As part of its efforts to close the ‘advice gap’ – where individuals who need financial advice are unable to access it due to cost, availability, or lack of awareness – the FCA is set to launch ‘targeted support’: something more personalised than generic ‘guidance’, but neither as complex nor as costly as bespoke holistic advice.
The proposed targeted support framework is a good one. The issue it has identified is important. The principles it is based on are sound. And it is focused on the right outcomes – ie (in the FCA’s words) ‘helping consumers, at scale, make effective, timely and properly informed decisions’.
However, it has also got one glaring potential weakness: we know the targeted support framework relies on segmenting consumers… but if that segmentation doesn’t include behavioural, as well as financial characteristics, the framework, for all its good foundations, will fail to deliver its intended outcomes.
Behaviour matters as much as money
Effective targeted support relies on effective consumer segmentation. In the context of targeted support sitting between generic guidance and bespoke advice, when the proposals talk of grouping consumers by ‘common characteristics’, it seems fair to assume this will resemble off-the-peg sizing for clothes: varied, but not tailored.
These ‘common characteristics’ will likely include demographic and financial details – age, life stage, dependents, overall assets and liabilities, income, risk tolerance, and basic affordability and knowledge measures.
But they must also include key elements of financial personality – the most crucial parts of each investor’s behavioural signature.
Just as it would be unthinkable to design targeted support without accounting for differences in financial characteristics, the same must apply to behavioural ones. Traits such as composure, confidence, and impulsivity can substantially affect the right course of action for a consumer, often as much as their financial capacity or experience.
Product suitability is behavioural as well as financial
Because the FCA’s consultation used the specific example of annuitisation as an area that could fit the targeted support framework, let’s use that as an example.
Standard approaches tend to treat annuitisation as a quantitative, actuarial problem. Feed all the relevant financial variables and projected cashflows into a stochastic modelling engine, and out pops the ‘optimal’ level of guaranteed income. Except that it isn’t.
In practice, financial circumstances are only part of the story. Behavioural traits dramatically influence how retirement income is spent and experienced. For example, a retiree with high impulsivity is likely to deviate from any spending plan, risking greater exposure to sequencing risk or even running out of money altogether. Their lower-impulsivity peer, with identical finances, may behave entirely differently — sticking to a plan, adjusting sensibly to market conditions, and managing longevity risk more effectively.
In other words, behavioural differences don’t just influence how support is delivered; they can materially change the answer to what is ‘suitable’.
And annuitisation is far from unique. Many of the product types likely to fall within the scope of targeted support are similarly shaped by behavioural differences:
• Smoothing: Whether paying for smoothing characteristics is worth it for a given investor is influenced by several behavioural factors, eg composure, impulsivity, and financial comfort (ie their subjective self-assessment of financial wellbeing).
• Downside protection: Alongside the obvious role of capacity for loss, the suitability of purchasing specific downside protection is heavily influenced by several behavioural factors, eg composure and confidence. Those who aren’t emotionally uncomfortable with short-term falls in value have other, usually more cost-effective, ways of dealing with them.
• Familiarity (themes, local bias): An investor with high ‘familiarity bias’ derives particular comfort from investing in ‘known’ assets and, as a result, is more likely to commit to and stick with a portfolio that includes (or highlights) them. This heavily influences the suitability of tilting either a portfolio, or how it’s presented, towards those assets (eg a ‘home bias’ can provide psychological comfort, regardless of any financial pros or cons).
• Sustainability: Sustainability preferences are far more nuanced than a crude desire to invest in ‘greener’ companies (or avoid investing in ‘sin stocks’). A multi-dimensional understanding of sustainability preferences would be necessary for any targeted support that aimed to account for them. However, even simple measures of an overall preference for sustainability and impact could mean identifying investors with common characteristics for whom some investment products or solutions were likely to be a better fit, leading to greater ability to get invested, stay invested, and attain better outcomes.
• Income-generating versus total-return portfolios: While this is often framed as a technical portfolio design decision, behavioural preferences play a key role. Investors with high spending reluctance may feel more comfortable withdrawing only natural income, even if it results in lower long-term returns. Others with greater composure or lower mental accounting needs may be better suited to total-return strategies, where returns are drawn from a mix of capital and income.
These differences cannot be accounted for without a sufficiently robust behavioural assessment. Fortunately, this needn’t be more complicated than a handful of – really, really – well-tested questions.
Behavioural vulnerability deserves special attention
Behavioural vulnerability is a distinct form of vulnerability. It reflects tendencies towards poor emotional decision-making, even among those who are otherwise financially and cognitively capable.
If targeted support segments fail to reflect how consumers are likely to behave in financial situations – not just what their financial situation is – the outcomes will often fall short, especially for the behaviourally vulnerable.
Fortunately, this can be avoided by ensuring that segmentation is: a) systematic and robust, and b) reflects identifiable differences in behavioural response.
This calls for care. There are better and worse ways to assess behaviour. Poor tools can do more harm than good. But well-designed ones – especially those that identify common personality clusters – can dramatically improve the fit between consumer and support.
Importantly, this doesn’t require a full individualised assessment. Based on our research assessing thousands of investors globally, we’ve identified a small number of behavioural personas that map naturally onto the FCA’s vision of pre-defined segments.
A handful of key traits — captured on a ‘low’, ‘medium’, or ‘high’ scale — can generate a robust basis for designing targeted support that accounts for the majority of behavioural variation.
These personas would also meet the FCA’s expectation that, by aligning with a pre-defined consumer segment and scenario, a readymade solution should deliver better outcomes than no targeted support at all.
Helping investors help themselves
Alongside product fit, behavioural traits also influence how people approach a set of common financial decisions — the kinds of decisions that many investors struggle with, and where targeted support could make a meaningful difference. These include:
• Determining the appropriate cash buffer to hold before investing
• Getting invested
• Staying invested during market volatility
• Choosing appropriate withdrawal rates
• Setting review frequency
Many of these issues were covered under ‘potential scenarios for targeted support in retail investments’ in the FCA’s consultation paper.
These challenges aren’t solved with better information alone. Telling someone sitting on too much cash that they’re missing out on returns doesn’t help if they’re doing so because it feels safer. The ‘right’ solution has to feel right too.
Targeted support must account for that emotional reality — not just offer the numerically optimal option and hope for the best.
Practical behavioural segmentation is possible
You cannot overlook behaviour in any credible targeted support model. And you cannot account for behaviour without measuring it. The good news is: doing so isn’t hard.
Just as targeted support seeks a pragmatic middle ground between advice and guidance, so too must behavioural segmentation balance refinement and feasibility. Better measurement enables better targeting – and better outcomes.
Behavioural personality traits influence suitability. We know how to measure them. And we know how to use them to build robust, scalable personas that align with pre-defined segments. Doing so won’t just support better product fit – it will help people become more confident investors, and support long-term engagement and trust.
To ignore this would be a woeful missed opportunity.
Greg B Davies, PhD, is head of behavioural finance at Oxford Risk
The FCA’s consultation about its proposed targeted support framework in relation to pensions closed last month. You can read the whole consultation paper here.
