Optimizing Client Recommendations At The Right Behavioral Level Using A Synthesis-Based Approach

Financial planners are trained to be analyzers. When generating recommendations for clients, we gather the necessary details needed, break an issue (or issues) up into smaller parts, look for inefficiencies, and then seek to eliminate them. It’s common to optimize at a very ‘zoomed-in’ level (e.g., “I see you are contributing to your Roth 401(k), but our projections are forecasting that you’ll be in a lower tax bracket 15 years from now when you take funds out of this account, so getting the tax deduction from the traditional 401(k) option you also have available to you will reduce your long-term tax burden.”).

Synthetic (Versus Analytic) Observation As A Valuable Tool To Evaluate Client Needs

This process of ‘zooming in’, or breaking a scenario up into smaller parts, is an example of ‘analysis’, which is highly valued in our modern, empirically minded society – particularly among academics who have come to focus on extremely narrow research questions.

However, while analysis is a valuable way of thinking about problems, it is not the only valuable way to think about problems. Analysis can be contrasted with synthesis, where the aim is to generate insight from ‘zooming out’ rather than from ‘zooming in’. In his book, Myth, Meaning, and Antifragile Individualism, philosopher Marc Champagne notes:

So if analysis is zooming in to divide an issue into smaller parts, then synthesis is zooming out to unite various issues into an integrated whole… Such a synthetic style of thinking may have fallen out of fashion, but it was central to thinkers such as Aristotle and Epicurus who, in addition to investigating nature and human nature, mobilized their findings in the service of an even more important question: what would constitute a meaningful life?

While both synthesis and analysis are important, the former has arguably been significantly underappreciated by financial advisors when it comes to providing guidance to clients. The reason is that optimization that is too narrow can actually lead to suboptimal results.

Consider a common example where financial advisors actually do a better job of synthesizing than other professionals: strategies emphasized by advisors for reducing taxes via long-term tax planning, versus attempts to lower taxes via single-year tax preparation done by tax preparers or CPAs.

It is a fairly common complaint among financial advisors that many tax preparers get overly focused on trying to reduce the current year’s tax burden without the broader perspective of where taxes will be at overall. So, for instance, a tax preparer with an ‘analysis’ perspective may criticize a Roth conversion (“Why would you convert those assets to a Roth IRA? Your tax bill went up $25,000!”) even though it may have been a really good decision from a long-term planning standpoint (because from a ‘synthesis’ perspective, paying those taxes now makes more sense, compared to in the future when the client would have owed $50,000 to distribute the same funds!).

While analysis and synthesis are both useful and can support one another, it is also worth noting that powerful synthetic thinking can sometimes override the need for analysis. For instance, suppose you know that a client is young, had unusually low income for the year, has lots of extra cash, and is contemplating a Roth conversion. An analytic approach to decide whether the Roth conversion is a good idea would involve following up with the client to get precise numbers and run a tax projection. However, in this case, the analysis is probably unnecessary. Yes, it may provide some additional degree of specificity, but by zooming out and thinking about the situation holistically, an experienced advisor will quickly see that the client’s temporarily low tax bracket provides for a situation where the numbers are going to come out in favor of a Roth conversion.

This is important to note, because sometimes synthetic thinking can get a bad reputation for being sloppy or uninterested with the details. But that isn’t necessarily the case. Sometimes we simply don’t need the analysis to make good decisions, or we have synthetically derived reasons that are compelling enough to eschew some lower-level optimization. This may be particularly relevant when thinking about choices that have implications at the lifestyle/behavior level (e.g., avoiding certain types of debt may be valuable enough for behavioral reasons that we may want to disregard strategies that could seem to go against this advice from an analytical perspective).

Ultimately, neither analysis nor synthesis should be neglected, and it can be helpful as advisors to consider both approaches of assessment to help think about where we may similarly have some of our own blind spots and tend to overlook the benefits of a more synthetic style of thinking. Champagne further notes:

All too often, we get bogged down in so many technical details and sub-distinctions that we lose sight of our ends. Those who uncritically privilege analysis over synthesis are especially prone to this. Zooming in closer is sometimes important, but once we are done we have to see whether and how our results fit in a bigger whole (see Sciabarra 2000).

For instance, as financial planners, we tend to commonly operate at a certain level of depth, where we think primarily at the level of typical CFP-type topics. For lack of a better term, let’s call this the “FP level” of depth.

Sometimes optimizing at the FP level is the right thing to do, particularly if a question is rather mundane and isn’t going to have any broader behavioral/lifestyle implications (e.g., choosing which tax lot to sell when raising cash may serve to optimize portfolio trading efficiency, but isn’t going to result in a client doing anything different in their day-to-day life).

