From AML controls to personalisation of the client experience, the use cases for AI are hot topics for private bankers and wealth managers. We talked to Broadridge Financial Solutions, a firm with a prominent seat at the table in this field.

AI has been the dominant tech story for financial services in this year. It is likely to stay that way into 2025. And, as the initial hype (sometimes tinged with fear) calms down, wealth managers are starting to focus about practical ways to use AI in their business.

According to New York-listed Broadridge Financial Solutions, which provides tech solutions to financial firms, examples of AI enhancements include BondGPT, which is powered by OpenAI GPT-4 that answers bond-related questions and assists users in their identification of corporate bonds on the LTX platform. This app distills bond issuer and market data so that users can pose questions – such as how to find a replacement for a bond of a certain type – quickly, and in seconds, rather than minutes or hours after talking to an analyst, as has been the case. (LTX is an electronic trading platform for corporate bonds.)

This sort of AI-powered offering shows how one use case is the ability to cut through the fog of data to get actionable, accurate information, argues German Soto Sanchez (pictured), head of corporate strategy, Broadridge.

German Soto Sanchez

“Traders are overburdened with information,” Soto Sanchez told this news service, referring to a modern dealing room and its thickets of screens and terminals.

Perhaps inevitably in the early years of a new technology trend, there is a gap between the “leaders” adopting AI and the others. And there’s clearly a big need for the wealth industry to raise its staff’s skill levels to make the most of AI, he said. 

Broadbridge recently issued its 2024 Digital Transformation & Next-Gen Technology Study. One of its more eye-catching findings was that a “profound gap has opened between leaders and non-leaders (beginners and advancers). Companies that have invested big in digital transformation – and built the innovation cultures and talent capabilities critical to fostering growth – are now pulling ahead of firms that will need to play catch-up to stay competitive. For example, 44 per cent of leaders are making moderate to large investments in GenAI – more than twice the level of non-leaders.”

On personalisation specifically, a study (Dow Jones, State of Advice Study, 2022), found that only four in 10 advisors share personalised content with investors, despite 42 per cent of investors wanting content tailored to their interests and life events. There is a gap. 

Soto Sanchez confronted the point about the costs of adopting AI. Embracing AI and embedding it doesn’t necessarily require a “lot of money,” he said. 

There is a three-step approach that should apply, Soto Sanchez said: “Engage and build domain expertise; do research about what solutions exist, experiment and try things out; and find common minded partners. You need to get your toes wet.”

He spoke earlier this month as the Singapore FinTech Festival was taking place. The Asian city-state, along with its peers in other wealth management hubs, is keen to showcase its high-tech credentials, from blockchain to AI. The rise of generative AI has sent shockwaves through financial services. (This news service mused on the implications here.)

The territory
Ideas about the use cases for AI are legion. According to Francesco Filia and Daniele Guerini, authors of The Future Of Finance: The Rising Tide of Fintech Lending And The Platform Economy, AI capabilities include credit scoring and risk assessment; fraud detection and prevention; chatbots and virtual assistants; personalised banking and financial planning; algorithmic trading; customer relationship management; regulatory compliance; robo-advisors; and natural language processing (NLP). 

As Filia and Guerini state (page 126): “There is certainly more scope for traditional banks to catch up with fintech around the building block of AI as opposed to big data. Unlike the capability of big data, which would require expensive and time-consuming infrastructure and system change, AI can be utilised more effectively by empowering a small team of people with the ability to access and make recommendations for innovations and process improvements that may indeed deliver that trillion-dollar boost that McKinsey envisages.”

Soto Sanchez’s views gel with this sort of analysis. 

AI is “extremely transformative for financial services,” and in three broad areas: decision-making, personalisation and productivity, he said. 

With decision-making, there is an ability to harness AI to look at vast amounts of data and do so in a way that is easy to digest. On personalisation, firms can build solutions and products that are more in tune with what clients want. On the efficiency and cost control side, these apply in all financial weather: “Even when times are good firms focus on being efficient and when things are harder, they double down on being efficient.”

Back to the topic of a “gap” between leading AI adopters and the rest, Soto Sanchez notes how the aforementioned Broadridge survey found that while the firm aims to train all its staff in using AI tools, across the wider industry, the share is far lower. The survey found that only around 25 per cent of staff have been trained. 

“Upskilling is going to be extremely important,” he said.”

In other survey results, Broadridge found that about 45 per cent of staff allowed staff to use gen-AI for work purposes.

Guardrails and protections
This news service said it had to ask Soto Sanchez about the dangers and risks of AI, even if people don’t think we are heading for a science fiction dystopian future. For example, many readers will have experienced the frustration of using a chatbot to handle a complaint, wishing they could speak to a human being instead. Also, those who track AI are familiar with the issue of “garbage in, garbage out”. 

There would be problems in entrusting decision with a machine, such as it absorbing the biases of the surrounding data sets to regurgitate certain biases, for example when treating women differently from men when providing credit, Soto Sanchez said. 

Early on, it is easy to see how people became concerned about the lack of rules of how to operate, Soto Sanchez said. “You absolutely need to put in guardrails,” he continued.

“We have built a platform that everyone has to use in order to use AI,” Soto Sanchez said. That way, the firm can keep tight oversight of how its employees use the tools available.

Efficiencies
On the efficiency use case side, AI can raise productivity across the front, middle and back office. On the front office, for example, AI enables greater personalisation; firms can assemble reports and information for clients that hit their specific needs and tastes, Soto Sanchez said. 

However, what AI does not do is obviate decision-making – that stays with people, especially in critical functions, he said. 

Among a number of offerings, in 2021 Broadridge rolled out an anti-money laundering solution which makes use of “intelligent automation.”