Real-Time Collaborative Financial Planning And The Evolving Role Of The Financial Advisor

Author: Michael Kitces

Team Kitces

Michael Kitces is Head of Planning Strategy at Buckingham Strategic Wealth, which provides an evidence-based approach to private wealth management for near- and current retirees, and Buckingham Strategic Partners, a turnkey wealth management services provider supporting thousands of independent financial advisors through the scaling phase of growth.

In addition, he is a co-founder of the XY Planning Network, AdvicePay, fpPathfinder, and New Planner Recruiting, the former Practitioner Editor of the Journal of Financial Planning, the host of the Financial Advisor Success podcast, and the publisher of the popular financial planning industry blog Nerd’s Eye View through his website Kitces.com, dedicated to advancing knowledge in financial planning. In 2010, Michael was recognized with one of the FPA’s “Heart of Financial Planning” awards for his dedication and work in advancing the profession.

Read all of Michael’s articles here.

The inspiration for today’s blog post is a recent book I have been reading entitled “Scenario Selling: Technology and the Future of Professional Selling” by former advisor and entrepreneur Patrick Sullivan and psychologist Dr. David Lazenby. While the title may suggest the book is about “sales”, make no mistake - this is one of the most compelling books you'll read all year that makes you rethink how you deliver financial planning to clients. It provides a fascinating glimpse into the future intersection between technology and the human delivery of financial advice – and an especially prescient one, as the book was written in 2006 but remains as relevant as ever (or even more so!) in today's environment!

Complicated Versus Complex Problems And Financial Planning Simulations

As Sullivan and Lazenby point out, in the world of problems, not all are created equal. Some problem are relatively simple, and often you can “just do it” and figure it out as you go. Other problems are more complicated, and greater information, or some outright expertise is necessary; the problems are solvable, but there may be a lot to learn to solve them correctly. The unique distinction about simple or even complicated problems, though, is that ultimately the solutions can be known or figured out, at least once the requisite information is gathered and knowledge can be applied.

By contrast, some problems are not merely complicated, they’re actually complex. The key variables to the situation are interrelated, and changes to some may alter the others, which introduces a level of unknowable uncertainty to the outcomes that can’t be solved by logic alone, and instead experience begins to drive the decision-making process. For instance, as Sullivan and Lazenby point out, you don’t learn to ride a bicycle by just reading about it, and you (usually) don’t buy a sports car based on statistics alone; in both cases, you have to actually experience them (ride the bicycle, or take the car for a test drive) to get through the process.

In the extreme are “hyper-complex” problems, that require both a high degree of knowledge and experience to effectively solve (or at least navigate). For example, whether it’s heart surgery or flying an airplane, it’s not enough to have some with “just” good book knowledge, or someone with “just” experience but no knowledge of the underlying systems and structures; ultimately, you need someone with both, and a feedback system to know how you’re progressing towards a not-entirely-knowable future.

As Sullivan and Lazenby explain, the reason why it matters to characterize the types of problems we face is that the solutions to those problems vary as well. Simple problems can be solved by just doing it, while complicated problems require study first. Complex problems can be tackled by experimenting and learning from experience. But hyper-complex problems require something more – there’s too much unknown to just study it, and it’s not practical to learn from experience, so instead it’s necessary to simulate outcomes and try to learn by seeing how a wide range of alternative scenarios might play out in order to choose a path amongst them.

Source: Scenario Selling: Technology and the Future of Professional Selling by Sullivan & Lazenby

Collaborative Planning To Solve Hyper-Complex Problems

The reason it’s important to understand these distinctions in the types of problems is that the role of other people to help in solving those problems varies by the complexity level:

With simple problems, often no help is needed. Simple problems are essentially “do it yourself” problems (or the kinds of things that can be looked up).

Complicated problems often entail the role of a consultant to find a solution. The answers can be learned, but it may just be easier to hire and pay an expert who’s already studied it, can use tools to do the appropriate analysis, and can identify and explain/provide the solution.

In the case of complex problems, the level of uncertainty means there often is no single right answer to know and deliver. Analytical tools are often put aside, and the role of the helper is more like a counselor; someone to help you think through the situation and help yourself.

With hyper-complex problems, though, even just a counselor isn’t enough, because the challenges are so complex that it’s not enough to just think them through, and it’s not always feasible to just try them out due to a lack of knowledge. Instead, the role of the helper is to be a collaborator, to use tools to help simulate the problem together and analyze and think through the prospective scenarios and their potential outcomes.

