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Tuesday, August 15, 2017

How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs | Harvard Business Review - Technology

Photo: Thomas H. Davenport
Photo: Randy Bean
"Investment advisers are using technology to build deeper relationships" says Thomas H. Davenport, President’s Distinguished Professor in Management and Information Technology at Babson College and Randy Bean, CEO and managing partner of consultancy NewVantage Partners. 

Photo:  Harvard Business Review

Systems that provide automated investment advice from financial firms have been referred to as robo-advisers. While no one in the industry is particularly fond of the term, it has caught on nonetheless. However, the enhanced human advising process — augmented by machine learning — that was recently announced by Morgan Stanley goes well beyond the robo label, and may help to finally kill off the term.
 
New York–based Morgan Stanley, in business since 1935, has been known as one of the more human-centric firms in the retail investing industry. It has 16,000 financial advisors (FAs), who historically have maintained strong relationships with their investor clients through such traditional channels as face-to-face meetings and phone calls. However, the firm knows that these labor-intensive channels limit the number of possible relationships and appeal primarily to older investors (according to a Deloitte study, the average wealth management client in the U.S. across the industry is over 60).

So Morgan Stanley’s wealth management business unit has been working for several years on a “next best action” system that FAs could use to make their advice both more efficient and more effective. The first version of the system, which used rule-based approaches to suggesting investment options, is being replaced by a system that employs machine learning to match investment possibilities to client preferences. There are far too many investing options today for FAs to keep track of them all and present them to clients. And if something momentous happens in the marketplace — for example, the Brexit vote and the resulting decline in UK-based stocks — it’s impossible for FAs to reach out personally to all their clients in a short timeframe.

The next best action system at Morgan Stanley, then, is focused on three separate objectives — only one of which is common in the robo-adviser market. There is, of course, a set of investment insights and choices for clients. In most existing machine advice, the recommended investments are strictly passive, that is, mutual funds or exchange-traded funds. The Morgan Stanley system can offer those if the client prefers them, but can also present individual stocks or bonds based on the firm’s research. The FA is given several ideas to offer the client and can use their own judgment as to whether to pass along any or all of them.
 
The second aspect of the system is to provide operational alerts. These might include margin calls, low-cash-balance alerts, or notifications of significant increases or decreases in the client’s portfolio. They might also include noteworthy events in financial markets, such as the aforementioned Brexit vote. FAs can combine personalized text with the alert and send it out over a variety of communications channels.

Finally, the Morgan Stanley system includes content on life events. If, for example, a client had a child with a certain illness, the system could recommend the best local hospitals, schools, and financial strategies for dealing with the illness. That life-event content isn’t found in other machine advisor systems, and has the potential to help create a trusting and value-adding relationship between clients and FAs.
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Photo:  Harvard Business Review
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Source: Harvard Business Review


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