U.S. state and local public pension systems manage substantial assets, providing essential retirement income to millions of beneficiaries. As Environmental, Social, and Governance (ESG) factors are increasingly integrated into investment strategies, a critical challenge has emerged: how to promote sustainability while safeguarding the long-term solvency of these funds.
An emerging actuarial scholar, Ms. Luokexin Mo, is applying artificial intelligence and advanced simulation techniques to develop practical solutions to this challenge. She proposes to develop an AI-driven analytical platform to evaluate the long-term performance implications of different ESG allocation levels in pension portfolios. Using Monte Carlo simulations, informed by global risk management frameworks and adjusted to U.S. regulatory standards, her approach can project the long-term effects of modest annual return deviations over several decades.
“The ultra-long investment horizon of pension funds makes them highly sensitive to climate transition and social risks,” said Ms. Luokexin Mo. “Even a slight underperformance in annual returns can, over several decades, accumulate into a substantial funding gap—one that ultimately shifts additional costs onto taxpayers.”
Recent data indicate that U.S. public pension systems face significant unfunded liabilities, while the overall U.S. retirement-asset market has grown considerably. Although ESG integration may help mitigate certain long-term downside risks and align with the U.S. Department of Labor’s risk–return guidance, the absence of precise quantitative tools to assess potential return drag remains a critical limitation, one that could further amplify fiscal pressure.
Ms. Mo’s research directly addresses this gap. Her previously developed actuarial software systems—including the Annuity Product Actuarial Dynamic Valuation Intelligent System and the AI Actuarial Analysis System for Variable Interest Rate Insurance Products—form the technical backbone of real-time ESG scenario testing. These tools enable pension fund managers to rigorously balance sustainability objectives with financial soundness, optimize portfolio construction, and potentially significantly reduce intervention costs for the Pension Benefit Guaranty Corporation (PBGC), while also easing budgetary pressures on state and local governments.
Her Annuity Product Actuarial Dynamic Valuation Intelligent System employs recurrent neural networks to simulate cash flows, mortality, and surrender behavior in real time, thereby significantly improving valuation accuracy relative to traditional actuarial methods. This advancement enhances the precision with which long-term liabilities are measured and managed.
Her AI Actuarial Analysis System for Variable Interest Rate Insurance Products addresses one of the most technically challenging problems in actuarial practice: managing long-term liabilities under sustained interest-rate uncertainty. By employing deep reinforcement learning for dynamic asset allocation and hedging, the system significantly improved risk-hedging efficiency and yielded substantial economic benefits and cost savings. This approach demonstrates her shift from static stress testing to more adaptive, forward-looking risk control.
In addition, Ms. Mo’s Embedded Value Assessment Standard Formulation System and Solvency Capacity System International Comparative Analysis Platform elevate her work from product-level modeling to institutional and system-level solvency analysis. By leveraging natural language processing and knowledge graph technology, these systems automate the interpretation of regulatory requirements across jurisdictions, enhance the accuracy and comparability of embedded value assessments, and provide integrated analytical views of solvency regimes such as Solvency II and RBC. In my judgment, this demonstrates a sophisticated understanding of how quantitative models must interface with regulatory and policy frameworks to be effective in practice.
Taken together, these four systems form a coherent technical progression—from precise product-level valuation, to dynamic risk hedging, to standardized financial assessment, and finally to cross-regulatory solvency comparison. This progression reflects not only technical excellence but also a rare systems-level understanding of long-horizon financial risk, which is directly relevant to pension and retirement systems. Importantly, these systems are not academic prototypes; they have been licensed and applied in operational settings, producing measurable improvements in accuracy, efficiency, and economic outcomes.
Ms. Luokexin Mo’s approach deeply integrates dynamic scenario modeling with AI-based forecasting, providing regulators, pension fund boards, and policymakers with clear, actionable insights. Her work not only helps prevent systemic solvency risks, but also advances responsible investment practices—ultimately safeguarding the benefits of millions of retirees and potentially saving taxpayers hundreds of billions of dollars in future expenditures.
Ms. Mo has been awarded full funding for the Ph.D. program in Economics at Aarhus University in Denmark, a highly regarded institution in business and economics. Her doctoral research focuses on quantifying the impact of ESG allocations on pension fund solvency. This international recognition underscores the global importance of the issue, and her findings are expected to provide empirical support for improvements to U.S. ERISA policy and for enhancing climate-related financial risk assessment standards.
At a time when climate-related financial risks are receiving heightened attention from the U.S. Government Accountability Office and the Department of Labor, Ms. Mo’s research is particularly timely. If her AI-driven tools are adopted at scale, they could materially strengthen the resilience of the U.S. retirement system, enabling pension funds to pursue sustainable investment strategies with greater confidence without compromising long-term beneficiary security. Such advancements would not only help alleviate existing funding shortfalls but also position the United States as a technological leader in global sustainable finance, promoting more robust intergenerational economic equity.
This cutting-edge work demonstrates how advanced actuarial techniques can serve as a critical bridge between the green transition and pension security, injecting lasting resilience into a pillar of national economic stability.
Company： AVIVA-COFCO
Contact Person: Luokexin Mo
Web: https://www.linkedin.com/in/luokexinmo-40b529168/
Email: moluokexinv@gmail.com
City： Beijing
Disclaimer: The content of this article is intended for informational purposes only. The article does not constitute financial, investment, or legal advice. Readers should conduct their own research or consult with a qualified professional before making any financial or investment decisions. The application of AI-driven actuarial tools, as discussed, may not guarantee specific outcomes or returns.
