Artificial intelligence and investment management: Structure, strategy, and governance

Abstract

Artificial intelligence (AI) is transforming investment management by enabling data-driven decision-making across portfolio optimization, forecasting, risk assessment, advisory services, and regulatory compliance. Despite growing interest, much of the existing research remains fragmented, focusing narrowly on specific techniques or domains. This study addresses that gap through a comprehensive and systematic literature review of 178 peer-reviewed studies indexed in Scopus, covering the structure, strategy, and governance of AI applications in investment management. Adopting established review protocols and integrating thematic and visual analysis, the review categorizes the literature into six grand themes: AI-driven portfolio management and optimization; AI in financial forecasting and market prediction; AI-powered robo-advisory and personalized financial services; AI for risk management, fraud detection, and regulatory compliance; AI and strategic investment decision-making; and enablers and constraints in AI adoption. Findings reveal that AI is not just enhancing operational efficiency but also reconfiguring strategic and institutional practices. The review identifies key research gaps related to explainability, adoption barriers, and ethical concerns, and offers an integrated framework to guide future research, policy design, and industry adoption. By synthesizing a diverse body of work, this study advances our understanding of how AI is reshaping the architecture and dynamics of modern investment management.

Keywords Artificial intelligence, Investment management, Risk assessment, Market prediction