Updated on: July 31, 2021

Longevity risk is an essential consideration in any financial plan. It impacts nearly every facet of the plan, and a client’s sensitivity to an increase in longevity is an important measure of a plan’s resiliency. Put simply, many client’s number one concern is outliving their money.

One of the upfront decisions when creating a financial plan is to choose the time-horizon. Often, a planner will opt for a longer end date, projecting a client’s retirement to age 95 or 100. A Monte Carlo analysis is used to represent a plan’s probability of success at this age. This approach is often viewed as a way to take a conservative approach to ensuring a client won’t outlive their assets.

But this conservative approach presents its own challenges. Financial professionals are forced to guess what life expectancy to assume. In most instances, there are a range of life expectancies that are justifiable and defensible. Should financial professionals use the older end of that range? The younger end? What is the difference in Monte Carlo results between the two? Financial professionals have to deal with creating planning strategies based on a score from a singular life expectancy age with little or no insight into how the score could change if another equally justifiable life expectancy were used.

A better approach involves creating a Monte Carlo-enabled comprehensive view of a client’s financial health over their entire life, as opposed to a single year. This allows financial professionals to more accurately and efficiently view the strength of a plan through the lens of longevity risk. The plan becomes dynamic and collaborative—results change over time and that creates powerful insights into the nuances of the plan’s strength.

Accounting for Longevity Takes Time

Clients tend to underestimate how long they or their spouse may live. Financial professionals must account for the fact that human lifespans continue to increase.

Today, a 65-year old couple has a 63 percent chance that one person will survive to 93, and a 40 percent chance that one person will live to 98.¹ This is longer than in generations past. In fact, every generation can expect to live about three years longer than their parents.²

When people live longer, they go through more market cycles and have more years to fund. This has a direct and significant impact on the plan that must be accounted for.

When shown a plan’s probability of success at a given age, clients inevitably ask, “What if I live to 90? What if I live to 105?” The onus is on financial professionals to run multiple plans to create multiple probabilities of success so they can offer clients insight into how increased or decreased longevity impacts their plan. This process also results in a series of singular Monte Carlo scores, each presented separately rather than in a single coordinated, comprehensive visual.

It’s time-consuming for a financial professional to account for both the prospect of increasing longevity and client concerns regarding their plan’s probability of success at different ages. There’s also a piece of the puzzle missing when relying solely on Monte Carlo analysis at a fixed end point.

Creating More Dynamic Conversations with Monte Carlo Longevity Risk

Monte Carlo longevity risk analysis assesses a plan’s probability of success over time, solving the traditional limitations of Monte Carlo using a fixed endpoint. Looking at longevity this way creates a probability of success at every age, revealing trends in a plan’s probability of success over time, as well as a client’s sensitivity to increased longevity. Managing longevity risk in this way is advantageous for several reasons.

By viewing this information as a fluid measure over the course of a client’s life, rather than a static singular value, a financial professional achieves a more comprehensive and valuable understanding of the clients’ plan. Clients will easily understand the meaning of the chart that clearly shows how the likelihood of being able to meet their financial goals typically decreases the longer one lives. Clients may have an inherent understanding of this, but with Monte Carlo longevity risk, they can actually see it.

Armed with a more comprehensive analysis of the client’s plan, financial professionals can then turn the conversation to the client’s—likely longer than expected—lifespan, through actuarial data.

Using these in tandem creates a powerful narrative and helps you prepare your clients for a future that may last longer than they believed possible.

Confidence Age – A New and Intuitive Way to Present a Monte Carlo Analysis

Traditional Monte Carlo analysis outputs a percentage score which can create two challenges. First, many clients, and even some financial professionals, may be unclear on what an acceptable percentage score is for their financial plan. Second, using percentages also creates conversations around how that percentage was calculated, which can lead to overly technical conversations around the underpinnings of Monte Carlo analysis. Confidence Age, a new metric developed by eMoney, could be a great way to minimize those challenges.

Using Monte Carlo Longevity Risk Analysis as its foundation, Confidence Age looks at the Monte Carlo results in a different way. Confidence Age identifies the age at which the Monte Carlo score first dips below whatever percentage score the advisor deems as their confidence threshold. Communicating a Monte Carlo result in the form of an age can result in easier understanding for the client and reduce the need for technical explanations.

Using Confidence Age as a metric, financial professionals can easily communicate to clients they are confident they can meet their goals until a given age. Should they live beyond that age, the likelihood of needing to make lifestyle changes increases with each year. This opens up completely different conversations with clients than with traditional Monte Carlo scores.

When information is displayed differently, it can be explained differently. Using Monte Carlo Longevity Risk Analysis and Confidence Age, financial professionals can create more intuitive and deeper discussions with their clients.

To learn more on this topic, watch our video to see a practical application of Monte Carlo Longevity Risk and Confidence Age in eMoney.

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