Wealth management and AI is a natural combination. Standalone Fintechs, innovation labs of incumbents and of financial services IT providers, are all somehow working on this (3 types). There is another war of talent going on this area too. All three types of Financial services providers are looking for Data scientists and competing with all other industries (commerce, life sciences, and manufacturing). The market is tagging experienced conventional quants as AI experts. Public companies (mainly banks) are competing for tech branding.
I realized that I have not written about Morgan Stanley as much as Goldman or JP Morgan. Of course, this is not deliberate. I am well aware of the heads on competition between which of course is accentuated from business media. Look at the headlines during this reporting season and you will undoubtedly get a sense of this short-term pressure that public markets and the quarterly cycles, inflict.
What caught my attention this time about Morgan Stanley, was the release of the new version of the so-called “Next Best Action” system to the 16,000 RIA of MS. This system has been around for several years but as a rule-based system suggesting investment options for advisors and their clients. A system that every single bank with a wealth management offering has and that we all as clients wonder which is “best” (as if that is the right question in the first place since none of these rule-based systems could be customized).
Morgan Stanley’s “Next Best Action” is using Machine Learning to support advisors in increasing engagement. The success of this tool will be measured by its effectiveness to enhance the dialogue with the client whether it is through in-person meetings, phone calls or pure digital channels.
Like me, most of us are sick and tired of emails with pdf attachments of several analysts covering Alibaba (that I care about accumulating) and not knowing how to make sense of that. All of us, are realizing that only because of KYC stringent requirements, advisors look to incorporate our life events and goals into an investment proposal. Morgan Stanley’s “Next Best Action” system is using ML to advise clients on what to consider based on life events. 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. The system monitors and learns from the reaction of the client to the “Recommendations” and based on the client responses, improves the quality of ideas each day.
In a way, the system thinks for the advisor on a daily basis and presents relevant information and continuously improved recommendations. The advisor has a choice and can send customized emails and texts to clients. The system in a few seconds finds the clients’ asset allocation, tax situation, preferences, and values.
The system is empowering the advisor and this is where the potential of widespread adaptation lies. Never forget that tech adoption is always more of a cultural issue rather than a technical one. In machine learning, the more the system is used the better the next best actions are.
If the community of the 16,000 Morgan Stanley advisors make the “Next Best Action” their ally, then MS will have an edge and a loyal army taking care of their clients.
This is not about disintermediation. ML can build loyalty for the intermediaries servicing clients and at the same time offer continuously better advice to end clients.
This not some version of robo-advisory focused on best on-boarding and low fee execution. It is enhancing a hybrid wealth management offering in a way that offers a cutting-edge (value) to those using Morgan Stanley as a platform provider (i.e. the advisors) and the end clients.
Morgan Stanley has established its tech center in Montreal – Montreal Technology Centre. It has grown to 1200 tech employees focused on innovation in low-latency and electronic trading, cloud engineering, cybersecurity, AI/machine learning, and end-user technologies.
Barron’s reports that it took MS about 6yrs to develop the “Next Best Action”. The main KPI is customer engagement. The other five variables monitored are: cash flow, brokerage business volume, new advice clients, the level of banking business, and account attrition.
Get fresh daily insights from an amazing team of Fintech thought leaders around the world. Ride the Fintech wave by reading us daily in your email.