Artificial Intelligence in Financial Portfolio Management : Archival Research based on Secondary Data in form of Relevant Publications.

Abstract

Artificial intelligence (AI) has become an increasingly prominent tool in financial portfolio management, transforming how investment decisions are made. As the financial landscape grows more complex, AI offers opportunities to enhance efficiency and precision in portfolio optimization. This study investigates the historical evolution, current role, and future implications of AI in portfolio management. The research focuses on key AI techniques such as machine learning, deep learning, and predictive analytics, which enable portfolio managers to process vast amounts of data, identify patterns, and improve decisionmaking. An inductive approach, relying on secondary data, was applied, analyzing publications to gather insights into the current state of AI in this field. The findings reveal that while AI enhances portfolio management through real-time data analysis and dynamic strategy adjustment, limitations persist, particularly regarding transparency and model reliability. Despite these challenges, AI continues to revolutionize financial portfolio management, providing tools for more informed decision-making and better risk management. Future research could focus on real-time testing of AI models and further exploring ethical and regulatory concerns in the industry.