In another look at the different ways that banks, wealth managers and family offices are using AI, this article examines a survey and other information to convey where the market is heading.

Cost cutting appears to be the most popular reason for the world’s financial institutions to embrace generative AI, although enhancing the client experience and gaining a competitive edge are also strong motivators.

Those findings, from Japan-based NTT DATA, a digital business and IT services house, provide insight into how 800 senior banking figures worldwide (Europe, North America, Latin America, Asia-Pacific) think about the promise of AI and how this will affect the business. And the findings illuminate what sort of use cases – as this publication is noting – are gaining ground in the wealth management industry. 

NTT laid out what banks get most exercised about, as the following chart shows:

Source: NTT DATA

The NTT DATA report emerged a few days ago. Surveys are pouring out, chronicling what the banking, wealth management, fund management and other financial sectors see as AI’s potential. A report last year by KPMG, for example, found that three quarters (75 per cent) of the asset manager CEOs it surveyed see generative AI as a top investment priority. (Source: KPMG 2024 Asset Management CEO Outlook.)

Even the family office sector, which sometimes lags big institutions such as banks and investment houses, is feeling the trend. At US-based Eton Solutions, for example, in September 2024 it launched EtonGPT™, which it said was the world’s first generative AI module for family offices globally. Solution's ERP [enterprise resource planning] platform.

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).

The adoption of certain technologies is winning admirers.

“I am impressed by the technology’s ability to allow subject matter experts to make a bigger impact than they have traditionally been allowed within technology,” Spencer Lourens, chief data officer at CliftonLarsonAllen, the accounting and professional services firm, told this news service. “As an example, the AI models that are available today make it much easier for a business user who has a problem to create a prompt template or a small, repeatable solution that solves a business problem for them which used to take a lot longer to do manually. On a larger scale, this has led to the creation of many startups, including but not limited to Harvey AI, Decagon, and many others being built to solve very specific business problems with products that fill gaps these models can address.”

Analysis from Oliver Wyman and Morgan Stanley highlights a number of effects that AI will have, such as faster and more accurate decision-making; personalised client relationships, such as the use of natural language processing and recommendation systems; automation of internal processes, and advanced reasoning. Technologies such as Chain of Thought enable AI to explain how AI “thinks.” As explained by IBM, this “mirrors human reasoning, facilitating systematic problem-solving through a coherent series of logical deductions.”

Last year, AI use cases featured in the 12th edition of the WealthBriefing Tech and Ops Trends in Wealth Management 2024 report. 

Use cases
CLA’s Lourens was asked what sort of use cases are coming up. 

“There’s a broad range here – some clients want to get access to Microsoft Copilot or another generative AI platform/service. Some are looking for us to build a system that helps them make the question/answering process easier around their internal documents,” he said. “Some want to get rid of manual and cumbersome steps around document mining and data entry, and some are interested in creating 'AI agents’ to help them with broader, end-to-end tasks.”

“We don’t see one way that a firm should go as it is very dependent on where they are in their overall digital journey, but we also see clients still needing us to conduct data analysis and build custom models for them, which is something we have done for years before ChatGPT and generative AI of today existed,” Lourens said. 

In November last year, Broadridge Financial Solutions, which provides tech solutions to financial firms, gave this publication examples of AI enhancements such as BondGPT, which is powered by OpenAI GPT-4; it answers bond-related questions and assists users in their identification of corporate bonds on the LTX platform. This app distils 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.)

Also in the US, Raymond James, the wealth management house, said its advisors are using analytics to identify and support client touchpoints in the “Opportunities” application and machine learning in “Advisor Access” that predicts and recommends their next action for faster results. It is also piloting an enhanced intranet site powered by generative AI to relay relevant, timely information and resources.

Banks are devoting resources in various ways. For example, last year this news service spoke to Julius Baer, the Swiss private bank, about the work of its innovation lab.

Impact
NTT DATA’s report showed how significant AI’s impact is proving to be. GenAI is already making waves in the banking industry, with six in 10 organisations (58 per cent) fully embracing its transformative potential, an increase from 2023, when only 45 per cent of organisations had fully embraced GenAI.

Exploring viable use cases for AI in all its forms will be essential to justify the high spending it is causing, NTT DATA said. 

“Generative AI represents a pivotal moment for the banking industry,” said Robb Rasmussen, the firm’s global marketing and communications head. “While the potential benefits are enormous, the challenges of implementing GenAI are complex and varied, requiring careful navigation and a structured approach. Given the anticipated high spending on GenAI, achieving a return on investment is crucial.”