Future Of Financial Advice – More Co-Pilot Than Autopilot


OPINION OF THE WEEK: Future Of Financial Advice – More Co-Pilot Than Autopilot

The authors of this article argue that they expect generative AI will take root across the wealth advisor value chain.


How will AI affect advice to wealthy people? This has to be
one of the most dominant debates right now, given that artificial
intelligence – however one defines that term – has grabbed so
much of the tech and strategy agenda in this industry. 


As the editor takes a break from weekly commentaries, we give
the pulpit to John Lin, Abdul Abdirahman, and Zoey Tang,
investors at F-Prime Capital, a
US global venture capital firm, located in Cambridge,
Massachusetts. The editorial team welcomes this contribution to
debate. Email tom.burroughes@wealthbriefing.com
if you want to respond. The usual editorial disclaimers
apply. 


When you put “wealth management” and “genAI” in the same
sentence, most minds jump straight to some version of “autonomous
finance.” However, that’s a concept that would have to overcome
significant trust hurdles with advisors and the public to gain
widespread adoption. According to research by Vanguard, the
personal connection and trust that exists between financial
advisors and consumers drives about 40 per cent of the value in
any advisory service. 


Over the last 10 years, advisors have started to transition from
“stock pickers” to client relationship builders. As a result, new
inefficiencies have emerged in the value chain: advisors now
spend their time collecting and synthesising information across a
sprawling and outdated tech stack to help clients make decisions.


So, while generative AI isn’t going to put our money on autopilot
any time soon, it has the potential to save advisors’ time by
handling the more repetitive and labour-intensive aspects of
their jobs. As a result, advisors will be free to build deeper
relationships and trust with an expanding client base. 


The current state of play

Most financial advisors juggle five different tech platforms
every day: 


 


Many of the steps outlined above involve pulling together
disparate information, often supported by different tech stacks,
and synthesising it all to generate insights a unique
strength of generative AI technology is that it can quickly
process large amounts of data. 


Freeing financial advisors from mundanity

Generative AI will enable the construction of new “co-pilots” for
financial advisors. Seeking to cut costs in an environment of fee
compression, firms are eager to automate routine tasks. Those
tasks include reviewing legal documents, opening accounts,
preparing client presentations, adjusting asset allocation,
requesting query service, addressing ad hoc questions, and other
activities beyond their core role of advising clients, which
currently take up 36 per cent of advisors’ time. Put another way:
the average advisor spends more than two hours “behind the
scenes” for every hour they spend with clients. 


One way that CIO offices have attempted to streamline these
processes is through the creation of in-house research databases.
However, advisors still burn much of their workday conducting
research, digesting information, and surfacing the most relevant
insights in response to specific questions by their clients. It’s
important to remember that most of those clients are seeking
intuitive responses from a human they trust, rather than highly
technical or precise answers. 


Generative AI can swiftly perform the synthesising legwork for an
advisor, who can then spend more face-to-face time with the
client, create suggestions, and ponder implications for their
personal portfolios. 


Some incumbents (such as Morgan Stanley) are taking the time to
build these solutions in-house. However, legacy tech debt
means  that they usually take a long time to build – for
example, Bank of America spent 10 years and $100 million to build
its proprietary Merrill One Wealth Management platform. Others
are understandably open to partnerships – see JP Morgan”s and
TIFIN’s initiative to develop AI-enabled fintech companies.
Meanwhile, startups such as Parcha envision a co-pilot that goes
beyond answering questions and can instead complete tasks
autonomously. 


Many startups have built compelling AI-enabled products for
advisors: Muse finds tax deductions and credits; Toggle assists
advisors with investment research and addresses client questions
based on the firm’s proprietary research; Greenlite and Parcha AI
assist wealth management companies with KYC review and fraud
reduction; and OneAdvisory is automating the collection of client
data and account opening while maintaining data synchronisation
across the advisor tech stack. In the past, companies such as
DriveWealth helped fintech players build investment products for
their end users. Going forward, we see a similar opportunity for
API-based solutions that help fintechs build GenAI-enabled
co-pilots for wealth managers. 


Over the last five years, a few trends have emerged that create
opportunities for GenAI-enabled wealth management solutions:

 

— Growing data pools: The amount of data available to wealth
advisors (from their internal systems, partners, third parties,
and elsewhere) has significantly increased over the past decade.
If advisors can quickly understand and harness this data they
will be well-positioned to optimise financial planning for their
clients, and offer tailored products and data-driven advice.


— PE involvement: A wave of consolidation in the wealth
management industry, with significant participation from private
equity firms, would suggest an easier go-to-market path for
startups by selling to a single decision maker who oversees many
advisors. A GenAI solution that gives advisors more time to reach
new customers would be an attractive tool in a PE firm’s search
for cost-cutting and efficiency-boosting tools.


— End-to-end options: We have also seen the emergence of
end-to-end RIA tech stacks from companies like Farther Wealth,
Zoe Financial, and Savvy Wealth. Financial data is currently
fragmented across a broad advisor tech stack, which hampers the
ability to take advantage of GenAI in this field. However, an
end-to-end solution could create a proprietary data lake to
effectively power GenAI tools. These platforms also have no
legacy tech debt, reduce per-head-cost of growing an advisory
business, and could therefore accelerate AI adoption in the
industry. 


— Increasing budget share: Finally, wealth managers are
spending more on software – an extra 10 per cent of wealth
managers’ budgets have gone to third-party tech purchases since
2018, mainly to replace the industry’s 20-to-30 year-old existing
stack. This means that advisors have cause and budget to seek out
new solutions, and a wedge with generative AI could be a great
catalyst to switch. 



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