Fear of missing out drives GenAI race in wealth management

Not wanting to lag the competition, wealth management firms have been rolling out new GenAI use cases, but this needs to be done for the right business reasons.

Generative artificial intelligence (GenAI), the new poster child of AI applications, promises to boost wealth advisers’ productivity and enable hyper-personalised client interaction, by being able to process large amounts of information, execute information search, synthesise within context, and auto-generate human friendly responses, while also creating new content.

Wealth managers have been exploring new use cases, eager to pioneer the launch of new solutions, aware that this technology is set to transform their business models and affect their value proposition.

“There are a tonne of exciting advancements being made right now in the wealth management space,” says Koren Picariello, head of generative AI strategy and execution at Morgan Stanley Wealth Management. “Our industry is leading the charge, which is made all the more impressive when you consider our highly complex operating environment.”

Game changer

Leveraging its partnership with OpenAI, the global bank, managing $5.7tn in client assets, has launched two GenAI solutions since last year, rolling them out to all its 15,000 advisers, aiming at increasing their productivity and deepening client relationships.

Both programmes were built in house using GPT-4, with the large language model (LLM) being built by OpenAI.

Sitting in on client Zoom meetings, Debrief, launched in June this year, keeps detailed logs of advisers’ meetings, previous client consent, and automatically creates draft emails and summaries of the discussions. Replacing the note-taking that advisers or junior employees have been doing by hand allows them to concentrate on making decisions during client meetings. The tool has been welcomed as a “total game-changer” by the bank’s advisers.

This initiative builds on the bank’s ChatGPT-like service, Assistant, launched last year, which enables financial advisers to ask questions and contemplate large amounts of content and data.

“Adoption of Assistant has been stellar with 98 per cent of Morgan Stanley’s financial adviser teams using the tool, and searches doubling from the prior Virtual Assistant,” says Ms Picariello.

When faced with the challenge of where and how to apply this powerful technology, it became clear that “our first use case would be best for our financial advisers who often rummage through our internal research documents on the day-to-day”, she adds.

The decision to use a “closed system”, with the tool only having access to Morgan Stanley’s internal documents, was important to overcome potential “scepticism among users”.

When it comes to deciding whether to buy ready-to-use capabilities versus developing them in house, there is not a one size fits all approach, says Ms Picariello.

“Buying disparate, narrow solutions offers ready-to-use capabilities but may hinder enterprise-wide integration. Custom-built solutions on a single large language model have opposite considerations.”

But regardless of the approach “the most challenging part is developing an evaluation framework to measure the efficacy of the solution”, she adds.

iPhone moment

The productivity benefits of GenAI are visible across front, middle and back-office functions, including summarising meeting transcripts and drawing conclusions from large swaths of data, says Northern Trust’s chief information officer for wealth management Vijay Luthra. “That is where we see the immediate opportunity for wealth managers: streamlining manual, time-consuming, administrative tasks to accelerate decision making.”

In addition to the productivity and operational use cases, the US bank is piloting a programme to give advisers interactive access to the Northern Trust Institute, “a research division with around 200 staff members studying 40 areas of importance to wealthy families”.

The solution enables advisers to search for content that applies to specific client situations, and it also pushes out “curated, customised content, based on life and industry events, so they can provide timely and relevant advice to clients”.

The programme, which has received “very positive feedback” from advisers, is still subject to more rigorous testing, validation and risk reviews, says Mr Luthra. “We are also building out the ‘humans in the middle’ aspect to review the output and adjust the engineering accordingly.”

GenAI is currently at the peak of market expectations and real, pragmatic solutions across industries will likely emerge over the next 12-24 months, he predicts. “Since we are in the business of accuracy and serving client interests with clarity and precision, the bar is even higher.”

The biggest challenge to face is the hallucination and existence of bias in the output of GenAI models.

“While we experiment with the technology to improve our operational efficiencies internally, we are keeping a close eye on how the industry matures in terms of tackling content accuracy and eliminating biases,” he says.

Northern Trust has invested $4.1bn in technology over the past three years, across asset servicing, asset management and wealth management.

“While GenAI seems to be having its ‘iPhone’ moment, we are equally excited about numerous other AI capabilities that can be applied across our technology platform,” he adds.

Improving the client journey

Another large bank, HSBC, is working on several AI pilots to improve the client journey. Its key focus is to make HSBC AI Markets, a digital trading platform built for institutional investors, available for private banking and wealth clients.

“The platform uses a proprietary purpose-built NLP (natural language processing) engine that allows users to generate bespoke financial market analytics, gain access to HSBC’s real-time and historic cross-asset data sets, and browse the latest market insights,” explains Anil Venuturupalli, chief information officer, global private banking and wealth and asset management. The solution is currently being developed and tested in regions where the bank’s private banking and wealth division has a strong presence and offering capital market products.