On the other hand, sometimes there is more interplay between financial decisions and behavioral/lifestyle implications, and getting the FP level right at the expense of the behavioral level could have negative consequences (e.g., encouraging the use of low-interest debt when it ultimately leads to more consumption).

In our defense, though, decisions at the FP level have clearer, more black-and-white-type answers. “You are paying 2.5% on your mortgage and can expect to earn more than that in the market” (FP level) is easier guidance to justify than “living mortgage-free provides greater security and peace of mind that could lead to taking more productive risks in your career” (behavior level). This is yet another reason why advisors may tend to operate at this level, but that doesn’t mean that our blind spots are not still relevant.

Example: The Potential Value Of A No-Debt Constraint

There are a large number of cases where FP-level decisions may conflict with behavioral-level decisions. For instance, using credit cards over other forms of payment to earn more points (though, at least for some, the temptation of easy spending on a card comes with negatives that far outweigh the points actually earned); buying a term life insurance policy over permanent life insurance to save the difference (although many people don’t actually save the difference); refinancing debt at a low-interest rate versus using a debt snowball (for some, one giant loan won’t provide the ‘wins’ that one needs when progressing toward a debt-reduction goal and may simply result in lower motivation to save).

However, for purposes of discussion, let’s focus on a common claim that few advisors would object to: it’s okay to use debt strategically – such as in cases where one expects to earn more in the market.

Now, from a purely mathematical perspective, this is obviously true. If you earn more than you pay on debt, then your wealth grows.

The problem with this type of thinking, however, is that it is subject to what’s known as the “ceteris paribus fallacy”. Ceteris paribus is Latin for “all else being equal”, so the ceteris paribus fallacy would refer to falsely assuming all else is equal.

The truth is, living one’s life to a “no-consumer-debt” constraint can set one on a different path in life as contrasted with how their circumstances may end out had they taken some level of consumer debt.

For some, a no-consumer-debt constraint can be very powerful because the reality is that our wants don’t go away. So, by artificially constraining ourselves to not use debt, we essentially have three options when we don’t have the immediate means to obtain something we want:

• Suppress our wants or perceived needs (maybe we didn’t really need it that much after all?);
• Expand our income; or
• Cut unnecessary spending elsewhere.

Something that is underappreciated about each of the paths one might go down when adhering to a no-consumer-debt constraint is that the paths themselves actually promote better long-term outcomes.

First, purchasing behavior is rarely actually a single-point-in-time decision. If someone buys a Honda EX (Honda’s 2^nd-tier trim level), it becomes harder to ‘downgrade’ by buying a Honda LX next time (Honda’s lowest-tier trim level). Rather, we tend to want to at least stay with at least the EX, if not move up to an EX-L or a Touring. So, the inability to simply finance the extra expense of a higher-end model would have consequences both in the present and the future in terms of suppressing/prioritizing our wants.

However, sometimes we really want something that we may not have the means to immediately purchase outright. If we stick true to a no-consumer-debt constraint, a second way to still get what we want is to earn more and expand our income. Notably, this too tends to have long-term impacts.

For instance, suppose someone works really hard to get a $10,000 raise so that they can afford their preferred car. Oftentimes, once they’ve put in the work and moved up the ladder, it’s generally not the case that their income is going to drop simply because they are no longer motivated by a purchase. Rather, they harnessed their motivation toward a particular goal, and now they have an extra $10,000 per year to show for it, potentially for many years to come (if not even more if they continue to move on a similar trajectory).

And finally, if someone goes through the process of budgeting and cutting expenses to afford a vehicle, often there’s some persistence to those changes as well. We may cut cable initially for budgeting reasons, but then decide it wasn’t actually needed after all.

Now contrast the paths above with the option of hitting the ‘easy button’ and just financing a larger purchase at 0.9%. It’s certainly true that people can expect to earn more than 0.9% in the market long-term (FP level consideration), but those excess earnings may actually pale in comparison to how adhering to a no-consumer-debt constraint changes the decisions we make in life (lifestyle/behavioral-level considerations).

Financial Personalities Optimize At The Behavioral Level

It’s no secret that financial advisors and certain financial gurus tend to butt heads a lot. While not every financial guru out there may be worth defending, it’s very possible that many of the persistent conflicts arise because of varying levels of optimization each group tends to use. Advisors tend to optimize at the FP level, while gurus tend to optimize at the behavioral level.

