Banks use AI and other automated controls to assess whether a payment looks suspicious, then combine that risk signal with account activity, authentication, and follow-up. A flagged transaction is not necessarily fraudulent, and it does not always have to be declined: possible responses range from closer monitoring or customer contact to reissuing a card or closing an account. The aim is to catch fraud while limiting false alerts and avoidable disruption, but no system can guarantee both outcomes.
What AI fraud detection does—and what it does not promise
In this setting, “AI” can refer to machine-learning models that identify patterns in payments or account activity. Neural networks are one example the Federal Reserve Board gives for assessing debit-transaction risk. That example does not mean every bank uses neural networks, or that every payment is judged by the same automated system. The Federal Reserve’s March 2026 supervisory testimony says some firms use machine learning for fraud detection and prevention, and notes that some capabilities have been in use for years.
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A fraud model estimates risk; it does not establish with certainty that a customer or payment is fraudulent. A legitimate payment can look unusual, while a criminal payment can resemble ordinary activity. Banks therefore need to decide not only what to flag, but how much evidence warrants a decline, an extra check, monitoring, or investigation. The practical goal is not “AI instead of human judgment.” It is a decision process that uses available signals and selects a response proportionate to the assessed risk.
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There is no comparable, industry-wide figure in the cited official sources for the share of legitimate transactions that banks’ AI systems falsely decline. That number cannot be inferred from fraud totals or from a detection experiment: they measure different things.
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How the decision loop works
1. Score a payment at authorization
Risk assessment can happen while a debit-card payment is being authorized. The issuer may evaluate the transaction using an automated model, including a neural network, as one possible mechanism described in the Federal Reserve Board’s Official Board Commentary on Regulation II. Authorization-time scoring can inform the decision before the payment is approved, but the commentary does not say that every bank uses the same timing or model.
A single transaction may not contain enough evidence to classify it. The commentary explains that an issuer may instead monitor sets of transactions initiated with a cardholder’s debit card. A payment that is unfamiliar on its own may be less concerning when considered alongside a customer’s usual activity; a series of individually plausible payments may look more suspicious as a pattern.
2. Add customer and fraud-pattern context
Rather than treating every payment as an isolated event, a system can compare activity with a cardholder’s typical transactions and with patterns associated with known fraud. The relationship among transactions can matter as much as any one payment. This context helps explain why an unusual payment is a risk signal rather than proof: legitimate customers also travel, change spending habits, replace cards, or make occasional purchases outside their normal pattern.
Post-authorization monitoring can add time and evidence. An alert may lead to closer review of a sequence of transactions or follow-up with the customer. The Federal Reserve commentary describes customer text alerts as one way a customer can report suspicious activity promptly. An alert is useful only if the bank’s response process can act on the report; it does not itself establish whether a payment is legitimate.
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3. Choose a response, not just a score
The response depends on the circumstances and the issuer’s assessment of current and future risk. The Federal Reserve commentary identifies possible measures including closer monitoring, contacting the cardholder, reissuing a card, and closing an account. These are not interchangeable outcomes: a temporary concern about one payment need not imply that the underlying account is compromised, while evidence of continuing risk may justify a more extensive response.
Some payment systems may request additional authentication or customer verification, but that creates its own trade-off. A customer can be inconvenienced by an unnecessary check, and a fraudulent payment can still succeed if the customer is manipulated into approving it. A bank should not be assumed to clear every hold instantly or to use the same escalation path for every product.
4. Feed outcomes into controls and review
Confirmed fraud, customer reports, investigations, and other outcomes can help an institution assess how well its controls work and where patterns are changing. The Bank for International Settlements’ Project Hertha report emphasizes labelled training data, a robust model feedback loop, and explainable algorithms. A model that does not learn from reliable outcomes can continue to miss new patterns or flag activity that is not actually fraudulent.
Feedback is not a license to change a model without oversight. Banks need testing and monitoring to detect whether a model’s performance or the environment it operates in has shifted. The Federal Reserve’s March 2026 testimony describes testing regimes and human-in-the-loop systems among controls used at supervised institutions, while emphasizing that governance and oversight should grow with the use of AI. This is a supervisory perspective, not a census showing that every institution uses human review in the same way.
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What changes when analytics span a payment network?
One bank sees activity associated with its own customers and accounts. A payment system that can lawfully and appropriately analyze a broader set of payment flows may be able to identify connections that are difficult to see at one institution—for example, related activity spread across multiple accounts or banks. This network-level view can supplement, not replace, a bank’s own customer-level controls.
Project Hertha, published by the Bank for International Settlements Innovation Hub on 5 June 2025, tested payment-system analytics for identifying financial-crime patterns in real-time retail payment systems. Its simulated dataset contained 1.8 million bank accounts and 308 million transactions; it used synthetic data and no real customer data. In that experiment, the project reported 12% more illicit accounts identified with payment-system analytics and a 26% improvement in identifying previously unseen behaviours. Those are results for the project’s synthetic-data detection task—not measured outcomes from a live bank deployment, guarantees of future performance, or estimates of false declines.
Hertha also highlights a model-design tension: increasing the ability to catch more suspicious activity (recall) can create more false alerts, while avoiding false alerts (precision) can leave more illicit activity undetected. The report’s percentages do not state how a live institution should set that balance, nor do they establish a universal service level. In practice, the appropriate operating point depends on the institution, the payment channel, the consequences of a mistaken decline, and the evidence available for follow-up.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsCross-institution analysis also raises practical, legal, regulatory, and privacy questions. It should not be described as banks freely pooling all customer data. Separately, the Federal Reserve’s SR 26-3 letter of 9 July 2026 discusses FinCEN’s clarification of information sharing under Section 314(b) of the USA PATRIOT Act. Participation in that sharing is voluntary and the letter situates its safe harbor in the statutory context of identifying or reporting activity that may involve money laundering or terrorist activity. That framework is distinct from the mechanics of an individual bank’s transaction-scoring model.
