Abstract: Effective asset management is essential for making the most of financial resources and reducing operational risks in today's organizations. Traditional asset management systems depend on manual monitoring and fixed rule-based methods. This often leads to slow decision-making, human mistakes, and a lack of real-time information. To tackle these issues, this paper details the design and development of an AI-based Asset Management System that uses an Intelligent AI Agent for automated portfolio monitoring and decision support.
The proposed system reviews user asset portfolios made up of stocks, mutual funds, gold investments, fixed deposits, and savings accounts. It uses machine learning-based forecasting models, intelligent agent logic, and rule-based decision making to assess asset performance, spot risk patterns, and create improved investment suggestions. The system also sends alerts for portfolio imbalances, unusual market activity, and deposit maturity events.
A secure web-based interface allows users to manage asset data, view analytics, and receive AI-driven insights in real time. Experimental testing shows reliable forecasting accuracy, low processing delays, and consistent alert notifications. This solution provides a scalable, affordable, and smart framework for modern financial asset management needs.

Keywords: Asset Management System, Artificial Intelligence, Intelligent Agent, Portfolio Analysis, Investment Forecasting, Financial Decision Support, Alert-Based Monitoring.

Downloads: | DOI: 10.17148/IARJSET.2026.13117