To streamline its operations, HSBC is also looking at adopting AI to aid advisers and investment counsellors in summarising research reports, while also looking to automate some of the bank’s back office processes. “We anticipate that GenAI could be used to better support clients and work more efficiently, which will, in turn, allow us to better support more clients and drive growth,” believes Mr Venuturupalli.

GenAI can save between 10 to 30 per cent of advisers’ time across the value chain, from client acquisition, onboarding, servicing and internal support, according to BCG.

When asked to rank the top three areas where GenAI could have the greatest impact on their business, wealth and asset management firms with more than $2bn in revenue indicate use cases across the value chain (see chart).

Data challenge

To be able to integrate GenAI in the business successfully, firms must take a few key steps.

“Quality and curated content are crucial. Smart AI cannot function effectively with poor inputs. This is one of the biggest challenges that companies, big and small, face,” says Morgan Stanley WM’s Ms Picariello. “GenAI will force companies to develop enterprise solutions to help improve accuracy and standardisation of content.”

It is also important to have a specific goal for use cases, “as it keeps the team focused on the end result”.

Additionally, says Ms Picariello, “it’s important to engage with the end user early in the process, in addition to educating and partnering with teams across the organisation”.

With any transformational technology, there is excitement but also fear of getting left behind.

“Today, there is a fair amount of fear of missing out (FOMO),” says Kabir Sethi, transformational leader, previously head of wealth management at LPL Financial and before that, head of digital wealth management at Bank of America Merrill Lynch.

“FOMO is not a bad thing,” he says, “because it drives urgency in trying to understand the new technology and what it means for the business.”

But one of the biggest things that will differentiate wealth managers, foundational data aside, is not rushing into development, he adds, also stressing the importance of investing in infrastructure, training and governance. Firms should develop new use cases “for the right business reasons rather than just to say: ‘we’ve got into AI’”, says Mr Sethi.

While capabilities are still evolving, firms need “a very clear idea” of the benefits and commercial outcomes of a use case before “jumping” into it. They need to define exactly how that new process is going to work, understand how advisers will react and whether they will be comfortable to use that new capability, also because the old tool generally continues to be available to them, explains Mr Sethi.

“One thing that's always been a challenge in this industry is adoption of new capabilities; they don't automatically get used. Financial advisers are used to doing things a certain way and most of them run things in a very entrepreneurial way, even in organisations where they are employed,” he says.

Because of their larger resources at their disposal, bigger firms that have invested in their data foundation will do better than others, he predicts, as “they will be able to roll out use cases a lot faster”.

Larger institutions are also better positioned to use third parties and employ capabilities. But over time, these technologies will become more accessible and cheaper to use.

“Increasingly, that definition of an integrated ecosystem is going to include not just in-house capabilities, but third-party tools and capabilities which are just so much easier to integrate, and smaller firms will benefit from that.”

Nevertheless, it is important for wealth management firms to get expertise in house and train their own workforce. “There's value in having people who have a real deep, foundational knowledge of generative AI, but you also need to meld that with the knowledge of wealth management,” adds Mr Sethi.

Equaliser

While “people are panicking”, with FOMO around GenAI fuelled by the fear of lagging their competitors, “this new technology is a challenge but also an opportunity for wealth managers”, says Tommaso Migliore, CEO and co-founder of MDOTM, a global provider of AI-driven investment solutions for institutional investors. “GenAI is a great equaliser, because everybody is starting from day one, hour zero, where they have got to understand potential and benefits of solutions available on the market,” he adds.

Solutions such as SaaS (Software as a Service) and API (Application Programming Interface), allow firms to test multiple use cases “at a rather cheap cost”, he says, allowing them to make long-term plans, which can justify bigger investments.

Wealth management firms “do not need to reinvent the wheel”, he urges. Rather than spending millions of dollars developing potential use cases, they can accelerate their journey to GenAI by integrating existing solutions. If the specific piece of tech is believed to be critical for the organisation, they may then consider developing it internally in the future, he says.

The tech firm recently launched a new GenAI solution which generates personalised commentaries on client portfolios, in a range of formats, styles, languages and outputs. Combining the firm’s proprietary “analytical AI”, aimed at automatically rebalancing portfolios, with a GenAI layer, the software allows wealth managers to “efficiently scale” their portfolio reporting and commentary activities, meeting demand for personalised investment experience.

Major companies may have the budget to develop their own technology, but this does not always justify large investments, says Mr Migliore. “Innovation is never in the technology, unless you are a technological developer, but is always in how you use the technology, and in what the technology enables.”