Morgan Stanley’s headquarters are in New York City, where the company was established in 1935. Known as a pioneer in wealth management, they are a multinational American investment bank and financial services provider. Among the top 15 largest banks globally, Morgan Stanley has assets of over $1.19 billion.
Morgan Stanley recorded net revenue for the quarter ending June 30, 2023, of $13.5 billion, per the bank’s Second Quarter 2023 Earnings Results. Compared to the previous year, when the company reported $13.1 billion, this amount is higher.
To aid financial advisors in understanding client needs, Morgan Stanley has made investments in AI. To customize client communications, the company also makes use of machine learning and data analytics. Investments in hardware, processing power, data storage, networking, bandwidth, and more data scientists are all necessary for the development of AI systems.
This post will look at two use cases that show how Morgan Stanley supports their main business objectives with AI. A current OpenAI initiative seems to be starting at the same time that several earlier AI projects are maturing. Our reporting will expand upon our April 2020 feature of Morgan Stanley’s AI ambitions, which included an analysis of how to improve the recommendation systems in their wealth management tool, Next Best Action:
Financial advisor chatbots with an internal focus: To increase efficiency and scale, financial advisors can receive pertinent content and insights via large language models (LLMs).
Improving client-interaction recommendation systems: Boost client engagement by using machine learning and data analytics to personalize communications and boost the interaction between financial advisors and their clients.
Using data analytics to analyze impact: Help clients use data analytics to connect their portfolios with their aim to have a positive social and environmental effect.
Promote Scale And Efficiency
Data processing is undoubtedly expensive. Big investment firms spend anywhere from $1.8 to $3.6 million collecting, processing, and extracting data from documents, according to Cano Intelligence.
Earlier this year, Morgan Stanley Wealth Management (MSWM) and OpenAI collaborated to develop a tailored solution that makes use of OpenAI’s technology. Because of this strategic alignment, they have first dibs on emerging OpenAI products.
GPT4 is the backend used by MSWM for an internal chatbot. Financial advisers can engage with a chatbot to quickly obtain pertinent insights through the conversational AI enterprise capability. The advantage becomes much more evident when you take into account the size and scope of Morgan Stanley’s hundreds of thousands of page document repository.
They have a variety of document kinds in their content library, such as the following:
Analysis and research on the market
Insights from analysts
Co-President and Head of Morgan Stanley Wealth Management Jeff McMillan described how utilizing OpenAi helps save time. He clarified how Morgan Stanley’s financial advisors benefit from a competitive edge provided by OpenAI’s technology, which turns their extensive knowledge base into insightful knowledge. When that workflow loop is closed, financial advisers have more time to devote to providing better, more direct service to their clients.
Financial advisors can spend less time sorting through hundreds of papers when they use GPT-4. Financial advisors can therefore drastically cut down on how long it takes to provide their clients the information they require.
The Morgan Stanley customer story can be seen on OpenAI’s website; Morgan Stanley was the only strategic client in wealth management to have early access to OpenAI’s offerings.
McMillan describes how big language models altered how businesses use knowledge in their customer stories. Three components comprise his description of the fundamental change in the report:
GPT4 has nearly immediate access to, processing, and synthesis of content.
Morgan Stanley publishes thousands of papers every year, which contributes to their enormous intellectual capital. Among the subjects they discuss are insights on:
Classes of assets
Analysis of the industry
Areas of the economy
The big team of financial advisors at Morgan Stanley does a great job of helping their clients. GPT-4 has been specially trained by the team to meet their internal requirements.
According to McMillan, the technology will help his bank’s 16,000+ advisors understand the bank’s enormous collection of research and data. He made this claim in an interview with CNBC. He likened the advantage to having the chief strategy officer of the company sit next to the financial advisors while they are speaking with clients.
Presently, the application is utilized by 6% of the financial advisors at Morgan Stanley. The third quarter of this year is when the entire rollout is expected to take place. Because of this, quantifiable outcomes in the form of effect are not yet available. Although implementing a successful digital transformation can be difficult, ChatGPT’s reliance on natural language conversational dialogue lowers the learning curve.
According to a prior interview with Forbes, McMillan anticipates combining their Next Best Action system—which is explained below—with their GPT4 capabilities.
Customize Communications With Clients
It is first necessary to match clients with the proper financial advisor to personalize client interactions. For this reason, Morgan Stanley created LeadIQ, a lead management platform. According to a 2019 Gartner report, businesses that struggle to carry out their personalization initiatives risk losing as much as 38% of their clientele.
LeadIQ matches financial advisors with potential clients internally, per the company’s case study report. It selects financial advisors from a pool of 1,500 pre-selected advisors to receive referrals using machine learning tools. Advisors are ranked by the program according to their past customer closing rates.
To enhance the tailoring of the messages they provide to clients, Morgan Stanley developed an AI-based engine internally called Next Best Action. We discussed Next Best Action as a nascent AI program in our April 2020 report on AI at Morgan Stanley.
In 2018, Morgan Stanley unveiled Next Best Action, a system that leads the industry. The system has several features, such as:
serving as a forum for individualized client involvement and communication.
Financial advisors can share investing and wealth management ideas with their clients using an AI-powered recommendation engine.
Over 90% of Morgan Stanley’s brokers have implemented the Next Best Action methodology by the middle of 2022.
This system’s recommendation engine was initially based on a rule-based framework. These days, it makes investment recommendations based on customer preferences using machine learning. Machine learning is used by Next Best Action to find customized investment suggestions. They use their Customer Relationship Management (CRM) systems to send those concepts and pertinent messages to certain clients.
The primary goal of the system’s original launch was to customize investment offers. But it was helpful to refocus on the aspects of client engagement. Personalized advice was readily available to financial advisors, which in turn increased client engagement. MSWM also discovered that regular customer interaction is the primary factor contributing to financial advisors’ success.
In Q2 2021, the Morgan Stanley Client Council Survey revealed that 98% of clients are happy with the way their financial advisor responds to their inquiries and demands.
Tool For Impact Analysis
One of Morgan Stanley’s main goals in offering sustainable investing solutions is client retention. “Sustainability is now increasingly a driver of whether you can attract talent, retain talent, retain customers, and retain investors,” Chief Sustainability Officer Audrey Choi said to MIT’s Jason Jay at the 2022 MIT Energy Conference.
Because of this, Morgan Stanley has been a leader in climate-conscious investing for a long time; in 2013, Choi established Morgan Stanley’s Institute for Sustainable Investing. Promoting the use of sustainable investing practices in financial markets is one of its objectives.
Morgan Stanley understood that its investors wanted their investments to have a bigger effect on the world. Morgan Stanley developed the patented Morgan Stanley Impact Quotient®, or Morgan Stanley IQ®, impact analysis tool in response to this demand.
Investors used to have a hard time figuring out whether their investments matched their intention to make a certain impact. The challenge was caused by:
Absence Of Established Metrics To Assess The Query
Rival frameworks for evaluating the effect of Morgan Stanley IQ concentrate on the impact choices of their customer base. Three methods are used in the analysis to focus on these preferences:
Clients are assisted in identifying and prioritizing their priorities by a comprehensive framework that comprises over 100 social and environmental impact preferences, nine impact themes, and more.
Uses third-party data sources and proprietary analytics to match investments to previously determined client preferences.
Gives financial advisors access to investment options that are long-term compatible with customer impact choices.
A key component of their Investing with Impact Platform is the Morgan Stanley IQ tool. At the 36-second mark in the video below, the company explains to its clients how to invest in racial fairness.