A seatbelt reduces your risk of dying in a car accident dramatically. A full racing harness reduces it further. A reinforced cage around the driver reduces it further still. At some point the safety equipment becomes more burdensome than the risk it addresses, and you are spending forty minutes getting in and out of your own car. The question was never whether more protection is theoretically better. It is whether the cost of that protection is worth paying.
We accept this trade-off instinctively in everyday life. In wealth planning, we have not quite managed it.
The quiet demand that enters many retirement conversations is for a 0% failure rate. A guarantee, written in numbers, that the money will outlast the person. It is a deeply human instinct and a mathematically treacherous one. Because here is what the projections rarely show: the cost of chasing that last fraction of certainty is always real, always paid, and almost never worth it.
The Number You See Is Not the Life You Will Live
Most plans are built around a single projected return, a median estimate of what a portfolio might do across several decades. The trouble is that nobody actually experiences the median. Returns arrive as a wide range of outcomes shaped by market cycles, inflation, geopolitical shocks, and the particular order in which events unfold. That last part matters more than most people realise. Two portfolios with identical average returns over thirty years can produce very different results depending entirely on whether the bad years came early or late. A retiree who encounters a severe downturn in the first few years of drawing down is in a fundamentally different position from one who encounters the same downturn a decade in (even if the long-run numbers look the same on paper).
Tools like Monte Carlo simulation and historical backtesting exist precisely to surface this reality. Rather than showing one projected future, they model many, running a plan through a wide range of conditions to see how it holds up. They are more honest than a straight-line projection. But they are widely misread, which is where much of the anxiety begins.
How failure is defined matters enormously, and it varies more than most people realise. In some frameworks (such as William Bengen’s foundational safe withdrawal rate research, which remains one of the most cited), failure means genuine portfolio depletion: running out of money entirely before the end of a defined retirement period. That is a serious outcome by any measure, and the concern is legitimate. In goals-based planning, however, failure is often recorded whenever a target spending level is missed by any amount, even briefly. In that context, a one-dollar shortfall and a complete collapse can be logged the same way. The word “failure” can therefore be doing very different amounts of work depending on the model.
Beyond 95%, Every Percentage Point Costs Dramatically More Than the Last
This matters enormously when we consider what it actually costs to push that number toward 100%. The relationship between additional savings and improved odds is not a straight line. Early capital has a dramatic effect on a plan’s resilience. Each subsequent addition does progressively less. By the time you are pushing from 95% toward 100%, you are no longer buying protection against ordinary market downturns. You are buying insurance against events that most living investors have never witnessed (very prolonged depressions, currency meltdowns, decade-long stagnations). You are paying huge premiums to protect against tiny possibilities.
And that capital has to come from somewhere. It comes from working longer, spending less, or both. These are not abstractions. They are years spent hesitating, trips not taken, life changes deferred, or a decade of your best health exchanged for protection against a scenario that in all likelihood will never arrive. The plan that promises zero failure may well succeed in ensuring your money outlives you. Which may be, in its own quiet way, also a failure.
The Model Assumes You Have No Judgment
There is a further flaw in how these models work, and it compounds everything above. Standard simulations assumes the retiree will behave like a machine. If the market falls 40% and stays there, the model keeps drawing the same monthly amount, unchanged, until the account hits zero. No pause. No adjustment. No response to circumstances.
The spreadsheet retiree watches the house burn down and carries on ordering new furniture.
Real people do not behave this way. They skip a year of travel. They hold off on the renovation. They pick up some part-time work, draw from a different source, or simply spend a little less until conditions improve. This unglamorous adaptive capacity is more effective than almost any financial product at managing long-run risk, but it is entirely invisible to the model.
Research by Jonathan Guyton and William Klinger puts numbers to this intuition. They showed that plans where spending adjusts in response to portfolio performance, rather than rolling forward blindly, can support meaningfully higher starting withdrawal rates while still holding up over long retirements. A plan that can bend will rarely break. But even their approach has limits worth understanding. Their framework applies fixed rules: if withdrawals exceed a set threshold, cut spending by a defined percentage. It is more thoughtful than a static plan, but it is still a predetermined response to a predetermined trigger.
Real judgment is something different. A person assesses that a market downturn at 68 is not the same problem as one at 80. They factor in family circumstances, a potential property sale, and a change in their own needs. They read the moment in ways that no ruleset can anticipate. Demanding a 0% failure rate of the math is, in part, a complete refusal to trust in the common sense of the retiree.
We Are Using the Wrong Word
There is a language problem at the heart of all this. The word “failure” conjures catastrophe, like an elderly person unable to meet basic living expenses. But in the model, failure usually means something far more mundane: a plan that needed a modest adjustment somewhere across a thirty-year stretch. Collapsing that entire range into a single alarming word has done considerable damage to how people make decisions and, more concretely, how many years they feel they need to keep working.
What if we stopped calling it a “Failure Rate” and started calling it an “Adjustment Probability”?
A 10% adjustment probability means there is roughly a one-in-ten chance that at some point over three decades, you will benefit from spending a little less for a while. That is a guardrail, not a cliff. And the difference in how we respond to those two images is not trivial. One triggers a drive to accumulate more at almost any personal cost, while the other invites a measured and far less painful recalibration.
Behavioural research has often found that the fear driving most of this fear of total ruin is considerably less common in practice than the quieter regret people actually report—running out of life with money left over that they were too cautious, too tired, or too unwell to spend. The profession has spent decades building tools to solve the first problem. It has paid far less attention to the second, despite the evidence that among people with genuine means, it is the more prevalent one.
What a Good Plan Actually Looks Like
None of this is an argument for recklessness. A plan needs to be sound, and having enough capital genuinely matters. There is no substitute for a solid base. But “enough” and “as much as mathematically possible” are very different destinations, and (not to belabour the point) the road between them is measured in years of additional work and in dreams unfulfilled.
The plan that ends well is not the one with coffers necessarily overflowing. It is the one where resources were used thoughtfully, where the balance between security and living was struck with open eyes rather than defaulted into through fear. For those who wish to leave something behind for the next generation, that too should be a deliberate choice, sized and planned for, not an accidental surplus produced by excessive caution.
The only truly successful plan is the one that is safe “enough” and enables the pursuit of your ikigai. Everything else is just a rounding error in a simulation.
References:
- Bengen, W., “Determining Withdrawal Rates Using Historical Data,” Journal of Financial Planning (1994);
- Guyton & Klinger, “Decision Rules and Maximum Initial Withdrawal Rates,” Journal of Financial Planning (2006);
- Tharp, D., “Fat Tails and Monte Carlo Analysis,” Kitces.com (2019).
This is an original article written by Bryan Chan, Lead of Solutions at Providend, the first fee-only wealth advisory firm in Southeast Asia and a leading wealth advisory firm in Asia.
For more related resources, check out:
1. Why the Reliability of Income Is More Important Than Investment Returns in Retirement
2. RetireWell® Part 1: Drawing Down Retirement Money
3. The Principles Behind RetireWell®
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