed comparison between traditional economic models and deep learning-based methods in terms of accuracy, scalability, and computational efficiency. The findings reveal that while deep learning offers substantial improvements in prediction accuracy, it also faces challenges related to data quality, interpretability, and computational demands. This paper concludes by outlining future directions for the convergence of deep learning and economics, proposing methodologies for overcoming existing limitations and enhancing the utility of AI in economic research.

Keyphrases: AI, deep learning, economic, methodology

@booklet{EasyChair:15813,
  author    = {Amir Samavati and James Kung and James Rock and Mohammad Azil},
  title     = {Leveraging Deep Learning for Economic Forecasting and Decision-Making: Opportunities and Challenges},
  howpublished = {EasyChair Preprint 15813},
  year      = {EasyChair, 2025}}