MyPrivateBanking Blog
Daily Comments on the World of Wealth Management

Robo Investing – Let’s Talk Digital

(By Francis Groves, Senior Analyst)

There was a strong consensus at yesterday’s Robo Investing conference that the future of automated investing is NOT standalone D2C robo-advisors. Breaking even as a go-it-alone robo is simply too much of a challenge for many to succeed; to be successful the robo approach needs to build on the advantage of established brands, though these are by no means certain to be just existing financial brands. Andrew Power of Deloittes made the point that a robo with average portfolios of £35K and charging 75 basis points would need AuM of £3 billion to break even.

What came across most strongly was the wealth of insights into what is need for automated interfaces to play their part in engaging new clients. Speakers made the point that the public need more education about their own need to make financial provision for themselves and the importance of switching from saving to investing if they are going to make their money work for them. However, as Rob Hudson of Aberdeen Asset Management said, institutions shouldn’t make education a main focus but, instead, should use ‘the power of easy’ and concentrate on putting financial products in front of customers.

Richard Theo of Wealthify suggested that simplicity (of design) could move mountains and that design really needed to concentrate on mobile delivery, gamification and the use of ‘nudge’ techniques. Anna Lane of the Wisdom Council also voiced concerns about simplicity and strongly recommended institutions leave out jargon and give absolute costs as well as percentages and basis points. The key learning points are to recognize that financial service users prefer automation to human interaction where it delivers what’s needed and that advisors need automation to improve efficiency and raise client:advisor ratios. Engagement is more than a good user interface and requires the creation of trust by means of the kind of preference and behavior analysis and anticipation of client needs that AI/machine learning can provide.

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