What does this mean for human wealth managers?
The good news is that blended models are proving popular and the industry has experienced strong growth as a result. AUM by robo-advisors went past $200 billion in 2017 and is projected to reach $2 trillion by 2020.
To learn more, Nick Jones and Ian Jenkins spoke to Charles Wong CEO of Prive Managers a Hong Kong based Robo Advisory business, that was a finalist of the Abu Dhabi Global Markets Innovation Challenge in 2017. Prive was also the top ranked Robo Advisor across the APAC region according to a recent KAPLAN white paper. This was due to the fact that Prive is “run by experienced, diligent and trustworthy managers who designed their robo systems by using the wealth of practical investment knowledge, latest financial models’ know-how and applied state of the art risk management.” High praise indeed so who better to ask?
How will Robo Advise open up wealth management and investment to a wider audience?
The wealth management industry is undergoing a paradigm shift whereby the product-pushing approach to investment advisory simply does not work anymore. Investors have become more demanding in terms of being presented products that are more personalised to them, rather than being pitched products that have absolutely no significance. The expectations have been scaled up to what is now considered the ‘normal’ experience with a heightened appetite for customisation. The problem being that in the Mass Affluent and High Net Worth space, advisors typically have numerous clients - often in the several hundreds - and therefore are not able to allocate time to understand each one of their client’s wants and needs. Where, on the other end of the spectrum, the investors in the Ultra-High Net Worth space get a truly personalised service since investors in this space are able to afford the high commission costs associated with being the sole client of an investor.
What robo enables, to a certain extent, is to bring that level of personalised service down to the lower wealth segments. Through automation, advanced algorithms are able to understand the investor as an individual and build portfolios according to the individual’s risk profile, investment preferences, wealth goals, etc. This way the investor is able to look at investment product that would actually be of interest to them, in a matter of seconds, without the commission costs of having an advisor and a higher chance of conversion.
What do humans bring to the table that AI struggles to replicate?
My belief is that better outcomes happen when the trained professional can apply his energy and wisdom and let the computers take care of the data management. Better outcomes happen when the artificial intelligence acts in a supportive role to the human element, not the primary role.
When human advisors leverage artificial intelligence to take care of back office tasks and automate the mundane, they are able to add more value to the wealth management process by contributing more of what artificial intelligence is simply unable to do.
While pure artificial intelligence are effectively passive portfolio management tools, which are based on generic goals and risk profiles, human financial advisors are able to conduct more of an active portfolio management role. This involves the ability to truly understand their client’s wants and needs and offer a sense of empathy not available through AI. This is crucial in constructing an individually tailored customer experience for the clients.
Trust is an important component of financial advice, how does the blended robo/human model support this?
The wealth management sector rightly prides itself on being relationship-driven, with long-term loyalty secured only through a years-long and painstaking accumulation of client knowledge. From our experience, we have found that people accept the robo advisory for financial services, but most of these individuals would still like to have human interaction for more complex financial services. The reason being that individuals would have less faith in computers to carry out functions that are more sensitive in nature, and therefore would prefer humans empowered by artificial intelligence.