Artificial Intelligence in Investment Management: A Literature Review of Models, Markets, and Mandates

Artificial Intelligence in Investment Management: Models, Markets, and Mandates

Artificial intelligence is becoming an important part of modern investment management. Its role is no longer limited to experimental trading models or isolated research tools. AI is now being applied across market research, asset pricing, portfolio construction, risk monitoring, client communication, reporting, and advisory workflows.

This whitepaper reviews the role of artificial intelligence in investment management through three connected lenses: models, markets, and mandates.

The first lens is models. It examines how machine learning, deep learning, natural language processing, large language models, reinforcement learning, and agent-based systems are being used to process financial information, identify patterns, support forecasting, analyze portfolios, and structure investment decisions.

The second lens is markets. It considers how AI is being applied across public equities, fixed income, funds, digital assets, private markets, alternative data, and multi-asset investment environments.

The third lens is mandates. It reviews the responsibilities that come with using AI in financial decision-making, including explainability, governance, suitability, conflicts of interest, data quality, risk controls, compliance, and human oversight.

The central argument of this paper is that AI should not be understood only as a prediction engine. In investment management, the more important question is how AI can become part of a responsible decision-support system.

Models alone are not enough. Investment management requires context, constraints, risk awareness, client objectives, regulatory discipline, and clear reasoning. AI can improve parts of the investment workflow, but its value depends on how well it is integrated into a structured investment process.

This paper therefore approaches AI not as a replacement for investment judgment, but as a new support layer that can strengthen research, portfolio understanding, risk review, advisory interaction, and more informed decision-making when designed with the right controls.

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