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Fraud and authentication change over time
Authentication can reduce some forms of fraud, but it does not eliminate the need to monitor behavior and adapt controls. In a joint report released on 15 December 2025, the European Banking Authority and European Central Bank said reported payment fraud in the European Economic Area reached €4.2 billion in 2024, compared with €3.5 billion in 2023 and €3.4 billion in 2022. These figures describe reported EEA payment fraud, not AI performance or global losses.
The same EBA and ECB release reports that card payment fraud was 17 times higher in 2024 when the recipient was outside the EEA. In that comparison, strong customer authentication was not legally required and often was not used for the outside-EEA recipient case. The figure is tied to that region and circumstance; it should not be read as a universal multiplier for every cross-border payment.
Fraudsters also adapt to controls. The EBA and ECB report describes attacks that exploit authentication exemptions or manipulate customers into approving fraudulent payments. A payment that passes an authentication step is therefore not automatically legitimate. Behavioral monitoring, customer reporting, investigation, and other controls remain relevant after authentication.
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The same release reports €2.200 billion in credit-transfer fraud losses in 2024, up 16% year over year, and €1.329 billion in card-payment losses for cards issued in the EU/EEA, up 29%. Those instrument-specific totals show why a single overall fraud figure can hide materially different patterns. They do not reveal how many legitimate payments were declined or which AI approaches produced a particular result.
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How bank approaches differ
“AI fraud detection” can describe different combinations of timing, context, action, and oversight. The distinctions below are more useful than a single accuracy score, especially because the available official sources do not supply comparable live-bank false-decline rates.
| Design choice | What it can show | What it cannot establish by itself |
|---|---|---|
| Authorization-time scoring | Risk can be assessed before a debit transaction is authorized; a neural network is one example in Federal Reserve commentary. | That every institution uses the same model, or that a score proves fraud. |
| Post-authorization monitoring | A sequence of transactions can be assessed as activity develops, with potential customer contact or closer monitoring. | That a single unusual payment should automatically be declined. |
| Customer-level context | Activity can be compared with typical cardholder behavior and common fraud patterns. | That a customer’s usual activity never changes or that every deviation is suspicious. |
| Payment-system analytics | Patterns spread across accounts or institutions may become visible; Hertha tested this using synthetic data. | Live-bank effectiveness, unrestricted data sharing, or a false-decline rate. |
| Authentication and follow-up | Additional checks or customer reports can add evidence, and authentication can address some fraud patterns. | That authentication prevents all fraud; users can be manipulated into approving fraudulent payments. |
Oversight and regulation matter as much as the model
In April 2026, the Office of the Comptroller of the Currency, Federal Reserve Board, and Federal Deposit Insurance Corporation issued revised model-risk guidance. It addresses model development and use, testing, validation and monitoring, governance and controls, and vendor or third-party products. Its approach is risk-based: practices suitable for one institution may not suit another. The guidance does not set enforceable standards or prescriptive requirements, and it excludes generative and agentic AI from its scope.
The OCC’s guidance says it is generally expected to be most useful for models supporting significant business lines, operations, services, and functions. For a bank, the relevant question is not simply whether a model is labelled AI, but whether the institution understands how it is used, validates its performance for its purpose, monitors it, and has appropriate controls around the model and any vendor supplying it.
Regulation II requires careful scope. The Federal Reserve Board’s Official Board Commentary describes debit-card issuer policies and procedures for fraud prevention, monitoring fraud volume and value, updating procedures as methods change, and choosing an appropriate response to suspicious activity. Those debit-card examples should not be generalized into one rule that governs every bank product or payment type.
What customers can do when a payment is flagged
A decline or alert can be frustrating, but it may be a protective response to uncertainty rather than an accusation. Follow the bank’s official instructions to confirm whether you initiated the payment, and report a transaction you do not recognize through the bank’s established channel. If an alert asks you to approve a payment you did not initiate, do not treat the request as proof that the payment is safe; contact the bank using a channel you already trust.
If a card is reissued or an account is restricted, ask what specific step is needed to restore access and whether recurring payments or other linked services need to be updated. The Federal Reserve commentary describes several possible issuer responses, but it does not promise a universal resolution time or a guaranteed way to reverse every hold. The bank’s instructions for your account and payment are the relevant next step.
How to judge claims about fraud-detection AI
- Check what was measured. More illicit accounts found, a model’s detection score, reported fraud losses, and legitimate transactions declined are different measures.
- Check the evidence setting. Project Hertha’s 2025 results came from a synthetic-data experiment, not real customer transactions.
- Check the payment and geography. Card payments, credit transfers, and other instruments have different fraud patterns; EEA figures do not describe the world total.
- Check whether a number includes friction. A detection uplift alone does not show how many good payments were delayed, challenged, or rejected.
- Check for governance details. Testing, validation, monitoring, explainability, feedback, and vendor oversight are part of assessing a model’s reliability—not optional extras implied by the word “AI.”
The reliable conclusion is limited but useful: banks can combine automated risk scoring with transaction history, authentication, customer reports, follow-up, and—in some settings—network analytics to make more informed fraud decisions. The evidence does not support a universal promise that AI catches every fraud or prevents legitimate payments from being blocked.
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