Notably, this is partially due to the varying mediums for delivering advice. Many financial gurus such as Dave Ramsey and Suze Orman generally operate in a one-to-many channel. This medium does not work well for speaking to the nuance of every single analytic detail (as it does in a one-to-one channel for advisors), but it does work well for a more synthetic type of thinking that extracts core principles that can help someone achieve financial success.

This inevitably leads to conflict. Take, for instance, the classic advisor argument against Dave Ramsey’s debt snowball. Many advisors recommend that debt should be repaid starting with the highest interest rate first. At the FP level, advisors who object to the snowball are, of course, correct that, all else being equal, paying the highest interest debt first will eliminate debt fastest.

But notice how much work the “all else being equal” is doing here. We again have the ceteris paribus fallacy. When it comes to getting psychological wins and building momentum and excitement, all else is not equal. The debt snowball approach, which includes opportunities for many small wins and positive feedback loops, is qualitatively different from the highest-interest-first strategy from a motivation perspective. And it’s ultimately this cumulative motivation-building process that leads to the debt snowball working so well for so many people.

Personal savings velocity is a really underappreciated and understudied topic. While it certainly may not happen in all cases, anecdotally, there definitely are times when someone repays a debt, and rather than just truly following the snowball approach of shifting the old payment over to the next debt, they actually pay the next debt even faster (e.g., the old payment plus an extra $100/month). And the next win may bring another $100…then another $100…then another $200…so on and so forth, until by the end of the debt repayment the person taking the snowball approach got much farther ahead than they would have had they just repaid their debt from the highest interest to lowest interest.

So, as Champagne suggests, it is crucially important to both zoom in (analyze) and zoom out (synthesize) to consider the totality of our advice. This is particularly true since advisors can be prone to favoring analysis over synthesis, given our own behavioral temperaments, the focus of our industry training, and the potential to go further down the analysis path given the nature of the channel in which we provide advice.

And with that in mind, it’s worth acknowledging, and appreciating, the potential good that is done from the gurus' tendency to optimize at the behavioral level by implementing a ‘synthetic’ point of view. There’s likely much that we as advisors can learn from gurus in terms of engaging in synthetic thought and building an overarching philosophy that guides individual decision making.

A Closer Look At Dave Ramsey’s “Baby Steps”

Dave Ramsey’s process using “Baby Steps” is arguably one of the most popular – if not the most popular – high-level philosophies used by Americans for building wealth.

As advisors, something we may be able to learn from Ramsey’s approach is the value that comes from the cohesive progression of the Baby Steps themselves. For those who aren’t familiar, the Baby Steps are:

• Baby Step 1: Save $1,000 for your starter emergency fund.
• Baby Step 2: Pay off all debt (except the house) using the debt snowball.
• Baby Step 3: Save 3-6 months of expenses in a fully funded emergency fund.
• Baby Step 4: Invest 15% of your household income in retirement.
• Baby Step 5: Save for your children’s college fund.
• Baby Step 6: Pay off your home early.
• Baby Step 7: Build wealth and give.

The Baby Steps are intended to be completed sequentially, with the exception of Baby Steps 4, 5, and 6, which can be worked on simultaneously (e.g., build up to 15% savings first, but then continue to save 15% while also saving for a child’s college fund and eventually paying off your mortgage).

Notably, unlike much of the one-off, topical advice advisors tend to give (e.g., “Should I save into a Roth or a traditional IRA?”), the Baby Steps collectively provide a map that can guide individuals through their wealth-building journey. Furthermore, the steps are abstracted at a good level for action to be taken and to be understood by the typical American – even if they don’t fully answer every question one might have (e.g., “How should I invest my 15% in Baby Step 4?).

While not a scientific study, at all, a while back I reached out to several groups of advisors and posed the question of how they would go about modifying Dave Ramsey’s Baby Steps.

I found it interesting how complex many of the suggestions were. For example, rather than just a $1,000 emergency fund (or the 3-to-6-month fund that comes in Baby Step 3), one person suggested:

Calculate your personal financial risks to determine an adequate emergency fund. Consider emergency expenses such as job loss, disability (elimination period), home/auto/health insurance deductibles, and others not covered by insurance.

At the FP level, this is a very good answer. It’s exactly the type of response one would expect from a CFP. But, unless someone has actually hired a CFP to help them do this, it’s probably a non-starter at the behavioral level.

The amount of analysis required by this suggestion is staggering compared to (a) save $1,000 (no analysis needed), and (b) save 3-6 months of expenses (need to calculate 1 month of expenses and multiply by some factor between 3 and 6).

