Optimizing Long-Term Savings: Pension Design, Communication, and Fintech Application

Customers of pension plans often rely on financial advice to make investment decisions. This article proposes a pension plan design that improves both optimization and communication of retirement savings. Unlike conventional approaches, we advocate setting only an upper limit on desired income in the payout phase, while allowing the allocation to risky assets to remain unconstrained. Simulation results show that this design stabilizes both the average and volatility of pension payouts without imposing inefficient restrictions on early investment choices. To enhance client understanding, we introduce a set of decision criteria for retirement drawdowns that supports simple, intuitive interaction and builds trust. Our approach is fully implementable in artificial intelligence (AI)-driven wealth management systems, enabling fintech applications to guide clients effectively and assisting human advisors in providing better, data-driven insights. The proposed framework balances risk-adjusted returns and client comprehension, offering a practical, evidence-based solution for improving long-term retirement outcomes.

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Publication Details

Journal
North American Actuarial Journal
Published
2026-06-16
DOI
https://doi.org/10.1080/10920277.2026.2678183
Primary Topic
Financial Literacy, Pension, Retirement Analysis
Type
article
Field-Weighted Citation Impact
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article

Optimizing Long-Term Savings: Pension Design, Communication, and Fintech Application

Catalina Bolancé, Michael Scholz, Montserrat Guillén, Paulina Roszkowska et al.
North American Actuarial Journal
Financial Literacy, Pension, Retirement Analysis
article

Optimizing Long-Term Savings: Pension Design, Communication, and Fintech Application

Catalina Bolancé, Michael Scholz, Montserrat Guillén, Paulina Roszkowska, Jens Perch Nielsen
article en

Abstract

Customers of pension plans often rely on financial advice to make investment decisions. This article proposes a pension plan design that improves both optimization and communication of retirement savings. Unlike conventional approaches, we advocate setting only an upper limit on desired income in the payout phase, while allowing the allocation to risky assets to remain unconstrained. Simulation results show that this design stabilizes both the average and volatility of pension payouts without imposing inefficient restrictions on early investment choices. To enhance client understanding, we introduce a set of decision criteria for retirement drawdowns that supports simple, intuitive interaction and builds trust. Our approach is fully implementable in artificial intelligence (AI)-driven wealth management systems, enabling fintech applications to guide clients effectively and assisting human advisors in providing better, data-driven insights. The proposed framework balances risk-adjusted returns and client comprehension, offering a practical, evidence-based solution for improving long-term retirement outcomes.

North American Actuarial Journal
Joanneum Research (AT), City, University of London (GB), SGH Warsaw School of Economics (PL), Cambridge Econometrics (United Kingdom) (GB), University of Klagenfurt (AT)
Openalex Percentile: Top 3%
Financial Literacy, Pension, Retirement Analysis
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Optimizing Long-Term Savings: Pension Design, Communication, and Fintech Application — Catalina Bolancé, Michael Scholz, et al. · North American Actuarial Journal (2026) | TGRS Research Map | TGRS