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Banks use AI and other fraud controls to estimate whether a payment is suspicious, then choose a response based on the risk and available evidence. A score is not proof of fraud, and a flagged payment is not automatically blocked: an issuer may allow it, monitor related activity, ask the customer to verify it, or take stronger action. The challenge is to catch fraud while limiting false alerts, avoidable declines and customer friction. No reviewed official source establishes an industry-wide rate for legitimate transactions falsely declined by bank AI.
What AI fraud detection does—and what it cannot guarantee
Fraud detection is a decision process, not a single AI model that reliably labels every payment as “fraud” or “legitimate.” A bank can assess risk during authorization, consider the payment alongside activity over time, and use the resulting evidence to choose what to do next. Some institutions use machine learning for fraud detection; that does not mean every bank uses the same models, signals or procedures.
The goal is to identify enough suspicious activity without generating so many false alerts that legitimate customers are repeatedly interrupted or declined. Improving one side of that balance can affect the other: a system that flags more activity may catch more fraud but also create more cases requiring review. The Bank for International Settlements (BIS) describes this as a balance between recall (finding relevant illicit activity) and precision (limiting incorrect flags). Those are model-design objectives, not published service levels for banks.
For debit cards, the Federal Reserve’s Official Board Commentary on Regulation II gives neural networks as one example of a mechanism an issuer may use to assess transaction risk. This is an example, not a claim that neural networks—or AI at all—are used by every issuer or for every payment type.
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How a bank evaluates a payment in context
1. Score the transaction, sometimes before authorization
When a payment is presented, an issuer may assess its risk as part of authorization. A model can consider the transaction and how it relates to other information available to the issuer. The Federal Reserve commentary describes this type of debit-transaction risk assessment, but does not prescribe a universal model or list of inputs.
2. Look beyond the isolated payment
A single payment may not provide enough evidence to decide whether fraud is occurring. The same transaction can look different depending on the customer’s typical activity, what else has recently happened on the account, and whether the sequence resembles a known fraud pattern. In its Regulation II commentary, the Federal Reserve notes that an issuer may monitor sets of debit-card transactions because it may be unable to determine whether one transaction is fraudulent in isolation.
That context helps explain why a risk score is an indication, not certainty. A payment outside a customer’s usual pattern can warrant attention without being fraudulent; a payment that appears ordinary on its own can be suspicious when seen as part of a larger sequence.
3. Decide what to do with the alert
A bank’s response can be proportionate to the circumstances and the assessed risk. The Federal Reserve commentary describes closer monitoring, contacting the cardholder, reissuing a card or closing an account as possible actions in different situations. It also describes text alerts that can help customers report suspicious activity. It does not say every flagged transaction must be blocked, or that a customer can always clear a hold immediately.
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These interventions have different consequences. Monitoring may preserve the ability to make a purchase but allow further suspicious activity to continue if the concern is genuine. Contacting a customer can add friction and depends on reaching the right person. Reissuing a card or closing an account can be more disruptive, so those measures are distinct from simply asking about one payment.
How banks can limit avoidable declines
Reducing mistaken declines depends on using evidence carefully, not on assuming that AI can eliminate errors. A system can compare a payment with customer activity and known patterns, then use follow-up or monitoring where the evidence is ambiguous. The Federal Reserve’s examples support this graduated approach, but they do not establish one required decision ladder or show how often each option is used.
For readers trying to understand a declined or questioned payment, a bank’s alert or verification request is a security measure—not proof that the customer did anything wrong. Follow the bank’s instructions using a contact method independently known to be genuine, such as the number on the card or the bank’s official app. If the payment is legitimate, tell the issuer; if it is not, report it promptly. The sources do not establish a universal method or timeline for resolving holds, so the bank’s own instructions govern.
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A false positive in this context is a legitimate payment or account activity incorrectly flagged as suspicious. Yet an alert, a request for verification, a declined authorization, and a confirmed fraud case are not the same outcome. A useful comparison would need to specify what counts as a false positive, which payment types and institutions are included, the period measured, and whether the figure covers model flags or final customer-impacting decisions.
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The official sources reviewed for this topic do not provide a comparable industry-wide percentage of legitimate bank transactions falsely declined by AI. BIS experiment results about finding illicit activity and regional totals of reported fraud measure different things; neither can be used as a false-decline rate.