If you are a non-financial expert looking for a process to follow, the Baby Steps are a true process in a way that the type of analysis advisors are eager to do is not. Furthermore, as noted above, there’s the real risk that a smattering of advice spread across different topics doesn’t provide a solid enough framework for ordering steps and figuring out when to move from one task to the next.

This is where advisors can really benefit from more synthetic thinking. At the industry level, a lot of work has been done over the past decades in establishing the CFP designation (and related body of knowledge) and building up real professional standards, but as we dive further and further into the weeds, there’s an equal – if not greater – need to zoom out and provide clients with synthetic-type thinking that pulls together all sorts of disparate but important insights. Or, rather than reinventing the wheel, more serious engagement with the wheels that have been developed.

Avoiding Abuse Of Synthetic Thinking

One objection to a more synthetic mode of thinking may be that it makes it easier to justify some self-serving practices for some in the financial services industry.

For instance, consider whole life insurance (with a higher commission paid to the agent selling the policy) as a ‘forced savings’ mechanism. Is there a way to look at issues synthetically without losing analytical insights (e.g., the greater potential for building wealth with a term and save-the-difference approach)?

On this issue of whole life insurance particularly, it’s worth noting that Dave Ramsey – who again has probably developed the most successful and influential synthesis of personal financial thinking – has also built an extreme aversion to whole life insurance. In fact, his aversion to whole life policies is probably even greater than most advisors who take a stance along the lines of, “Whole life is usually not what’s best for most people, but it can be used in some cases.”

Furthermore, Ramsey’s objection to whole life insurance is based on several analytic insights (e.g., typical whole life coverage costs 20 times more than 20-year term coverage for the same death benefit; most whole life policies lapse) that then fit within a more synthetic collection of insights, which also includes broad, philosophic views toward finances (e.g., don’t use insurance products as investments; work toward the point you don’t need life insurance).

One advantage of synthetic thinking is that it relies on a convergence of evidence from multiple perspectives, which makes it easier to reject the “Man of One Study” (Scott Alexander’s modification of Thomas Aquinas’ “man of a single book” – both referring to someone who is overly reliant on a single perspective or piece of evidence). As a result, the task of truly justifying self-serving practices in a convincing manner may be harder than one thinks.

Additionally, folk or conventional wisdom (e.g., save 15% for retirement; neither a borrower nor a lender be) should be given greater appreciation under a synthetic perspective. In a philosophical reflection on the work of psychologist Jordan Peterson, Champagne notes:

The stories we tell are encoded practices that have been selected over time by evolution to serve purely biological ends. A story will be remembered when it fosters human flourishing and forgotten when it hinders (or has no bearing on) that flourishing. Peterson’s methodological assumption, then, is that the relative age of a story attests to its evolutionary pedigree. This assumption permits a key inference, namely that the further back in time we find a given narrative structure, the more safely we can assume that it served human ends. This inference is not 100% certain, but it lends old beliefs a measure of credence.

The point here is not that folk wisdom is perfect. Certainly it is not, and it can sometimes lead us astray. But within a synthetic perspective, we should likely give more credence to conventional wisdom than is commonly practiced, since these types of ideas have actually been subjected to evolutionary pressures that have led to the retention (if useful) or rejection (if not) of such stories as part of our cultural environment. As an example, if Grandma’s advice (or other folk wisdom) is to save for a rainy day, then the fact that this folk wisdom has survived in being passed down from generation to generation should lead us to be, at least initially, skeptical of some new strategy that claims that rainy day funds are dead.

Furthermore, the point is not to provide any sort of definitive answer on the whole life insurance debate, but rather to note that good synthetic-type thinking needs to provide a convergence of evidence from multiple perspectives. So, for instance, to overcome the analytical downsides to whole life insurance coverage that are readily apparent, we wouldn’t rely on a single argument in favor of it. Rather, we should see a number of lines of reasoning all pointing toward the benefits of whole life insurance (e.g., behavioral justifications, psychological benefits, better wealth outcomes for those who purchase whole life, etc.).

Ultimately, the main point is that we, as advisors, should be cognizant of our own potential bias in our tendency to look for solutions through an analytic lens. We may be particularly prone to this based not only on our own personalities, but also on the ways in which we work with clients on a one-on-one basis. While advisors may often tend to be highly critical of financial gurus, we may wish to ‘zoom out’ and appreciate what they do, how they do it, and how we, too, can use those insights to help our clients. If we don’t, then we run the risk of accidentally leading our clients astray – even with good financial advice – because we may be optimizing at the wrong level!