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We've officially entered that weird little window before fintech conference season goes into overdrive. The inbox is getting louder, fall campaigns are coming together, and my team is about to be booked and busy.

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You know the ones. The companies that have become verbs in our industry.

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"The winners of the AI revolution? Those companies haven’t been determined yet," said Jose Rasco, Chief Investment Officer at HSBC, at a recent press event in New York.

"Remember, Google didn’t go public until 2004,” he said. “We’re still at the beginning."

In fintech, that's not a reason to wait. It's a reason to move.

The infrastructure, trust systems, and client relationships being built right now will determine who wins, and the window to build them is open today, not indefinitely.

Rasco sees the AI revolution moving through two distinct phases.

The first is convergence, where combining technologies produces exponential rather than additive results.

The second is diffusion, where AI moves beyond the companies building it and spreads across every sector of the economy.

"When you put one plus one together, you get three, five, and seven," he said.

"The earnings delivered and the stock price performance of companies using AI is double what you’re seeing with others."

The line, he argues, is parabolic, and we’re in the early part of the curve.

While the defining companies of the AI era may still be forming, the race to determine how AI improves financial lives is well underway.

Inside the core banking software powering most U.S. banks. Inside the advisor relationships managing trillions in assets. Inside the consumer apps that 10 million everyday Americans use to manage their paychecks.

The companies building them aren’t unknown startups.

They’re some of the most established names in banking, wealth management, and consumer fintech.

And they're all arriving at the same conclusion from different directions: the more AI advances, the more human relationships matter. The data already shows it.

In June, HSBC released new findings from a survey commissioned by Ipsos Asia Limited of 10,000 affluent and high-net-worth individuals across 10 markets, including more than 1,000 in the U.S., exploring how people use AI in financial and investment decision-making.

Fifty-seven percent of U.S. affluent investors use AI for financial and investment tasks. But only 7% cite AI as the most influential factor in their last investment decision.

When asked where their last investment idea came from, 59% said financial professionals and institutions, versus 19% who cited AI.

The pattern sharpens at the highest wealth levels.

Among U.S. high-net-worth investors, 67% say their last investment idea came from a financial professional. Only 16% cite AI.

Among investors who use AI heavily, 77% still cite the need for reassurance from a human advisor, while 68% look to advisors for strategic expertise.

"AI has democratized access to information," said Racquel Oden, Head of International Wealth Management and Private Banking, U.S. at HSBC.

"But information isn't advice,” she said. “That's the distinction."

The gap between access to data and the judgment to act on it is reshaping how fintech companies think about their role.

Laurel Taylor started Candidly in 2016 to solve one of the most stubborn problems in American personal finance: the collision of student debt and wealth building.

A decade later, the platform has raised more than $40 million and counts UBS, Fiserv, and Salesforce among its clients, embedding its tools inside the financial institutions and employers that serve millions of Americans.

The shift she's watching in fintech is unmistakable: "The question is no longer just how fast to grow," she said. "It's about scaling trust."

That is the race. And right now it’s playing out on three fronts.

Fiserv and FIS together underpin the core technology of thousands of U.S. banks, credit unions, and financial institutions.

The scale of what they collectively power is staggering.

FIS services $8 trillion in assets, counts 95% of the world’s leading banks among its clients, and serves 14,000 financial institutions across 150 countries, including 58% of large and regional U.S. banks with more than $10 billion in assets, according to the company.

Fiserv processes 25,000 financial transactions per second at peak, serves 10,000 financial institution clients, and has 1.6 billion issuing accounts on file across six million merchant locations globally, according to the company.

When these two companies make a bet on AI infrastructure, it is a signal about where the entire financial system is heading.

Both launched major AI infrastructure plays this year.

Both are making the same argument: this transformation requires rearchitecting from scratch, not optimizing existing processes, but questioning whether those processes should exist at all.

