By Jinesh Patel, Vice President, DC Consulting, Redington
Research and experience tell us that limited consumer engagement and knowledge mean most employees will opt for the default fund. Clearly, by virtue of a default being a one size fits all strategy, it is far from perfect, particularly for employers with a diverse workforce, both in terms of age, and in terms of expected outcomes. What is needed is a graded system of default funds which fully support the goals of individual scheme members.
But how graded should, and indeed, can these schemes be? At this point, many in the industry will hit a brick wall. After all, with engagement and understanding amongst employees so low, isn’t presenting them with a number of choices (thereby increasing the chances of some making an inappropriate choice) just asking for trouble? Of course, it is possible to educate them to some degree about risk appetite and investment returns. But even fully paid up advocates of financial education (like my colleagues at Redington) understand that it will take a full generation to resolve the general public’s gaps in financial knowledge.
What is required is a solution which is both quick to deliver and far-reaching in its application, but that offers the tantalising possibility of personalised, and therefore more accurate, results for retirement savers. The good news is, leveraging technology should allow us to do just this. We are living in the golden age of data, with processes for collecting it improving immeasurably in the last decade. The sheer bulk of information now available to us has enriched our lives in so many ways, whether that’s via the human genome coding project, which is targeting a complete overhaul of the way we treat human diseases or simply through consumer intelligence which enables stores to offer us more relevant deals based on what they know to be our preferences.
It is time for us to apply this to the world of retirement savings. For too long now, investment strategies have been clunky and generalised, governed by a few outdated strategies, which are very much rule of thumb. As a result, older people have tended to be directed to less risker assets such as bonds, while younger people, who have time on their side, have been pushed towards higher yielding, higher risk equities. Of course, the maths behind this modelling is sound, but when we really drill down into these demographics, we learn it is far too simplistic. Greater insights gleaned through data tell us that there are subsets within subsets, let alone different personality types – and the best possible outcome for each of them depends on a subtly different approach.
For instance, there is a growing movement of so-called millennials who are keen to retire early: blue chip equities alone are not going to be the solution for them. Likewise, those aged 35-45 who may ordinarily be guided towards a gradual repositioning into less risky assets may actually want to retire later. It would thus make sense to treat them more like a millennial, so increasing risky assets. In fact, a really joined up approach might allow us to scrutinise their health records so we can postulate an accurate ‘health cycle’ for them (which would predict both life expectancy and how long they might be able to work productively). Technology, and the data that drives it, can also help to shape policy and behaviour in a meaningful, constructive way. This could, for instance, open the doors to insisting on higher levels of contributions for those with low risk tolerance – this would help to offset some opportunity cost along the way.
These solutions may sound futuristic. But the truth is, the technology now exists to allow us to do almost anything we want to do. We need to programme it correctly to begin with the end game, but letting technology do the heavy lifting could transform the investment outcomes for everyone for the better.
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