Matt is a 2009 graduate of the University of Michigan and has worked in multiple roles at Advicent. He loves talking about financial planning and the value it brings both to advisors and to their clients. When not discussing FinTech trends with advisors, he enjoys spending his time outdoors with his wife and their dog.
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I recently had a conversation with an advisor who was trying to compare financial planning tools and was very focused on stress testing their financial plans with Monte Carlo Sensitivity Analysis. The advisor was on a trial of NaviPlan and comparing the results with another competitor. NaviPlan was showing 69 percent success rates compared to 99 percent in the competing product and the difference was producing a large amount of confusion for everyone involved.
Assumptions are an important and often overlooked component of purchasing a planning tool, likely because so often the assumptive properties operate in the background while advisors and their clients tend to focus on results.
However, when you run into such a large difference in output like in my previous example, it is essential that the advisor can easily and effectively audit their plan. As Michael Kitces has pointed out in his "Financial Planning Software Manifesto," advisors need to end the distinction between goal-based and cash-flow based software. This concept is paramount when thinking about the value of Monte Carlo Sensitivity Analysis in any tool.
Monte Carlo, or probability simulation , is used to determine and understand the risk and uncertainty involved in financial management. It is all about applying volatility to rate of return assumptions in your plan. However, advisors need to be able to customize Monte Carlo enough to determine what percentage of a trial should still be considered a success. This is where the marriage of cash-flow and goal-based analysis intersect. In NaviPlan, you are able to adjust deficit coverage (cash-flow) to reflect what a successful trial (goal-based) is for each of your clients. This means that you can build in cushion for each client and each goal. Where a high net worth family may find it tolerable to build in a $10,000 cushion during retirement years, that number may be different for a mass affluent family.
Additionally, Monte Carlo needs to be able to separate out each goal from the plan as a whole. Plan success rates are only so meaningful if retirement success is being dragged down by an education or major purchase goal. You can borrow for college but not for your retirement, so why limit your Monte Carlo analysis reporting to only a plan level? By assigning specific accounts and cash flows to individual goals, you will be able to provide a more complete, customized, and accurate plan to your clients.