For now, at least, robo advice capabilities in the UK remain fairly rudimentary. Primarily focussing on specific investment issues, most UK robo advice offerings use basic questionnaires to profile consumers and assess their financial needs. Existing investments are largely ignored and no attempt is made to offer rebalancing across the whole of an investor’s portfolio.
Although this approach may be useful for someone looking to investigate how best to invest their ISA, in its current form, robo advice will not only be unable to meet the needs of investors with more complex financial affairs but it will also fail to meet the expectations of those who might appreciate more sophisticated advice such as tax optimisation of retirement income, for example.
What then is the future for robo advice?
It is clear that new technology and continued innovation will help to enhance robo advice capabilities in the not too distant future. Not only will robo advice expand to encompass an ever increasing number of financial goals, including estate planning, long term care and protection needs, but future versions are likely to make use of machine learning, developing predictive models aimed at helping consumers to choose investment strategies most suited to their personal circumstances.
In addition, greater use will, undoubtedly, be made of increasingly sophisticated behavioural finance and gamification techniques in order to boost consumer engagement. After all, robo advice will only be a financially successful proposition if consumers are engaged and then provided with the relevant level of support to enable them to make financial decisions and act on the recommendations made.
Decision support is likely to become an ever more important component of the advice process. In fact, an emerging area of research is the use of “conviction narratives,” which can help consumers make decisions in circumstances where there are uncertainties which can lead to “decision freezing” and, ultimately, an inability to act. In essence, a conviction narrative is where consumers create stories (“narratives”) highlighting the potential gains of a given action, whilst suppressing any anxieties or doubts about any possible downsides, thus enabling them to be convinced enough to make a decision and feel comfortable about it. The narrative needs to be balanced and able to be reinforced by empirical evidence so that consumers can trust their decisions.
Another emerging area of development is the use of “Artificial Intelligence” (AI) to closely mimic the actions of human advisers. This will enable robo advice to become ever more engaging and provide consumers with greater confidence to act upon the recommendations given. By using data collated on consumers’ personal circumstances, their attitude to risk and the choices made previously by other consumers with similar profiles, robo advice processes will be able to make personalised recommendations in the same way as a competent human adviser would, by using his or her experience of what has worked well for clients in the past.
More generally, we are still in the early stages of the “Big Data revolution”, in which the ability to capture, interpret and use data from every aspect of our interaction with customers and the financial marketplace is gradually making a reality out of some of those big ideas about mass customisation, personalisation and bespoking which seemed like pipe dreams when they first started to emerge some years ago.
In summary, not only robo advice but in fact financial advice as a whole will look very different in five or ten years’ time. A wide range of goals and financial need areas will be covered as a matter of routine.
Pure robo advice will be part of an integrated, direct to consumer proposition involving full advice delivered traditionally by advisers, hybrid advice and the self-directed purchase of financial products (using compatible advice and guidance tools so that plans developed in each channel are consistent).
Individuals with complex financial plans will use a hybrid model, entering data and using basic modelling techniques with support and advice from human advisers making use of consistent, more sophisticated tools.
Management information and continuous feedback loops will be used to implement changes quickly to improve the effectiveness of the overall robo advice process. Constant improvements will be made using the latest ideas from behavioural finance, gamification and psychology, as well as cognitive computing, to drive up consumer engagement and increase the implementation of financial plans.
Robo advice is the catalyst for what will be, in effect, a new financial services distribution model. Advice will be industrialised and of a consistent quality which will reduce regulatory risk and penalties. The use of management information will help to lower the cost of compliance monitoring by identifying the level of regulatory risk associated with advice cases and focusing attention on those cases that pose the greatest risk. Outcomes will be improved for both consumers.in particular, and the financial services industry, as a whole.
In the end, the most successful institutions operating multi-channel distribution models will be those whose core competency is the efficient delivery of affordable and regulatory robust advice to all.