This report highlights how AI technologies, including generative AI and machine learning, are being implemented across the pension value chain to enhance efficiency, personalization, and governance while promoting sustainable retirement security.
Report Overview
The 22 largest pension markets globally hold $55.7 trillion in aggregate assets, representing 69% of the GDP of these economies, according to the Thinking Ahead Institute. Despite this vast capital, however, these pension systems face mounting challenges. Aging populations, rising economic inequality, and heightened inflation are creating significant pressures. Many countries are transitioning from defined benefit (DB) plans, where employers guarantee payouts, to defined contribution (DC) plans, which shift financial responsibility to individuals. These demographic and systemic changes demand innovative solutions to ensure sustainable retirement systems. At the same time, advancements in artificial intelligence (AI) and machine learning (ML) now offer opportunities to modernize pension management and address these challenges.
AI technologies — including machine learning techniques, large language models, and AI agents — are rapidly evolving and have the potential to transform how pensions operate. They promise enhanced efficiency, personalization, and decision-making capabilities across the pension value chain. But successful integration requires thoughtful implementation, balancing innovation with trust and ensuring these technologies complement human expertise.
“Pensions in the Age of Artificial Intelligence” identifies where in the pension value chain AI and ML technologies can be implemented to enhance the overall value proposition for investors. It also highlights the risks and challenges associated with integrating new technologies and the key considerations for industry professionals when developing long-term strategies to successfully meet the needs of current and future generations of investors.
CFA Institute conducted interviews with pension experts and industry professionals for this report to identify key areas in which AI can be implemented across the value chain for both DB and DC plans. Our report presents seven representative case studies to showcase current implementations of AI and to illuminate the likely paths forward.
CFA Institute gratefully acknowledges the topic and thought leaders who contributed to this report’s findings and provided the case studies. We would also like to thank the Pensions Expert Panel of CFA Society United Kingdom for providing preliminary feedback for this report.
Key Takeaways
- Enhancing personalization, efficiency, and accuracy: AI applications are diverse, and consideration must be given to how to best use AI to enhance overall retirement security for pension members. Enhancements will require targeting specific areas of the pension ecosystem that will contribute the most value for the unique needs of each pension fund.
- Member engagement and financial literacy: Implementing AI for member onboarding, communications, reporting, and retirement planning could enhance overall member engagement, boost financial literacy, and support pension plan members throughout their retirement life cycle.
- Pension plan governance: AI technologies can enhance pension plan governance by facilitating multistakeholder interactions, reducing administrative tasks, and aiding pension boards with decision making. This includes improving optimization of investment strategies and prompt resolution of member issues.
- Investment Management: AI and machine learning models can boost the analytical capacities of portfolio managers, enhance actuarial analyses of pension fund risks, and keep market trend assessments up to date. These technologies may be especially useful for analyzing private markets and data related to sustainable investments.
- Predictive analytics and actuarial assumptions: Advanced machine learning techniques may enhance actuarial assumptions and predictive analytics, improving asset/liability management and pension derisking strategies. Defined contribution plans may benefit from personalized strategies across the life cycle of each individual investment plan, with accumulation and decumulation strategies based on member behavior predictions.