The Role of Digital in Financial Planning

By Lucas Weatherill, CEO & CIO at OnTrack Retirement, and Boris Liedtke, Distinguished Executive Fellow, INSEAD Emerging Markets Institute

 

Retirement planning, riddled with uncertainty and consumer biases as it is, may be best handled with a mix of digital and face-to-face advice.

 

Historically, so long as your company and government stayed solvent, you knew with a fair amount of certainty what your retirement benefits would be and how long they’d last – basically for life. The rise of defined contribution plans turned that on its head and created a large market for personal financial advice, as individuals suddenly had to figure out how to plan for their own retirement. However, as we outlined in the first part of this series, traditional face-to-face financial advice isn’t cost-effective for providers and average investors.

 

So, how to provide advice to the average long-term investor in a cost-efficient and profitable way? In this piece, we are going to delve a little deeper into a solution.

 

Financial Planning Isn’t Just About Finance

 

Long-term saving is a classic case study in behavioural biases. These must be managed and mitigated – whether it is through digital or face-to-face advice.

 

Inertia is one such bias. While people will generally put off taking action, research has shown that if they are intimately involved in preparing a plan, they are more likely to stick to it. The most committed planners also tend to be the most financially literate.

 

On a broader level, individuals need to understand the trade-offs they make, now and in the future. They need to be educated about the consequences of their decisions and consciously choose their priorities. What lifestyle do they want now? How about in retirement? Are they contemplating any bequest?

 

Imparting a good understanding of behavioural biases should be an integral part of the retirement planning process and needs to be built into any successful digital-style advice model. Either that, or the model should protect individuals from the worst of their own biases, as much as possible.

 

Any Model is Based on Assumptions That Must Be Evaluated

 

While robo-advisors are getting lots of press at the moment, they are mostly just a delivery mechanism. A nice user interface should not be a substitute for solid advice that ultimately addresses a key financial and behavioural problem. Digital poor advice is still poor advice.

 

Tool creators – particularly when there is limited opportunity to ask them questions – need to be upfront about the assumptions they used for calculations. By far the most consequential assumptions that go into long-term planning concern the expected rates of return. If the tool assumes that equity markets will continue to return 6% (in real terms) as they have for the past century, monthly savings need to be a lot less than if a 3% return rate is assumed. But which rate better reflects the future? Over which time frame? How is the person’s age taken into account? Does time to retirement matter?

 

Thinking in real terms is convenient, but what happens if inflation turns out to be 5% per annum instead of 2%? Inflation plays a key role as it is the link between salary (and hence saving capacity), asset market returns and valuations, the value of other assets (like property) and perhaps most importantly, spending in retirement. In short, it is so integral to the problem of retirement that it needs to be carefully modelled – and very clearly explained. Failing to adequately address it may render the advice misleading at best, leaving the user to reach retirement woefully underfunded.

The Role of Digital in Financial Planning

What a Useful Digital Tool Should Look Like

 

To be a valuable tool, a digital platform needs to be both robust and user-friendly. A smartly designed product that manages biases to bring about the outcome chosen by the consumer will be a remarkably cost-effective way of providing customised financial advice to most people, most of the time.

 

The tool should explain its assumptions in a simple way, but without sacrificing real-world complexity. Other points to note:

 

 

Computationally, a Monte Carlo approach – a computer-simulated analysis of potential decision outcomes - is the optimal way to allow for the range of possibilities that the unknowable future may hold in store. Simulations need to be run with different rates of return and inflation and maybe even varying levels of tax rates and government entitlements.

 

The Best Blend of Digital and Face-to-Face Advice

 

Ultimately, the biggest weakness of digital advice tools is the unpredictable behaviour of users. For instance, what will they do - and who will they turn to for counselling - when markets fall 20% in a month?

 

Moreover, government benefits are extremely difficult to project even five years out, let alone 20 years. These benefits vary by country but often include tax advantages for long-term savings, an old-age pension, health care subsidies and specific one-off cash grants. Given their inherent uncertainty, the value of these future benefits can be extremely difficult to model.

 

For all these reasons, we envision the current generation of digital advisors providing about 50% of the advice needed for 80% of the people. As retirement age approaches, it is wise for customers to sit down with a specialist and plan how to maximise their government benefits and tax structuring (especially estate planning in some countries).

 

In other words, it will be quite a while before the human planner goes extinct. Instead, financial advisors will deliver issue-specific advice using digital devices.  Gone will be the days of trudging to their offices clutching a pile of paperwork at an appointed time. Financial advice will only be a few clicks away after you’ve reviewed your plans on your phone.

 

Lucas Weatherill is the founder & CIO at OnTrack Retirement.

 

Boris Liedtke is a Distinguished Executive Fellow in the INSEAD Emerging Markets Institute.

 

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This article is republished courtesy of INSEAD Knowledge. Copyright INSEAD 2018.

 

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