"If a process is A plus B plus C, you don’t ask how do I optimize A, B, and C," said Dhivya Suryadevara, then-President of Fiserv, who came to the role after leadership positions at GM, Stripe, and UnitedHealth. “It’s literally, do I even throw B out? Does this process even need to exist?”

Editor’s Note: Since this interview, Suryadevara has resigned from Fiserv, according to a July 7 SEC filing.

That first-principles thinking shapes everything about how Fiserv built agentOS, an AI operating system for banks developed with OpenAI and AWS, covering workflows from commercial loan onboarding to AML triage.

The platform is LLM-agnostic by design, with nine fintech partners already signed to its agent marketplace. But Suryadevara is deliberate about one governing principle above all others.

"Instead of saying build it and then make sure it becomes compliant, we're building it ground up with the right rules in place," she said. "You can't do that as an afterthought."

The early results from beta suggest banks agree.

Operational intelligence, automated report generation that previously consumed significant manual effort, is already live.

Commercial loan onboarding is in beta with City National, compressing a process that used to require months of manual document review into something agents can handle end-to-end.

Deposit intelligence, which helps banks understand client retention risk in real time, is next.

"We want our clients to have choice," Suryadevara said.

"The way we do that is by giving them an opportunity to deploy whatever agents they need in a governed and safe way."

The inbound from Fiserv’s client base after the agentOS announcement has been strong, according to the company.

Banks aren't asking whether to adopt AI agents. They're asking which ones to deploy first and how to do it safely.

Financial institutions are no longer in a wait-and-see mode. They're in a build-and-govern mode.

When asked what Suryadevara’s vision is for what banking operations look like by 2030, she said adoption will be iterative, starting with high-fidelity, lower-stakes use cases like report generation and loan onboarding, then broadening as trust in the systems builds.

Roles will evolve toward what she calls the checkers, prompters, and architects, people who know how to use AI tools to think bigger, not just faster.

"The marginal cost of intelligence is dropping significantly," Suryadevara said.

"If you’ve not been exposed to area X, Y, and Z, that doesn't matter anymore. You can open Claude or ChatGPT, spend a weekend getting deep in a topic, and come out quasi-expert in a domain you were completely unfamiliar with."

FIS made the same bet from a different angle.

In May, the company announced a partnership with Anthropic, deploying a Financial Crimes AI Agent already live at BMO and Amalgamated Bank, designed to compress anti-money-laundering investigations from hours to minutes.

President and CEO Stephanie Ferris has been explicit: FIS isn’t trying to become an AI model company. It’s positioning itself as the data control layer between frontier AI and the financial system.

Inside FIS, Sherry Baker, Head of Global Wealth Products and Services, runs Reliance Trust Company, a regulated entity actually deploying AI in its own operations, not just advising banks on how to do it.

FIS has also partnered with InvestCloud to integrate AI directly into its wealth platforms, Baker said.

At Reliance Trust, AI agents handle the operational work of processing transactions, but not all the way through.

"Where the human operator has to push the button and send a billion-dollar trade to DTCC, that stops," Baker said. “They can review everything and make sure it's in good order.”

A billion-dollar trade pausing for human review is what governance by design looks like in practice.

It’s a deliberate architecture decision about where human judgment remains non-negotiable.

Baker is hoping her team will be among the first to have Anthropic engineers embedded directly with FIS to accelerate the build, a signal of how seriously the company is treating the deployment.

In the meantime, Baker's democratization point connects the infrastructure investment to its real stakes.

"Forty-six percent of the US population is mass affluent," she said. "If we can bring these services and tools down market, it really opens up who they can sell to, who they can create offerings for."

The infrastructure layer isn't just about making banks more efficient. It's about making the financial system accessible to more people.

👉🏽 Read the full Forbes story here.

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Loving this classic pop vibe from one of my favorite girl groups. Full disclosure: I binged Pop Star Academy, the show that formed Katseye, on Netflix. I'm endlessly fascinated by the music business. The strategy, the storytelling, the fandoms, the branding. It's all so interesting. Who knows, maybe that's my Act 2 someday. 😉

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