Governor Cook moderates the “Emerging Innovation, AI and Financial Inclusion” panel, July 14, 2026

Good afternoon. Thank you, Governor Bar and Tim for your insights. A panel turns to one of the most consequential forces reshaping the economic landscape, artificial intelligence. We will explore AI's promise and risks for financial inclusion as this technology spreads through our financial system. As a Fed governor, I care deeply about financial inclusion because it connects directly to our dual mandate. When people have access to affordable credit, saving tools, and sound financial advice, they can participate more fully in the economy. The topic of AI and financial inclusion is central to my work at the Fed. as an FOMC member setting monetary policy, as chair of the financial stability committee, and as a member of the committee on consumer and community affairs. The speed of AI adoption is remarkable. In our most recent small business credit survey, nearly half of small employer firms reported using AI and 71% saw increased productivity. But we are still in the earliest stages of understanding how AI will reshape financial services. I am optimistic about AI's potential to demystify sophisticated financial tools. Around here at the Fed, I've been told I am too optimistic, but I'll take that. Historically, personalized financial advice and wealth management were available mainly to those who could afford them. AI can make high-quality financial guidance accessible to everyone regardless of income or wealth. But I have been asking hard questions about AI for several years before coming to the board. And we need to continue to ask hard questions. What happens when AI tools fail? Who is accountable? How do we ensure AI does not perpetuate existing biases in lending? How do we protect consumers data? Are the benefits reaching communities who need them the most? Every general-purpose technology from the steam engine to the internet has brought opportunities and disruptions. My research on innovation has shown that when access to new technologies is equitable and appropriate, guards exist, society benefits broadly. Today, we'll cover how consumers use AI for financial advice, how institutions deploy these tools, and which risks we need to mitigate. I'm eager to hear from our panelists about the remarkable possibilities and real challenges. First, allow me to introduce them. Starting to my immediate left, we have Sanjay Subermanian who leads PWC's technology alliance with Anthropic and the firm's global AI analyst relations practice. With over 25 years of experience, he helps large organizations put emerging technology to work. Sanjay will ground us in practical real-world applications of AI in business and banking today. Laura Blatner is head of impact at the bike shop at MIT. Now, you know, when I saw this, I got so excited because I used to live in Cambridge. When I was at Harvard, I had a bicycle and all those bike shops were closing. So, I thought finally one is opening. [laughter] Okay. And apparently this bike shop in Cambridge is elusive because this one is the bike shop at MIT and applied AI lab. still have to keep looking for them and advises he advises philanthropic funders on evaluation standards for AI products serving low-income Americans. Formerly a professor of finance at Stanford GSB, Laura brings the academic rigor we need to understand AI's deeper implications for financial inclusion. And then we have Sarah Willis Air. Sarah is executive vice president at Saverlife, a nonprofit financial technology company. Saverlife has developed an AI powered navigator tool to democratize personalized financial guidance. Sarah will help us understand how AI can serve consumers who need better financial tools the most. Thanks to each of you for being here today. Laura, let me start with you. So the bike shop research lab pairs behavioral science with AI research which is unusual. Why does behavioral science add when we think about AI and financial inclusion? What does it add?

That was really smart, Laura, because when I saw a bike shop, I got this warm, cuddly feeling. So, thank you all for being thoughtful about that. Sarah, what are you learning about how low and moderate income households are actually using AI for financial decisionmaking? Are there any surprises in the types of questions they're asking?

That's fascinating. You work directly with Sanjay, we're returning to you. You work directly with the teams building today's frontier AI. What can these systems now reliably do with someone's money and where does the hype still outrun reality when it comes to financial inclusion in particular?

So, it's interesting. I hear all of you, all three of you saying some version of information is power and you can empower people not by telling them what to do but by giving them the options and letting them use what is uniquely human and that's the decision making capacity. That's what I'm here and that can be empowering. That's great to hear. So I'd like to ask a question to the entire panel. So I'd like all of your views for this next question. The perspective of the work that you're doing is fascinating. How have you seen AI benefit consumers in the context of financial services? You want to start?

And you're seeing that right in the work that you're doing.

How about you Laura?

So, it's empirically based. Really is empirically based. So that is connected to my next question for you, Laura. It's easy to build an AI model that gives financial advice. It's hard to know whether the advice is good. How do you close that gap? Is this the way you close that gap by depending on empirical evidence?

So that's really interesting. So what about reliability? So you have these AI models that are interactive and they're learning and they're giving advice, but one of the issues is doing that consistently. So how do you make sure that these tools are reliable using the framework that you just articulated?

If I can I just want to ask one quick question. When you say that you're using behavioral science, are you mainly using behavioral economics or are you reaching out into other fields?

