X9 Wants Fraud Data to Move as Fast as Money

The U.S. payments industry does not lack fraud data. It lacks agreement about what that data means.

The Accredited Standards Committee X9’s new Payment Fraud Forum, which holds its first meeting Sept. 10, is targeting exactly that problem. The initiative brings together participants including Federal Reserve Financial Services, The Clearing House (TCH) and the U.S. Faster Payments Council to develop common approaches for identifying, categorizing, reporting and sharing fraud across payment types. Its work could eventually contribute to industry standards.

“Payment fraud is an industrywide challenge that requires collaboration across the payments ecosystem. The Clearing House is pleased to participate in the new X9 Payment Fraud Forum and contribute to this important industry dialogue,” Kyle Caldwell, VP, Fraud Product Management, The Clearing House, told PYMNTS.

For banks, payment providers and corporate finance teams, the significance goes beyond another fraud working group. Payments has spent decades making money interoperable. The next challenge is making fraud intelligence interoperable, too.

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Fraudsters Already Operate Across Payment Rails

The payment industry’s fraud architecture remains heavily organized around payment types. There is check fraud, ACH fraud, wire fraud, card fraud and instant-payment fraud.

Criminals see a more fungible system. An attack can begin with a compromised corporate email account, produce a fraudulent supplier instruction, trigger an authorized ACH or wire transfer, arrive at an account at another institution and move again before anyone recognizes the complete pattern. Each participant sees the transaction from its own position. No participant necessarily sees the fraud from beginning to end.

The PYMNTS Intelligence report “Vendors and Vulnerabilities: The Cyberattack Squeeze on Mid-Market Firms“ found that hackers increasingly target middle market firms. These companies depend on third-party cloud providers, software-as-a-service platforms and managed service providers, which can leave them exposed.

That fragmentation becomes particularly problematic when institutions describe what happened differently. Was an incident business email compromise, authorized push-payment fraud, account takeover, vendor impersonation or some combination of them? The classification matters because fraud data becomes substantially more useful when institutions can aggregate comparable events.

X9 is effectively targeting that translation layer.

See also: AI Gives Cybersecurity a Backlog CFOs and CISOs Can’t Patch Away

The Next Payment Rail Will Carry Fraud Risk Data

Sixty-eight percent of financial institutions increased their fraud-detection budgets year over year, according to the 2025 “State of Fraud and Financial Crime in the United States,” a PYMNTS Intelligence report produced in collaboration with Block. That spending comes as 46% of institutions report increasingly sophisticated fraud schemes, up from 35% a year earlier.

The payments industry has invested heavily in making transactions faster and more information-rich. Fraud creates the inverse challenge: Information about risk needs to move at least as effectively as information about money.

The X9 forum plans to develop approaches for identifying, categorizing and reporting fraudulent activity across payment types. Its existing Check Fraud Industry Forum will become a subgroup, extending an effort previously centered on one stubborn fraud problem toward a broader cross-rail model.

The strategic question is therefore shifting from “Can my institution identify fraud?” to “Can the network recognize what my institution has learned?”

See also: Frontier AI Finds Cracks in the Math Behind Bank Security

If the ecosystem can agree on how fraud events, account behaviors and payment risks are described, institutions do not need identical fraud engines to exchange useful intelligence. They need a common enough language for one system’s signal to mean something to another. As accounts payable becomes more automated, corporations are generating more-structured information about invoices, suppliers, approvals and beneficiary changes. That information can serve two purposes: automate the legitimate payment and establish what a legitimate payment is supposed to look like.

For CFOs, that makes payment-data architecture part of the control environment. The quality of supplier identity, beneficiary information and approval data can determine not only whether finance operates efficiently, but whether external payment infrastructure has enough context to recognize an abnormal transaction.

It can also help with AI. Artificial intelligence doesn’t just create compliance work, it creates an entirely new layer of enterprise operating costs centered on governance. In a new PYMNTS eBook, executives from Visa, FIS, Synchrony, WEX, Billtrust, i2c, Thales, Velera, Bottomline and other industry leaders describe what they are learning as AI agents move from demonstrations into real operating environments.

If X9 can help establish that common language, the payoff will not be another layer of fraud prevention. It will be something the U.S. payments system still largely lacks: a fraud defense capable of operating at the scale of the network it is trying to protect.

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