Editor’s Note — This article is sponsored by MX. As with all sponsored content in Fintech Takes, this article was written, edited, and published by me, Alex Johnson. I hope you enjoy it!
According to a survey fielded by MX last year, 58% of U.S. consumers say they would be likely or very likely to trust AI to deliver proactive reminders for important financial tasks such as paying bills and saving money. The same survey found that a majority of consumers would also be likely or very likely to trust AI to provide a comprehensive breakdown of how they spend money (57%), offer personalized recommendations on where they can make changes to improve their finances (51%), and automatically save them money through options like transaction roundups (50%).
And in case you think these findings are not indicative of what consumers actually will do, when push comes to shove, you should know that the same survey found that 22% of consumers already use AI to help them manage their finances. And given the pace at which AI has been evolving and the pace at which consumers are adopting AI tools, that percentage would almost certainly be higher today.
Put simply, this is already happening. Consumers are using AI to ask questions about their finances, research new financial products and strategies, and, in some cases, to acquire those products and execute those strategies on their behalf.
What this means for financial institutions is straightforward, even if the implications are uncomfortable: You don’t have to build anything with AI for AI to change the relationship you have with your customers.
When your customers ask AI to analyze their spending, explain their loan terms, or find a better savings rate, the AI needs data. And it will get it, either through a controlled channel that you’ve built, or through the backdoor however it can.
Because every smartphone has AI on it now, every smartphone is now an on-demand data aggregator. There is no regulation or bilateral agreement that will prevent this decentralized data sharing from happening. If the consumer wants it and is willing to provide their bank account credentials, AI will make it happen.
The question isn’t whether your customers’ AI agents and chatbots will access your data. It’s whether you’ll have any say in how that happens.
Playing Defense
Financial institutions need to build controlled pathways for AI to access customer data; systems that grant granular, permissioned access, enforce rate limits, and eliminate the need for customers to hand over their login credentials directly to a third-party AI tool.
The U.S. was already moving in this direction — building APIs and reducing the need for screen scraping — thanks to the work that banks, fintech companies, data aggregators, and standard-setting organizations like the Financial Data Exchange (FDX) have been doing.
However, AI has given this foundational work new urgency. FDX launched a new initiative in April 2026 focused specifically on the safe, interoperable integration of agentic AI in financial data sharing.
Banks, particularly community banks, often assume infrastructure decisions like this are someone else’s problem to solve. They aren’t. If your customers are going to use AI to manage their finances (and as we’ve established, they already are) you need to be part of how that data flows, not an afterthought in someone else’s stack.
Playing Offense
But infrastructure alone is defense. The real strategic insight lies in what consumer behavior is telling us — that customers need more support. MX found that 37% of consumers feel that financial providers don’t do enough to support their financial needs. AI can bridge this gap.
Consumers using AI to understand their finances aren’t rejecting their financial institutions, they’re rejecting the way those institutions deliver insights today. They want financial guidance delivered naturally, through conversation, on demand. They want to ask questions the way they think, not parse charts, navigate menus, and manually categorize transactions.
Fintech companies like Public, Revolut, and Cash App have already figured this out, embedding AI agents and chatbots directly within their platforms. What they’re building is something genuinely new: a virtual financial assistant that lives inside your app, knows your complete financial picture, and can answer any question you have about your money in plain language. Not a FAQ or a simple, rules-driven chatbot. An always-on, personalized guide to your finances. That’s a fundamentally different kind of relationship between a consumer and their financial provider, and it’s one consumers are likely to find very hard to give up once they’ve experienced it.
Financial institutions face the same imperative: build this experience or watch customers find it somewhere else.
Data is Your Advantage
What separates fintech companies from traditional banks isn’t ambition or customer-centricity.
It’s architecture and data quality.
Cash App can launch an AI agent that answers questions about any part of a customer’s relationship with Cash App because their data lives in one unified, accessible environment. A large financial institution attempting the same feat would struggle because its data is fragmented across product lines, legacy systems, and decades of accumulated silos.
Building an effective AI agent requires two things most financial institutions lack.
First, all relevant customer data must be consolidated somewhere the agent can actually access it. Banks that have invested in open banking infrastructure often focus on the ability to pull in external data to build the full picture of a customer’s financial life, and that is the ultimate goal. However, the first step is for banks to get a handle on their own first-party data, which, if properly unified and structured, is the foundation any great AI experience is built on.
Second, data must be clean, well-categorized, and structured for machines to parse efficiently. This isn’t just about accuracy (though that’s very important). It’s about economics. Unstructured data burns tokens. Clean, tagged data means faster, cheaper, more accurate responses. The assumption that AI can handle messy data, so there’s no need to clean it up, is a costly mistake. And beyond that, hallucinations from inaccurate AI-processed data cost humans even more.
Solve Your Data Problem
The financial institutions that win in an AI-driven financial services landscape won’t be differentiated by the models they use. Everyone will have access to the same frontier models, built by the same 3-4 AI labs.
The differentiation will come from the data that empowers those models. Data determines the quality of the experiences you can build on top of them, the accuracy of the answers they can give, and the breadth of what they can act on.
Empowering AI to deliver value for your customers is, fundamentally, a data problem. And the time to solve it is right now.