Okay. So you hit the daily double. Economists can't be trusted with everything. we're, you know, we can't be trusted all the time [laughter] and we do need to reach deeply into other fields. So you went it was a rhetorical question. [laughter]

You gonna say something?

Oh yeah.

Depends on.

Depends on the women. [laughter]

Exactly.

Yes.

In general. In general. In general.

Um, so that's really interesting because we can certainly think of different tools that could be helpful. I mean the example that you bring up, Carbine has a large literature in economics and basically I've decided to outsource car buying to somebody else like Costco.

So that's a suggestion that's a recommendation because I'm going to there's a dollar value put on how much I would lose if I were to do it myself. And I know what kind of trouble I would get in if I were bargaining for a car. So outsourcing it is absolutely the best thing. So that's right. It does depend on the human. Absolutely. But the car buying example I think is really instructive. So Sarah, let's turn to you. You emphasize designing AI to support consumers rather than replace their judgment for high stakes financial decisions. Have you approached responsible AI and guards while building the financial navigator?

So one thing I hear you three saying too was that the iterative process is absolutely fundamental. I mean the testing, certainly I was saying to you all when we had an earlier conversation, the thing that I absolutely hate because I have to interact with these AI tools all the time is the lack of testing and just sort of, and this isn't even for low and moderate income users specifically, but the care that's taken is appreciated. And I'd like to just say I mentioned the name of a car buying service. That was not meant to be product placement. So, you know, we are here at the Fed. So a car, a generic car buying service. So let me clean that up. Okay. So, Sarah, beyond engagement, what early evidence are you seeing that personalized AI guidance may be influencing financial behaviors or outcomes?

We need those survey respondents over at BLS.

Okay.

From Saver Life. Yeah. There's a question that I'd like to ask the three of you. But before I do that, one common question about how your lessons apply to potentially other organizations. But Sanjay, before we run out of time, I'd like to ask you one first. So you sit close to where capital flows, including private equity, are products for underserved communities have always been hard to fund because the economics were thin, or let me be more careful about that. The traditional way these are measured might be thin, right? Is AI really changing which products get built for these customers or just making the old ones cheaper to run?

Okay. Good. Good. So the common question that I wanted to ask all of you before I get to the last question is how would you advise other institutions, organizations who would like to augment financial inclusion? What would you tell them about the lessons you've learned from your own endeavors? Why don't we start with you, Laura?

So just one follow-up question then. The use of synthetic data is something that Sanjay was talking about and it's spread very widely. Is that something that you all use in your evals?

Yeah. Never helpful.

Sarah, what about you? What are some lessons for other organizations?

Right. Right. Agree. Do you want to?

Thank you. So the final question is this one. Everyone on the stage owns a piece of this: the labs, the funders, the people building products, and the regulators. From your view across the whole ecosystem, what does each of them most need to get right so that AI widens access without leaving people more exposed?

That seems pivotal to trust. Crucial for trust.

Great. Laura, what about you?

Agreed. Agreed. So I would just like to add one thing as a regulator. One thing that I walked in the door doing at the Fed in 2022, even before ChatGPT appeared, was holding roundtables across the country with all the stakeholders because I noticed that they weren't talking to one another. So to your point about collaboration, you can't understand what people really need, what communities really need, unless you're talking to them and they're talking to each other. So this siloed thing, I've been in an academic career on an academic campus my entire career. I know about silos. People can live in silos. And for this moving so quickly, we really have to understand what everybody's learning as quickly as possible. And I understand that there are proprietary considerations, but I think a lot of those can be surmounted, especially when you're in a forum like this or a forum like we used to put together around the country and that I'm still putting together around the country. So this is community colleges, this is AI firms, this is venture capital folks, this is nonprofits who invest using these alternative data. And I think we don't necessarily have to use alternative data anymore because I think they're not so alternative anymore. So the data that we have access to, we have so many more data series available to us. So I would say as a regulator, we've got to get people talking to one another. That's our public role. The convening power of the Fed is one thing that I try to leverage, and I think other regulators can do the same. And we have to keep a human in the middle, we have to have these guard rails, we have to promote sandboxes. And it sounds like this is what you all are doing. You know, you're trying out a small sample and then you keep iterating to learn more from a larger sample. And from economics, although I just bashed it, that is a good habit to have and to hone. So anyway, I saw the five-minute marker go up just some time ago. So why don't I actually do something that is rarely done in such a forum: let me thank our guests. Thank you very much for coming. Thank you for your insights and thank you so much for your work. We couldn't get this all done without you. Thank you all and thank you all for attending. [applause]