Abstract

The study explores the opportunities, risks, and regulatory challenges associated with the use of artificial intelligence (AI) in asset management. Its aim is to provide a thorough analysis, based on evidence, of how AI-supported strategies can transform performance, risk control, and market stability. Methodologically, the work combines a systematic literature review, a quantitative analysis of over 120 AI funds, and in-depth case studies of leading asset managers. The results show that AI can generate significant alpha contributions in liquid equity markets, while there are data-related limitations in illiquid bond segments. It points out that model instability, low explainability, and fragmented data infrastructures can exacerbate regulatory risks. In response to these challenges, the study underscores the need for stronger transparency obligations, advocating for the creation of consolidated data feeds to ensure ethical considerations in AI-driven asset management.