Designing Pensions with AI: A New Approach to Retirement Security

Author: Denis Avetisyan

This research demonstrates how machine learning can personalize pension products by learning optimal investment strategies for diverse risk preferences.

In a decumulation-only scenario, a neural network approach to retirement planning demonstrates markedly different outcomes compared to a provably convergent method, highlighting the potential for significant divergence in long-term financial security.

A novel reinforcement learning framework enables the creation of a family of pension solutions tailored to individual utility functions and varying levels of risk aversion.

Designing optimal pension products requires navigating a complex interplay between individual risk preferences and evolving market conditions, often presenting computationally intractable challenges. This is addressed in ‘Machine-learning a family of solutions to an optimal pension investment problem’, which introduces a neural network framework for efficiently identifying personalized investment and consumption strategies across a spectrum of utility function parameters. By learning a family of optimal solutions, the approach facilitates interactive exploration of pension outcomes and enables rapid adaptation to changing investor needs. Could this machine-learning approach unlock more robust and accessible retirement planning tools for a wider range of individuals?

Navigating the Evolving Landscape of Retirement Security

The shift from Defined Benefit to Defined Contribution plans necessitates tailored investment strategies, demanding accurate modeling of preferences and future needs. Existing models often struggle with intertemporal choice and longevity risk, leading to suboptimal decisions. As illustrated in the accompanying figure, potential retirement outcomes span multiple deciles, highlighting substantial financial insecurity. Achieving a secure retirement requires orchestrating time, risk, and desire in harmonious balance.

The neural network, trained with fixed parameters, produces a distribution of retirement outcomes spanning multiple deciles, indicating substantial variability in potential financial security.

Precision in Preference Modeling for Optimal Outcomes

Effective portfolio optimization hinges on accurately representing investor preferences. Established models, like Epstein-Zin and Exponential Kihlstrom-Mirman, capture risk aversion and diminishing marginal utility. Parameterization, guided by tools like the Risk Questionnaire, translates subjective assessments into quantifiable inputs. The choice of utility function directly impacts the resulting investment strategy and Adequacy Level. This approach achieves prediction accuracy within 1% standard error, demonstrating robustness across market conditions.

A comparison of one-step and fixed network approaches reveals differing consumption and investment strategies for achieving the outcomes presented in Figure 4b, suggesting a potential for optimization through adaptive methods.

Leveraging Machine Learning for Decumulation Efficiency

Machine Learning techniques, specifically Recurrent Neural Networks enhanced with Gated Recurrent Units, offer a computationally efficient solution to the optimal control problem of investment and consumption throughout retirement. This allows for exploration of complex financial landscapes and adaptation to changing economic conditions. The accompanying figure demonstrates the effectiveness of a percentile-based neural network, achieving comparable performance to the primary RNN. This methodology is approximately ten times faster than provably convergent numerical methods while maintaining accuracy.

Retirement outcomes generated by the replacement-ratio percentile neural network demonstrate performance comparable to that of the main recurrent neural network (RNN), indicating the effectiveness of this alternative approach.

Refining Prediction Through Neural Network Estimation

To enhance accuracy, the research employs Mean Estimating and Scaling Networks to estimate the expected value and variance of the Loss Function, improving model calibration. The Log-Sum-Exp Function ensures numerical stability during optimization, mitigating underflow or overflow. The analysis of outcomes, as depicted in the figure, reveals a divergence between two-step iterative approaches and fixed networks, suggesting a pathway for improved financial planning. The percentile network achieves prediction times of approximately 1/3 of a second, a substantial improvement over Monte Carlo simulations.

Analysis of outcomes from Figure 4a shows that a two-step iterative approach to consumption and investment diverges from the strategies produced by the fixed network, suggesting a pathway towards improved financial planning.

Building Resilience into the Future of Retirement Planning

A novel methodology addresses the complexities of retirement planning, moving beyond traditional models. This framework utilizes stochastic modeling and optimization to generate personalized investment strategies that incorporate individual risk preferences, longevity risk, and prediction uncertainty. Simulations demonstrate that individualized strategies consistently outperform benchmarks across scenarios and demographics. This methodology can be extended to alternative savings structures, such as Tontines, to enhance security. By optimizing for both return and risk mitigation, the goal is to create resilient plans that empower individuals to achieve financial well-being throughout their lives.

The pursuit of optimal pension design, as detailed in this study, echoes a fundamental principle of harmonious systems. Just as a well-composed melody balances notes for a pleasing effect, this machine-learning framework seeks equilibrium between investment strategies and individual utility functions. Mary Wollstonecraft observed, “It is time to turn our attention to the cultivation of the female understanding,” and while seemingly disparate, this sentiment mirrors the need for a nuanced approach to financial planning – recognizing that a ‘one-size-fits-all’ solution neglects the complexities of individual needs and risk aversion. The interactive tuning facilitated by this framework allows for a personalized ‘understanding’ of each investor’s profile, moving beyond generalized models toward a more elegant and effective outcome. This careful calibration—where every detail matters, even if unnoticed—creates a system that ‘sings’ with efficiency and responsiveness.

What’s Next?

The pursuit of optimal pension design, framed as a reinforcement learning problem, reveals a quiet truth: elegance in financial engineering isn’t merely about maximizing returns. It’s about distilling complexity into an interface so intuitively understandable that explicit explanation feels redundant. The current work demonstrates a capacity to generate solutions, but a truly refined system would anticipate the user’s implicit needs – a delicate balance between personalization and preservation of capital. The true challenge lies not in creating more parameters to tune, but in reducing them to the essential few.

Further investigation should address the limitations inherent in translating theoretical utility functions into tangible behavioral models. A tontine, elegantly resurrected through machine learning, remains a conceptual exercise without rigorous testing against the messy realities of human decision-making. Exploring the robustness of these learned policies to unforeseen market shocks, or behavioral biases, feels less like technical validation and more like philosophical inquiry.

Ultimately, the field demands a shift in perspective. Refactoring this code isn’t a technical obligation; it’s an art. The goal isn’t simply to solve the pension problem, but to craft a system that quietly, confidently, guides individuals towards a secure future – a solution so transparent, it barely needs to be explained.

Original article: https://arxiv.org/pdf/2511.07045.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/