How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs – Harvard Business Review

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Executive Summary

Systems that provide automated investment advice from financial firms have been referred to as “robo-advisers.” 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. The “next best action” system at Morgan Stanley is focused on three separate objectives — only one of which is common in the robo-adviser market. It includes providing operational alerts, as well as 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. This service has the potential to help create a trusting and value-adding relationship between clients and financial advisers.

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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).

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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.

The features and functions of the system are important, of course, but the rollout is just as critical to its success. Morgan Stanley is being careful, observant, and open to change in the rollout process. Several FAs were involved in the design of the system. The development of the system is complete, it is being tested now, and initial rollout to 500 FAs will take place in September. The creators of the system — the Analytics and Data Organization within Wealth Management, headed by Jeff McMillan, the Chief Data and Analytics Officer — know that getting FAs to adopt the system is an enormous change management project. The FAs have traditionally relied on their experience, and at first they won’t understand how the system works.

Initially, the next best action system will primarily be mediated through FAs, but clients can get access to new online information as well. Morgan Stanley plans to eventually release a digital-only version with managed portfolios. It will be offered at a lower cost level to clients who prefer digital-only channels (many of whom will presumably be in the Millennial generation). To assist these clients, and to work with FAs as they adopt the system, Morgan Stanley is hiring a cadre of digital adviser associates, who will work out of call centers and provide expert advise on the use of the system.

McMillan emphasizes the continuing human role in wealth management and finds the “robo-adviser” term particularly distasteful. He told us over the phone:

For the foreseeable future, systems like these are complements to the human relationship between advisers and clients. Throughout the industry, the “hybrid” human/machine offerings have been much more successful. Humans can understand the context, deal with client emotions, and process disparate data sets. They still have a very important role to play in financial advising.

McMillan and his colleagues have considerable work to do in order to make all of the firm’s investing knowledge available through the system. They found, for example, that there was no artificial intelligence system available today that could take the knowledge embedded in investment analyst reports and make it available to support the choices presented to clients. So McMillan is working with the firm’s research department to try to make the knowledge in reports more structured and consumable by machines. This is a change management challenge that is at least equal to getting FAs to use the next best action system effectively.

Certainly, the robo aspects of this new system and process are a small part of the total. Neither Morgan Stanley’s business model nor its culture would fit well with an entirely machine-based solution for wealth management that provided no human support. Most other firms in the industry, we believe, will discover the same truth.

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