Accelerating BRICS Economic Growth: AI-Driven Data Analytics for Informed Policy and Decision Making

• Published in: Journal of Economics, Finance and Accounting Studies Finance and Accounting Studies, 2024-12, Vol.6 (6), p.102-115
• Main Authors: Abir, Shake Ibna, Mohammad Hasan Sarwer, Mahmud Hasan, Nigar Sultana, Md Shah Ali Dolon, S M Shamsul Arefeen, Abid Hasan Shimanto, Rafi Muhammad Zakaria, Sarder Abdulla Al Shiam, Tui Rani Saha
• Format: Article
• Language: English
• ISSN: 2709-0809; 2709-0809
• DOI: 10.32996/jefas.2024.6.6.8

This paper analyzes how the Artificial Intelligence (AI) and Machine Learning (ML) are bridging the gap between economic growth in the BRICS countries. BRICS countries are emerging economies that are challenged by increasing income inequality, industrial transformation and the need for infrastructure development. Driven by AI, this study applies data analytics to macroeconomic datasets, tracking down patterns and functional takeaways regarding policy formulation and strategic decision making. The research employs techniques, including predictive modeling, clustering, and natural language processing (NLP), in areas such as trade optimization, resource allocation and labour market analysis. Case examples document successful introduction of AI systems to solve critical economic problems, from increasing healthcare access to raising productivity in agriculture. The findings illustrate the role of AI and ML in helping BRICS policymakers to an informed, data driven development. The research puts AI as core to the process of economic advancement, a solution to developmental gaps and a driver for growth. This research contributes both to its practical outcomes and by providing insights into how AI and ML can solve the complex economic problems of emerging markets. The paper introduces predictive modeling, which anticipates economic trends based on past data and clustering which groups similar economic behaviors to find patterns as tools that are important in economic analysis. Further, Natural Language Processing (NLP) is covered as a highly effective approach to understand policy documents, news, and unstructured data to improve the ability to make decisions. By helping students, researchers, and policymakers understand these AI powered techniques that optimize trade, resource management and labor, these scalable solutions to sustainable development are available. This study touts data driven innovation as a critical means to solve global challenges, well-equipped readers with the skills and knowledge to leverage AI for economic progress in a geography of the dynamic and connected.