CFA Institute Equips the Investment Sector to Navigate AI Developments
18 November 2025

Responding to the rapid uptake of artificial intelligence in the investment sector, CFA Institute today announced the release of AI in Asset Management: Tools, Applications, and Frontiers, a comprehensive new resource created to help investment professionals adopt, adapt, and succeed in an era of technological change.
The publication seeks to help practitioners understand the tools, capabilities, and responsibilities that come with AI adoption. It explores the impact of artificial intelligence and machine learning on investment strategy and professional practice, highlighting the need to balance innovation with transparency, accountability, and ethical governance across portfolio management, risk analysis, and trading. It builds on the 2023 “The Handbook of Artificial Intelligence and Big Data Applications in Finance”, published by the CFA Institute Research Foundation.
Leading industry and academic experts in the field of AI and data science authored chapters, bringing both academic rigour and hands-on experience. The volume covers all major branches of AI, giving practitioners a toolkit of methods and frameworks they can adapt directly to their own workflows.
Mona Naqvi, Managing Director, Research, Advocacy, and Standards at CFA Institute, said:
“Artificial intelligence is challenging us to rethink long-held assumptions about how we create, measure, and deliver value. By moving beyond theory to real-world implementation, AI in Asset Management shows how AI can supplement professional judgement.”
“We seek to equip investment professionals with the knowledge and ethical frameworks needed to integrate AI responsibly. CFA Institute has long helped the profession recalibrate through change, ensuring that new technologies are applied based on ethical frameworks and human judgment. The examples in this volume invite practitioners to think critically and experiment with these tools, while keeping ethics and investor trust at the core.”
Practical examples are relevant for portfolio managers, analysts, quantitative researchers, and institutional leaders deploying AI in investment contexts. Practitioners will learn how to:
- Identify where AI adds demonstrable value beyond traditional quantitative methods.
- Integrate machine-learning pipelines into existing investment processes.
- Balance automation with human oversight, while maintaining governance and accountability.
- Evaluate and manage new sources of risk introduced by complex algorithms built into investment models.
- Use deep-learning approaches for trading and risk management, enabling more adaptive and data-driven decision processes.
Companies in this story: CFA Institute