Generative AI can allow wealth managers to personalise at scale, enabling them to put clients at the centre of business models.
While Tesla’s CEO Elon Musk has sued ChatGPT’s OpenAI, the start-up he co-founded, for putting profit beyond humanity, wealth managers and private banks are busy investing in artificial intelligence (AI) to reduce costs, enhance client service and increase revenues.
“In the financial industry, AI has been used for years in areas including algorithmic and high frequency trading. Now, we expect AI will provide positive impact at the client level,” says Ida Liu, head of Citi Private Bank.
AI, she predicts, will add a new dimension to trend identification, with its “superhuman processing capabilities”. It will also “unlock potential portfolio synergies” by selecting investment opportunities that best complement a client’s holdings and investment objective profile, believes Ms Liu.
Citi aims to use AI as a tool that “streamlines and automates processes”, while allowing private bankers to spend their time serving clients.
This vision is shared by many competitors, who have been busy investing across the whole AI spectrum, including machine learning, natural language processing and robotic process automation, as well as the most recent generative (Gen) AI.
AI’s popularity as the investment of choice is stunning. Over the last three years, 73 per cent of private banks and 64 per cent of wealth management firms have committed more to AI than any other technology, according to a 2024 global report from a research coalition led by US think-thank ThoughtLab. Fifty-eight per cent of all respondents expect AI to be their biggest tech investment over the next three years.
In customer service, it is mainly used in investment advisory, research and financial planning. At the back end, AI is mainly employed for fraud detection and cyber risk management.
Personalised service
The beauty of AI is that it can save time and costs around routine tasks, while also adding capacity to improve the customer experience. “AI is a marvellous tool for cutting costs and driving productivity by automating time-consuming tasks, but it can also help private banks boost the top line and build competitive advantage,” says Lou Celi, CEO of ThoughtLab.
A third of private banks are seeing increased revenue from their investments in AI and related digital technologies, and they expect continued revenue growth in the years ahead, according to the firm’s study that surveyed 250 wealth managers.
A key benefit is that AI enables advisers to deliver personalised service and recommendations to clients and reach a greater number of them, improving productivity. “AI can squeeze out investment inefficiencies and take care of elements where humans don’t add as much value, such as portfolio construction,” says Mr Celi.
In particular, the advent of GenAI enables wealth managers to personalise at scale. “Its unique superpower” is ability to create videos and images for marketing campaigns, software code for programmers, sales forecasts and scenarios, and personalised chatbot interactions.
“Rather than looking simply at age or wealth categories, GenAI can create thousands of different client profiles and generate marketing and investment ideas uniquely targeted to each one,” explains Mr Celi.
In financial planning, it enables firms to conduct more accurate forecasting and impactful scenario analysis, allowing them to offer more bespoke services and products.
GenAI enables private banks to “walk the talk” and “put clients at the centre” of their business, believes Tommaso Migliore, CEO and co-founder of MDOTM, a provider of AI-driven solutions for banks, family offices, wealth and asset managers. He sees “pretty high demand” for this technology across the board in the wealth management space.
“While ‘analytical AI’, the computing part of AI, has been around for several years, the transformative part has been brought by Gen AI, which gives a different dimension to numbers and humanises the models themselves,” says Mr Migliore. Advisers, for instance, can get a detailed analysis of how a portfolio is allocated, but also, through GenAI, receive a commentary in different languages on why the portfolio is positioned or has been rebalanced in a certain way. This enormously facilitates client meetings.
New client segments
With AI enabling personalised service at lower costs, advisers can add value and service higher wealth or affluent customers, traditionally shunned by private banks. This client segment is a “huge untapped opportunity”, believes Mr Migliore.
According to a 2023 study by McKinsey, in Asia alone, where affluent and mass-affluent market segments are "at a tipping point", AI will help boost incremental revenue for wealth managers by $5bn to $25bn in the next three years, by allowing firms to reach underserved investor segments. This will represent more than half of the industry’s revenue growth in the region.
“GenAI could help scale the services we provide to make it more accessible for clients. This is the same concept as robo-advisers, which use technology to scale sound, high quality financial advice to reach people that did not have access to it before,” says Nick Holeman, ‘robo-adviser’ Betterment’s director of financial planning.
Betterment sees GenAI mainly as a tool “to augment human advisers”, potentially lowering the minimum thresholds for client to access human advisers or lower its cost. Mr Holeman believes, however, that tech-first players such as Betterment will “have a leg up in the AI race”, pointing to a distinct lack of success of big, incumbent players including UBS and Goldman Sachs, in attempts to crack the mass-market demographic.
According to ThoughtLab research, rather than focusing on the mass affluent, private banks plan to move target their efforts towards higher wealth levels, using AI to identify and better serve wealthy client niches, particularly corporate executives, entrepreneurs, professional athletes, and beneficiaries of existing clients.
AI may also be also an additional tool to reach self-directed younger generations. DBS Bank has leveraged AI/ML to analyse more than 16,000 customer attributes, including individual risk profile, browsing history, investment activity, and portfolio holdings, which result in personalised “nudges” to clients. Today, 90 per cent of the bank’s clients monitor their portfolios and transact via its digital app. Moreover, relationship managers hold more AI-augmented conversations with clients, with nudges driving 30 per cent more clients to take up legacy planning solutions.
