Open finance has spent years solving the access problem.
Permissioned APIs allow consumers to connect financial apps without handing over their bank credentials and hoping for the best. That is real progress, giving consumers more control and financial institutions a more secure, visible way to share data.
But access was never the whole problem. It was just the part that everyone could see.
The next question starts after an AI agent gets through the door: Can it trust what it finds?
A secure, well-governed API can still return data that is stale, incomplete, or easy to misread. When that happens, failure rarely announces itself. The response looks normal, the agent keeps moving, and no one may realize anything went wrong until a bad decision has already traveled downstream.
A person who sees something odd might pause or check out another source. An agent generally has no instinct that something “looks off.” It acts on the data it receives.
Most conversations about AI and open finance begin with the right questions: Who authorized the agent? What can it access? Can the consumer revoke that access?
Those questions are necessary, but they are also incomplete. They assume that once an authorized agent starts linking an account, checking a balance, identifying an institution, or validating payment details, the underlying information is accurate enough to support the next action.
Sometimes it is not, and that gap is where an agent's real risk begins.
Financial data is not a static reference table: institutions merge, names change, routing numbers are added, retired, or retained during a transition, and participation in payment services changes. The Federal Reserve’s E-Payments Routing Directory is synchronized with FedACH and Fedwire databases daily, which tells you something important: this information has a lifecycle.1
An API can deliver a perfectly formatted answer and still deliver the wrong answer for the moment.
The gap between an API working and an API being right isn't always obvious. It shows up in specific, predictable places, and three of the most common are identity, routing history, and freshness, each one capable of passing every technical check while still handing an agent the wrong answer.
A familiar name is not a unique identity
Search for a bank by name and you quickly run into variations on “First,” “Community,” “Citizens,” or “Farmers.” A person often brings extra context without thinking about it: the state, logo, branch, or town where the account was opened.
An agent matching name alone may not have that context. If it receives one plausible result, it can treat that result as the answer instead of a candidate that still needs to be resolved. The FDIC’s BankFind Suite reflects why identity involves more than a display name, including location, operating status, history, and a unique certificate number.2
Names are useful for people, but they are not reliable primary keys.
Routing data has history
The phrase “the bank’s routing number” makes the relationship sound simpler than it is. A financial institution can hold multiple routing numbers to support different processing arrangements, locations, electronic delivery needs, or routing numbers retained through a merger. ABA’s policy allows an eligible bank to hold a principal routing number plus as many as nine additional numbers without special board approval.3 After a merger, the surviving institution becomes responsible for the routing numbers involved, and the Federal Reserve provides options for converting ACH activity from a non-survivor routing number to the survivor’s number.4
A system designed around one institution and one routing number flattens that history into something neat and sometimes wrong.
Freshness is data, too
A balance without an “as of” time is not quite a balance. A cached routing record without an effective date is not quite payment instructions.
If freshness, provenance, and status are not part of the response, an agent cannot reliably account for them. Staleness does not look like an error to a machine; it looks like data.
We already understand this argument in security: automation increases the speed and volume of both legitimate activity and abuse. Accuracy works the same way.
A person may check a balance once, notice a discrepancy, and stop. An agent can repeat the same lookup across thousands of sessions before anyone recognizes a pattern. One ambiguous institution match can become a stream of bad decisions. One stale record can spread into caches, summaries, and downstream systems.
Automation gives small data defects a much larger blast radius often without a person present at the moment a questionable value should have been challenged.
The tempting response is to correct the records, close the tickets, and declare the dataset clean. That fails because the real world keeps changing. Whatever is correct today begins aging tomorrow.
The answer is not a cleaner snapshot. It is an operating discipline:
This matters more as open finance moves toward standardized, machine-readable data, which is what Section 1033 calls for, even though its implementation timeline is currently stayed while the CFPB conducts a new rulemaking.5,6
Structure helps an agent read the data, but it does not, by itself, make the data true.
The second half of trust
We've written before about why institutions need to know who's requesting access to their data. This is the other half of that equation: knowing whether what they're accessing is actually true.
Underneath the AI framing, this is a data-quality problem, one that AI simply makes impossible to ignore.
At human scale, ambiguity can hide inside manual review, institutional knowledge, and the occasional gut check, informal safeguards that disappear once you move to machine scale. Agents expose every place where a system quietly depended on someone knowing what the data was supposed to mean.
Open finance has made real progress on the first trust decision: Can this agent get access through a secure, permissioned channel?
Now we must take the second just as seriously: Is the data behind that channel current, correctly identified, and complete enough to act on?
Secure access gets an agent through the right door. Trusted data keeps it from confidently walking into the wrong room.
Akoya's Open Finance Solution is built to solve the first problem: a secure, standards-based API layer with consent visibility and audit trails, so your institution can see who's accessing what, and under what authority. That's the foundation. What runs on top of it, the accuracy, freshness, and identity resolution of the data itself, is the work this piece is calling institutions to take just as seriously.
To see how Akoya helps institutions build that foundation, visit akoya.com/openfinance or contact us.
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