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Monte Carlo Simulations for Retirement: Sparking Conversations that Matter

Matt Rogers October 19, 2022

Monte Carlo projections for retirement conversation

When talking about retirement, financial planners may find themselves answering some of the same questions for many of their clients: When can I retire? Do I have enough to fully fund my retirement? When might I need to make lifestyle changes?

Skillfully answering these questions is essential to gaining a client’s trust and confidence in their financial plan. This conversation is also a great opportunity to get clients to add concrete ideas and substance to their retirement plan.

Monte Carlo simulations for retirement planning can be a powerful way to demonstrate how a financial plan will help a client achieve their goals, and in the process, help answer essential questions.

Monte Carlo simulations model the probability of different outcomes in a process that can’t be easily predicted due to the intervention of random variables. In financial planning, the random variable is market performance.

Monte Carlo Solvers for Dynamic Retirement Conversations

A solver for Monte Carlo simulations allows you to quickly view planning scenarios based on adjustments to a single variable at a time. This can lead to much faster and more intuitive conversations about retirement.

With Monte Carlo solvers, financial planners can determine things like retirement age or lifestyle expenses without guessing. They can set a probability of success at 85 percent, for example, run the solver, and immediately see the client’s retirement age and lifestyle budget based on their current plan trajectory.

This can be very powerful when done in a meeting with clients. Let’s say for example that after running a Monte Carlo solver with an 85 percent probability of success, it’s revealed that the client would need to work two years longer or spend $20,000 less per year to reach that Monte Carlo score. The client may say, “What if I work one more year? How much would my expenses have to drop?” The planner can easily edit and “lock” the retirement age solver to increase only one year and run the lifestyle solver to see by how much the client would have to cut their spending.

Quickly solving questions like this as the conversation progresses is a far more dynamic and impactful way of communicating with clients, helping them more easily zero in on their retirement goals, and is much more efficient for the planner.

Representing Monte Carlo Scores as an Age

One traditional drawback to Monte Carlo simulations is the way the results are represented. To a financial planner, a 90 percent probability of success may provide more than enough confidence in a plan, but a client may have difficulty understanding why a 10 percent chance of “failing” is acceptable.

A more clear approach to this is to present Monte Carlo scores as an age—specifically, the age at which the financial plan’s probability of success falls below the desired confidence threshold.

For example, if a Monte Carlo projection shows a client’s plan falls below an 85 percent probability of success at age 93, the planner can tell the client they are confident in their plan until their early 90s, at which point there’s a chance some lifestyle adjustments would be needed.

The way results are represented makes for two very different conversations. There’s a big difference between saying, “I’m confident in your plan until your early 90s” and, “Your plan has an 85 percent probability of success in funding your retirement.”

Clients are more likely to understand an intuitive age metric as opposed to a probability of success that requires further explanation. Simplifying the concept of planning success is a great way to communicate with clients and differentiate yourself.

Monte Carlo Longevity Analyses

Every financial plan has some degree of longevity risk. In other words, the risk that a client will live longer than expected and need more to fund their retirement.

In a traditional Monte Carlo simulation, the probability of success is given at a single age. If a client hears that their plan has an 85 percent probability of success at age 95, it’s natural to then ask “What if I live to 100?”

Without a longevity analysis, there’s no easy way to answer this question. A planner would have to create another Monte Carlo simulation ahead of time to be able to show clients their probability of success.

A Monte Carlo longevity analysis, however, gives a probability of success at every age, letting a client easily see what would happen to their plan if they live longer or shorter than expected. This is a quick way to ease client concerns about funding a retirement that could last several decades.

What’s more, a longevity analysis gives planners and clients greater insight into the potential outcomes of a plan, as well as insight into how soon any planning actions might need to be taken. If a plan has an 80 percent probability of success at age 90, but then the probability of success rapidly declines afterward, that’s an important sign that changes should be made far in advance to ensure the client has what they need in retirement and avoid that “cliff”.

Conversely, if the probabilities of success stay relatively high after the confidence threshold is passed, it’s a great indication that the client has a resilient financial plan and any potential changes could likely be done later in life giving the client and planner time to see how the client’s real world situation plays out.

Longevity analyses provide essential details that a traditional, singular Monte Carlo score does not, and they’re especially important as improving lifestyles make longevity risk a greater threat to financial plans.

The Evolution of Monte Carlo Simulations

Monte Carlo simulations are a powerful tool for sparking retirement conversations that matter. Between solvers, age metrics, and longevity analyses, they’ve come a long way from the static, singular probabilities of success that they used to be.

Continue learning more about Monte Carlo simulations to facilitate better retirement conversations in your practice. Watch our on-demand webinar where we dive deeper into the ways Monte Carlo simulations have changed over time and how you can apply them in your firm. (On-demand version not eligible for CE credits).

 

Or for more on planning innovations, check out some of our latest product enhancements here.

About the Author

Matt Rogers is a Director in the Financial Planning Group at eMoney Advisor, LLC. He has over two decades of experience in financial services spanning sales, marketing, and software development. Matt has published multiple articles and co-authored the Life Insurance chapter of Financial Planning for Medical Professionals. At eMoney, Matt enjoys helping build software to advance the company vision of financial piece of mind for all.

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Welcome to
Heart of Advice

a new source of expert insights for
financial professionals.

Get Started

Tips specific to the eMoney platform can be found in
the eMoney
application, under Help, eMoney Advisor Blog.