Leveraging Agentic and Generative AI for Intelligent Global Market Analysis
Faculty Mentor
Dr. Md Shohel Rana
Location
Russell Union Room 2054
Type of Research
Proposed
Session Format
Oral Presentation
College
Allen E. Paulson College of Engineering & Computing
Department
Information Technology
Abstract
Global financial markets produce enormous amounts of structured and unstructured data on a per-second basis. The data is comprised of economic data, news feeds, social media data, and geopolitical data. Traditional approaches to financial market analysis have difficulties integrating these data sources and adapting to the changing global environment. This project aims to create a novel framework for autonomous financial market intelligence using Agentic AI and Generative AI technologies. Agentic AI is an emerging technology that allows for the creation of autonomous decision-making agents. The agents can be programmed to continuously monitor and analyze data sources, detect emerging trends, and adjust strategies accordingly. In the proposed framework, intelligent agents will be utilized to continuously gather and interpret global financial market data. The data will be processed and synthesized using generative AI to create explainable reports. The proposed architecture is based on the integration of multi-agent coordination, adaptive learning, and human-in-the-loop approaches to create an autonomous financial market intelligence system. The system is not intended to replace human decision-makers but to complement strategic decision-making. The system can potentially accelerate pattern recognition and scenario generation. This research aims to showcase the potential of integrating autonomous AI agents with generative AI to create adaptive, explainable, and scalable financial market analysis tools. The proposed framework is expected to open new avenues for intelligent financial forecasting, risk assessment, and economic intelligence.
Program Description
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Start Date
4-23-2026 11:45 AM
End Date
4-23-2026 12:00 PM
Recommended Citation
Rahman, Anichur; Aishi, Airin Afroj; and Rana, Md Shohel, "Leveraging Agentic and Generative AI for Intelligent Global Market Analysis" (2026). GS4 Student Scholars Symposium. 130.
https://digitalcommons.georgiasouthern.edu/research_symposium/2026/2026/130
Leveraging Agentic and Generative AI for Intelligent Global Market Analysis
Russell Union Room 2054
Global financial markets produce enormous amounts of structured and unstructured data on a per-second basis. The data is comprised of economic data, news feeds, social media data, and geopolitical data. Traditional approaches to financial market analysis have difficulties integrating these data sources and adapting to the changing global environment. This project aims to create a novel framework for autonomous financial market intelligence using Agentic AI and Generative AI technologies. Agentic AI is an emerging technology that allows for the creation of autonomous decision-making agents. The agents can be programmed to continuously monitor and analyze data sources, detect emerging trends, and adjust strategies accordingly. In the proposed framework, intelligent agents will be utilized to continuously gather and interpret global financial market data. The data will be processed and synthesized using generative AI to create explainable reports. The proposed architecture is based on the integration of multi-agent coordination, adaptive learning, and human-in-the-loop approaches to create an autonomous financial market intelligence system. The system is not intended to replace human decision-makers but to complement strategic decision-making. The system can potentially accelerate pattern recognition and scenario generation. This research aims to showcase the potential of integrating autonomous AI agents with generative AI to create adaptive, explainable, and scalable financial market analysis tools. The proposed framework is expected to open new avenues for intelligent financial forecasting, risk assessment, and economic intelligence.
