Focusing On Durable AI Use Cases For Wealth Management

We speak to an innovation platform for the wealth sector about what AI use cases make most sense and are most likely to gain traction.

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Whatever else one can say about 2024, one fact that appears
uncontroversial is that this has been the year when AI went
increasingly mainstream. It is an ever-present feature of
conversations and stories. 

In picking through the thickets of all this, the focus must be on
the valid use cases that AI brings to the wealth management
table. As with the “robo-advisor” trend of a decade ago, the key
is not getting beguiled by the “shiny object” attractions of tech
and understanding what really improves client service and
business. (Notably, references to “robo-advisory” are thin on the
ground today, while the term was everywhere a decade ago.)

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; personalized banking and financial planning;
algorithmic trading; customer relationship management; regulatory
compliance; robo-advisors; and natural language processing (NLP).

When such points arise, it is perhaps inevitable in this time of
rising labor costs – increased by forces such as the UK
government’s increase in employers’ payroll taxes (National
Insurance Contributions) – that people assume AI will replace
them. To an extent, that’s true but it is more complex than that
– AI will replace some roles, but augment humans’ capabilities
and hopefully productivity, in others.

Rob Pettman (pictured), president and chief revenue officer, at
TIFIN, an AI and
innovation platform for the wealth sector, told this news service
that there are three main elements to the AI use cases for wealth
management: growth, cost reduction and risk management/reduction.

“In 2025, we will see widespread adoption,” Pettman, who joined
the firm in April, said. He is based in Charlotte, North
Carolina. 

One use case is helping the distribution of wealth management
advice at scale,” Pettman said. “We have talked about
personalization for a long time in this business…but it is still
a mess. AI changes that market to be more scalable.”

To some extent, that comment dovetails with observations on how
delivering
mass-affluent wealth management requires the ability to
handle mass-customization. AI, while not a silver bullet, holds
the key in some ways to making this a reality. 

At Broadridge, the
US-headquartered group providing 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 previously
been the case. (LTX is an electronic trading platform for
corporate bonds.)

The rise of generative AI has sent shockwaves through financial
services. (This news service mused on the
implications here.)

Time for specifics

Pettman wants to see more focus on specific applications for AI
and less general commentary and noise, however understandable
that might be. 

“At the moment there is a certain amount of [AI] exhaustion,” he
said. 

Giving an example of a specific use of AI, Pettman discussed
TIFIN’s offering, SAGE, which focuses on portfolio construction
through AI-powered proposal engines and analytics. He highlighted
how generative AI can analyze documents, identify patterns, and
extract insights. By combining AI with data and analytics, SAGE,
he said, delivers scalable personalization. It generates tailored
commentary by integrating research insights with client account
analysis or customizing portfolios based on manager research.
This level of personalization at scale was previously impossible,
he said. 

AI can help with producing documents and information that
investment committees can examine, such as private markets and
other areas that eat up hours of time, he continued. 

“You can reduce due diligence times by 80 per cent,” Pettman
said. 

In-person and online

In-person analysis and meetings, in the initial stage of an
investment, are still unavoidable, but AI can then slice through
the time involved in subsequent stages, he said. 

Another important use case area is risk management. This news
service asked Pettman about the hot topic of outsourcing to
third-party providers and regulators’ concerns about maintaining
standards in this case, as well as the function of “model
matching” so that clients can get the investment that they want
and are suited for. 

AI can help what is happening to an investment model
continuously, so that clients can understand why a particular
decision took place. This also helps a wealth manager demonstrate
their value proposition, Pettman said. 

TIFIN has been busy. In July, it rolled out an India business to
expand ts direct-to-customer and business-to-business AI for
wealth applications. The new entity is called TIFIN India. It was
launched with the DSP family group, a financial services firm in
the country.

TIFIN’s companies have included 55ip (sold to JP Morgan),
Paralel, and Magnifi, TIFIN Wealth, TIFIN Give, TIFIN AG, TIFIN
AMP, Sage, Helix, and TIFIN @Work. TIFIN has been backed by JP
Morgan, Morningstar, Hamilton Lane, Franklin Templeton, SEI,
Motive Partners, and Broadridge among others.