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.
Guillén et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: