Two articles in a recent issue of The Insurance & Investment Journal provide perspectives on the potential impact of artifical intelligence on advice given by (human) financial advisors and brokers.
One refers to recently emerged services such as Weathsimple, filling in the gap generated by many younger investors not having sufficient assets to engage a traditional advisor, many also lacking the confidence or support to invest on their own via the discount market. Thus the ‘robo-advice’ model seeks to offer “the value of a smart portfolio with the simplicity of the experience”.
Getting regulators onside is assisted by the latter’s stated emphasis that “all Canadians, regardless of wealth, should have access to some level of support and advice”. Helping to reinforce the message of client discipline, Weathsimple for one has been adding website content, such as podcasts emphasizing investment discipline.
The message to the industry is to look at the robo-advisor role as a complementary one, helping clients who otherwise might not be investing at all.
The other article looks at a high profile, more nuts-and-bolts development.
Comments arising from a meeting held in Quebec late last year focused on Watson, the IBM cognitive technology which is the mainstay of the company’s financial services division. As explained by the global partner in this division, Watson processes information “more like a human than like a computer, using an understanding of natural language and generating hypotheses based on conclusive evidence and learning”.
At this point in time, Watson is actually involved in investment customer service, including matters of risk and compliance. Working on the levels of ‘cognition, prediction, prescription’, “banking clients are teaching Watson how to do the work of bankers”. (What impact might Watson have had if around in late 2008?)
While continuing to improve, Watson establishes a ‘semi-human relationship’, thus improving both machine and man/woman, but not replacing the latter. As Watson learns to deal with customers, its lack of understanding of self conduct can be balanced with knowledge and know-how. Each human input adds to Watson’s learning “what is correct and what is not, what is good and what is not”. Thus, to the extent Watson has ‘a good education’, with a constant learning curve, advisors can access a tool offering high added value.
The synergistic conclusion is that, rather than being vulnerable to obsolescence, advisors can benefit as well as consumers: Watson can provide evidence to support a position, but “in the end it is the advisor and the client who make the decision”.
All that’s needed now is to arrange meetings in driverless cars, to add mobility to the experience.
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