Morgan Stanley, like many other financial institutions, is constantly seeking ways to better serve its clients and to stay ahead of the curve in an increasingly competitive environment. Specifically, in its financial advising business, Morgan Stanley looks for ways to construct more optimal portfolios and to manage its large client base. Morgan Stanley has 16,000 financial advisors who have built and maintained relationships with clients through “traditional channels as face-to-face meetings and phone calls.”  This approach has helped build Morgan Stanley’s reputation in the financial advising business, but to stay competitive, construct better portfolios, and show customers individualized attention, Morgan Stanley has turned to machine learning or “robo-advisors” as a way to support its financial advisors. 
Machine learning is an important innovation for financial institutions, especially within the realm of financial advising which has historically been a human-driven business. Machine learning / robo advisors pose the potential to reduce human bias in portfolio construction and to reduce costs by eventually eliminating the number of advisors needed. As more and more investors turn to passive investment options, robo advisor can create cheaper alternatives allowing Morgan Stanley to “hold on to younger, fee sensitive customers”. 
Morgan Stanley’s machine learning system is “focused on three separate objectives — only one of which is common in the robo-adviser market.”  Most “Robo Advisors” use algorithms to help manage investors’ portfolios and do so at a discounted cost.  Morgan Stanley’s machine learning system does this, but also provides operational alerts and advice after certain life events. For example, operational alerts could include personal notices about account balances or information about how recent macroeconomic events effect one’s accounts specifically. Life advice could include hospital recommendations for a sick loved one. 
The way Morgan Stanley uses machine learning allows the machines to create analysis and recommendations but then allows the financial advisor to use human judgment in deciding what gets passed along to the client. In the short term, I believe this system is highly effective. Morgan Stanley’s McMillan states that “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.”  I agree with McMillan that in the short term the optimal solution is to combine machine learning capabilities with the human touch of a financial advisor.
In the medium term, Morgan Stanley plans to eventually launch a discounted “digitally managed portfolio option” which should be effective with a younger client base.  McMillan added that “A long-term effort is developing AI to provide recommendations for psychological advice.”  In the long term I think that research into machine learning in the field of psychology is important for the progression of robo advisors. Currently, many people hold the opinion that “one of the most valuable services that human advisors provide is the psychological part of investing — the hand holding, the communication, etc.”.  While humans are better than machines at reading expressions, tone of voice, and body language, we too are not perfect and often fail at both tasks. If robo advisors can learn more about human psychology, there is a huge opportunity for them to assist financial advisors. McMillan “emphasizes the continuing human role in wealth management,”  but if machine learning in psychology advances enough, I think there is potential for robo advisors to replace human financial advisors or at least to dramatically reduce the number of advisors needed.
One of the aspects regarding robo advisors that has not yet been explored is the question of their ethics. Some would argue that robo advisors have superior ethics compared to humans because they are able to eliminate many conflicts of interest. For example, a financial advisor may be incentivized to sell their client a product that yields higher fees for the themselves even if it is not what is best for the client. In theory, a robo advisor should not be conflicted. Others would argue that because robo advisors do not know right and wrong they cannot” truly act as fiduciaries.”  Will the creation of robo advisors improve or complicate the ethics of financial advising?
1. DiCamillo, Nathan. “Morgan Stanley Draws from ‘Hundreds of Conversations’ with Experts to Build Its AI.” American Banker, 12 July 2018, www.americanbanker.com/news/morgan-stanley-draws-from-millions-of-conversations-to-build-its-ai.
2. Bean, Thomas H, and Randy Bean. “How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs.” Harvard Business Review, 3 Aug. 2017, hbr.org/2017/08/how-machine-learning-is-helping-morgan-stanley-better-understand-client-needs.
3. Beilfuss, Lisa. “The Future Robo Adviser: Smart and Ethical?” The Wall Street Journal, Dow Jones & Company, 20 June 2018, www.wsj.com/articles/the-future-robo-adviser-smart-and-ethical-1529460240.
4. Metinko, Chris. “Why Robo-Advisors Will Not Replace Human Financial Advisors.” TheStreet, TheStreet, 28 Feb. 2017, www.thestreet.com/story/14007258/1/why-robo-advisors-will-not-replace-human-financial-advisors.html.
5. Kelly, Bruce. “What to Make of Morgan Stanley’s New Robo.” InvestmentNews , 22 Mar. 2018, www.investmentnews.com/article/20180322/FREE/180329974/what-to-make-of-morgan-stanleys-new-robo.