AI-Driven Financial Management Optimizing Investment Portfolios through Machine Learning

Financial management has generally focused on static and rule-based solutions to optimize investment portfolios, which can lead to inefficiencies and increased risk exposure in volatile markets. To address these issues, the study provides an AI-powered financial management system that uses advanced Machine Learning (ML) techniques for portfolio optimization. The system uses real-time data evaluation and predictive modeling to dynamically change investment allocations and risk profiles in response to market conditions. The proposed system collects and preprocesses large amounts of financial data, trains ML models to detect trends and investment possibilities, and includes a robust portfolio rebalancing mechanism. Compared to traditional techniques, the AI-powered technique seeks to optimize returns while limiting risk through rapid and data-driven decision-making. According to the results of the overall performance evaluation, the proposed system outperforms existing systems in terms of common annual ROI (12.5%), Sharpe Ratio (1.2), and maximum drawdown (-5.2%). It displays superior overall performance throughout a variety of market circumstances, including bull, bear, and stagnant markets.