Source: Scenario Selling: Technology and the Future of Professional Selling by Sullivan & Lazenby

An important key here is that hyper-complex problems cannot be solved by technology tools alone; there may be too much information to model, too much uncertainty for concrete outcomes, and technology lacks the experience and “street smarts” to be properly applied to individual human circumstances. In an environment where some advisors feel threatened by the “rise of the robo-advisor” and more simply the danger that internet search and web-based tools could make them irrelevant, technology may help solve simple and complicated problems, but the hyper-complex remains in the unique domain of requiring (expert) human assistance as well and may represent the last bastion in the world of advice that technology cannot replace.

On the other hand, it’s notable that trying to solve hyper-complex problems with “just” a consultative approach doesn’t work either. When problems are “merely” complicated, but can be solved with sufficient knowledge, information, and analysis, it’s possible to gather data, analyze it, come up with a solution, and provide a recommendation (the typical financial planning process!). However, with hyper-complex problems a “take the information, analyze it, and come up with the solution” approach can actually be remarkably ineffective. Instead, a more engaging and collaborative approach is necessary.

Banishing The “Wizard Of Oz Effect” And Real-Time Collaborative Planning

According to the CFP Board Practice Standards, the 6-step financial planning process entails Establishing the client relationship, Gathering data, Analyzing the data, Developing recommendations, Implementing recommendations, and then Monitoring. In practice, this typically means having 1-2 meetings with the client to establish the relationship and gather data, followed by a break in time when the advisor analyzes the data and develops recommendations (inputs information into the software, evaluates the results, and produces “the plan”), before ultimately scheduling another meeting with the client to deliver the recommendations for implementation. Or as Sullivan and Lazenby put it, the advisor gathers the information with the client, and then disappears like the Wizard of Oz behind the curtain (to number-crunch with their computer!), before grandly re-emerging to provide their advice.

The problem with this “Wizard of Oz Effect” is that it removes the client from the process at what is actually one of the most crucial points. Were the problem merely complicated, the planner could indeed take the information, analyze it, and like a good “consultant” deliver the recommendations. But when the problem is more complex – like real-world financial planning situations – it’s not enough to just study the numbers and provide a recommendation, because of the natural trade-offs entailed in real-world financial planning situations. Deciding to save more for the kids’ college funds means pushing retirement later. Trying to avoid delaying retirement means saving more now. Saving more now means changing spending plans. Giving up on spending requires a commitment that is hard to justify unless the client has buy-in to the plan. And it’s hard to have buy-in to the plan when the client wasn’t a part of crafting it in the first place, because the advisor did it on the other side of the curtain.

The traditional approach to solving this challenge is to try to anticipate the different types of “what-if” scenarios the client might want to view… an approach which occasionally works, but often just leaves clients asking for additional versions of the plan, planners who are frustrated by the number of iterations that clients keep asking them to run, and ultimately ends with clients who once again don’t have real buy-in to the solution by the time the advisor comes back with the 7^th version of the plan (or worse, the advisor refuses to keep running different versions of the plan and the client is cut off before being able to arrive at a decision at all).

As an alternative to solve this problem, Sullivan and Lazenby advocate a collaborative planning approach, where the advisor does not disappear behind the Wizard-of-Oz curtain to do the number-crunching analysis, but instead uses the software live, interactively, on the spot, with the client – a feat that is actually quite feasible in today’s age of high-powered computers and relatively inexpensive 60” television/monitors. After all, think of a typical situation where a client asks for a plan, and then starts asking “What if I retire at 64 instead of 62?” “What if I save another $500/month instead?” “What if I retire at 64 and save another $500/month?” “What if…” Instead of going back to the computer with each scenario to analyze the situation, re-run the reports, and bring back to the client to discuss, it was simply there on a big-screen monitor to make adjustments on the fly and discuss the implications and trade-offs of each decision?

Notably, for much of its history financial planning software tools weren’t even capable of such on-the-fly planning – and in point of fact, Sullivan and Lazenby wrote their book in 2006(!) and at the time had such difficulty finding software tools, their made their own visual interactive financial planning software Scenario Now to allow for a more interactive and collaborative financial planning process. But increasingly, financial planning software tools are evolving to provide such capabilities, from easier data gathering input workflows to a better user interface to an outside series of sliders to allow the planner and client to engage.

But at the most basic level, just imagine for a moment that the next time a client wants to look at lots of different “what if” scenarios for their plan, you simply loaded it up on the big screen, handed the keyboard and mouse to the client, let them grab the sliders, and explore the possibilities of their plan on the spot, while you as the financial advisor guided them through the process and used your experience and wisdom to help them interpret the results they were seeing and formulate a game plan.

Welcome to the world of real-time collaborative financial planning.