Lifetime Incomes in a DC World

As interconnected risks grow more complex, insurance must shift from reactive measures to frameworks that are inherently adaptive, preventive, and sustainable. This abstract introduces a forward-looking system designed to embed sustainable preventive solutions directly within insurance—a modular, credibility-based approach that dynamically manages and mitigates risks in real time. Building on established actuarial credibility theory and adaptive learning, this framework enhances predictive reliability by blending historical data with current observations and seamlessly integrating expert judgment. Unlike conventional AI/ML algorithms that often rely exclusively on data, this approach respects the critical role of expert insights, allowing nuanced adjustments that mirror life’s layered and unpredictable patterns. To deliver real-time, personalized preventive guidance effectively, the system incorporates engagement-driven credibility weighting that adapts based on user behavior, as well as context-sensitive adjustments that respond to changing external conditions. Through a modular, interdisciplinary framework, HRS integrates psychological, environmental, and traditional actuarial data, fostering a comprehensive and sustainable engagement with evolving risks. By combining adaptive data science with credibility-enhanced techniques, this framework redefines risk management as both structured and agile, offering an insurance solution that is data-driven yet responsive to human expertise—a harmonious balance that supports sustainable risk management in a world inherent with vibrant change, embracing unpredictability responsibly.

This study examines the return on investment of pension funds in Brazil using Return-Based Style Analysis (RBSA). While there is an extensive body of literature on RBSA in the mutual fund industry, its application to pension funds remains limited, mainly due to challenges related to data availability. Additionally, research focusing on investment strategies in emerging markets is relatively scarce. This study seeks to fill these gaps by using a panel dataset from Brazilian regulator (PREVIC), with 1,138 plans from 2015 to 2023. The dataset includes nine asset classes that are relevant under current regulations. Our findings reveal significant differences in asset allocation strategies across pension funds. Larger pension funds tend to generate higher returns, while defined benefit plans outperform other plan types in terms of returns. Interestingly, entities classified as systemically important report lower returns compared to those that are not categorized as such. These results contribute to a deeper understanding of pension fund investment dynamics in emerging markets and offer valuable insights for policymakers, regulators, and investors.

Lifetime pension pools—also known as group self-annuitization plans and tontines—allow retirees to convert a lump sum into lifelong income, with payouts linked to investment performance and the pool’s collective mortality experience. Existing literature has predominantly examined basic investment strategies like constant allocations and investments solely in risk-free assets. Recent studies, however, proposed volatility targeting, aiming to enhance risk-adjusted returns and minimize downside risk. Yet they only considered investment risk in the volatility target, neglecting the impact of mortality risk. This presentation thus aims to address this gap by investigating volatility-targeting strategies for both investment and mortality risks, offering a solution that keeps the risk associated with benefit variation as constant as possible through time. Practical investigations of the strategy demonstrate the effectiveness and robustness of the new dynamic volatility-targeting approach.