“I believe Gen AI will also help uplift further our ‘phygital’ engagement model, taking what we have currently have to the next level, and facilitating an even more dynamic transition between a client’s online and offline interactions,” says Tse Koon Shee, DBS Bank’s group head of consumer banking group and wealth management, who will be speaking at the upcoming PWM Wealth Management Summit Asia in Singapore.
Productivity gains
“GenAI is an incredibly versatile tool with countless applications that have the potential to revolutionise the private banker experience,” agrees Bank of Singapore’s chief data officer Celine Le Cotonnec, who will be speaking at PWM’s Innovation in Wealth Management Summit in May in London.
With the bank recently launching an algorithm-based discretionary portfolio, she believes portfolio managers will soon be able to generate more personalised commentary for discretionary portfolio mandates, using large language models (LLMs). This year, the bank introduced an internal AI chatbot to assist with “extensive write-up activities” involved in the onboarding and advisory processes. “This has significantly saved time for private bankers when it comes to documenting, for example, a client’s source of wealth,” says Ms Le Cotonnec.
Additionally, support functions within the bank are already benefiting from GenAI through assisted transcription of meeting minutes for boards and committees. “The implementation of GenAI has resulted in significant productivity gains throughout the organisation,” she says.
AI has also become an essential tool in the advisory process. For instance, it helps researchers and advisers collect, analyse and interpret the extensive amount of economic, market, and sentiment data available worldwide.
UBS, the world’s largest wealth manager, “actively uses” AI models to support informed decision-making and process automation. AI-supported tools are employed to help advisers prepare customer meetings by pooling information about specific clients from various internal systems into a briefing, or by translating and displaying relevant information and analysis about a client or their portfolio on a dashboard.
“The goal is to improve advice and services for our clients and enhance client experience with more tailor-made and high-quality solutions and recommendations. At the same time, the use of AI helps increase operational efficiency,” says a UBS spokesperson.
Unstructured data
GenAI can provide private banks and wealth management firms — which are essentially data-driven businesses — with much faster and deeper insights from these mountains of data and offer competitive advantage. This means players without access to quality data will most likely be left behind.
“While Amazon exists because of the postal service and the internet, AI can exist because of cloud computing power and because over the past 15 to 20 years there's been a greater focus on controlling and storing data better,” says MDOTM’s Mr Migliore.
“Companies that have not done that transition properly into how they manage their own data, especially client data and preferences, will be lagging behind,” he warns.
Many wealth management firms have ‘dark data’, ie different types of data stored in different operational systems, trading platforms or CRMs, which cannot be readily used for analytics and provide insights, explains Zach Womack, chief technology officer at SEI.
“Private banks still operate in a world that largely runs in batch and overnight processing, but what they need is real time access to data from multiple sources, so that they can layer business intelligence, which can include AI capabilities, on top of that. Only good, structured data that you can train AI against, will give you a competitive advantage,” he says.
Differentiating factor
While on the surface, commonly available LLMs appear to have levelled the wealth management playing field, there is already a new battle for tech supremacy bubbling under.
“The true differentiator will be if firms invest in proprietary AI models or LLMs that are trained on the rich enterprise data to differentiate from peers,” insists Mike Lee, EY global wealth and asset management leader.
AI will exacerbate the differences between firms investing and embracing this technology versus those who are not. “We will see a clear demarcation in the level of service and customer experience for firms who have integrated AI in their core processes,” says Mr Lee.
This concept strongly resonates with private bankers. “I firmly believe that AI has the potential to become a crucial distinguishing factor for private banks in the future,” believes Bank of Singapore’s Ms Le Cotonnec.
Risks on the high-tech highway
But the successful integration of AI into wealth management will require overcoming challenges related to adoption, data security, and regulatory clarity. Moreover, using GenAI comes with “its own set of risks” including lack of trust with LLMs, model “explainability”, inaccurate responses or “hallucination” and threats about data security and intellectual property infringement. Wealth managers should also consider “risks of using sensitive data” within these models, explains EY’s Mr Lee.
Other issues include biased and unreliable data, a lack of tech-savvy talent and systemic risks linked with portfolio managers optimising investments using the same AI base, adds LGT’s Simon Gomez, head data and innovation at LGT Private Banking.
It is also crucial to understand AI's wider impact on broader society and maintain critical oversight of AI's outputs. “Addressing all these challenges involves a multifaceted approach, including education, human-in-the-loop oversight, and rigorous data validation processes,” says Mr Gomez.
Whether AI is going to be a key differentiating factor for private banks will depend on “how effectively it is woven into each bank's unique value proposition and service delivery”, he believes.
There is a strong consensus among private bankers that AI will significantly change the way they work. But one of the biggest barriers to the use of AI is staff resistance to change. Should advisers feel threatened by AI? “It's sort of a common saying now that ‘you're not going to lose your job to AI, but you might lose it to someone that knows how to use AI’,” warns SEI’s Mr Womack.
Tse Koon Shee, DBS Bank’s group head of consumer banking group and wealth management, will be speaking at PWM’s Wealth Management Summit Asia in Singapore on March 14. Bank of Singapore’s chief data officer Celine Le Cotonnec, will be speaking at PWM’s Innovation in Wealth Management Summit on May 2 in London.