When payment-system analytics add a wider view
One bank may see only part of a criminal pattern when activity spans accounts or institutions. Payment-system analytics can add a broader view by examining patterns across a payment system. This is supplementary to an individual institution’s controls, not evidence that banks freely pool all customer data.
BIS Project Hertha, published on 5 June 2025, tested this approach using a simulated dataset of 1.8 million bank accounts and 308 million transactions. The data were synthetic; no real customer data were used. In that experiment, payment-system analytics helped identify 12% more illicit accounts than the comparison approach, and improved identification of previously unseen behaviours by 26%. These are results for the project’s synthetic-data detection task—not a live-bank deployment result, a guarantee for other settings, or a measure of false declines.
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The project also highlights practical considerations: useful models need labelled training data, a robust feedback loop and explainable algorithms. Applying network analytics in real payment systems raises legal, regulatory and privacy questions as well as technical ones. The experiment therefore demonstrates a possible analytical advantage under its test conditions, not that every payment system can or should use the same data or design.
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Fraud changes, including when authentication is used
Authentication can help establish that a payment is being approved by the customer, but it does not stop every form of fraud. In a joint report release dated 15 December 2025, the European Banking Authority (EBA) and European Central Bank (ECB) said strong customer authentication helped against some payment-fraud patterns while also reporting attacks that exploit exemptions or manipulate people into approving fraudulent payments.
For the report’s 2024 comparison, card payment fraud was 17 times higher when the recipient was outside the European Economic Area (EEA), where strong customer authentication was not legally required and was often not used in the described case. This is a specific comparison about recipient location and authentication conditions, not a general risk multiplier for every cross-border transaction.
The same release reported €4.2 billion in payment fraud across EEA payment reporting for 2024, compared with €3.5 billion in 2023 and €3.4 billion in 2022. These are regional reported fraud totals across payment activity, not worldwide losses, AI performance figures or proof that fraud increased because of any one technology.
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|---|---|
| Total payment fraud, 2024 | €4.2 billion; EEA payment reporting |
| Total payment fraud, 2023 | €3.5 billion; EEA payment reporting |
| Total payment fraud, 2022 | €3.4 billion; EEA payment reporting |
| Credit-transfer fraud losses, 2024 | €2.200 billion, up 16% year over year |
| Card-payment losses for cards issued in the EU/EEA, 2024 | €1.329 billion, up 29% |
All figures in the table are from the EBA and ECB’s 15 December 2025 release. The reported credit-transfer and card-payment figures describe distinct payment categories; they should not be added to or substituted for the overall total without accounting for the release’s definitions and scope.
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How models are tested and governed
Fraud patterns change, so a model that performs acceptably at one point may need reassessment as customer behaviour, payment practices or criminal tactics shift. BIS Project Hertha emphasizes labelled data, feedback and explainability. In testimony on 26 March 2026, Federal Reserve Board Director Randall D. Guynn said some supervised firms use machine learning for fraud detection and prevention, and described development practices, testing and human-in-the-loop systems as controls. His testimony is a supervisory perspective, not a census of all banks or a statement that every alert receives human review.
On 17 April 2026, the Office of the Comptroller of the Currency (OCC), Federal Reserve Board and Federal Deposit Insurance Corporation issued revised interagency model-risk guidance. It covers model development and use, testing, validation and monitoring, governance and controls, including attention to vendor or third-party products. The guidance is risk-based: an approach suitable for one institution may not suit another. It does not set enforceable standards or prescriptive requirements, and generative and agentic AI are outside its scope.
This governance context matters because a bank’s fraud controls are more than the model itself. Testing can assess whether it works as intended; monitoring can reveal when behaviour or outcomes change; and governance can clarify who is responsible for decisions and controls. These are risk-management practices, not proof that any particular system is error-free.
Information sharing is limited and distinct from model scoring
Financial crime may involve activity at more than one institution, creating a reason to share relevant information in some circumstances. The Federal Reserve’s 9 July 2026 letter, SR 26-3, discusses FinCEN’s clarification concerning suspected-fraud information sharing under Section 314(b) of the USA PATRIOT Act. Participation is voluntary, and the letter situates the safe harbor within its statutory context of identifying or reporting activity that may involve money laundering or terrorist activity.
That framework should not be confused with a bank’s ordinary customer-level transaction scoring or treated as authorization for indiscriminate pooling of customer data. Whether information can be shared depends on the applicable legal framework and purpose.
What to look for when comparing fraud-detection claims
A headline accuracy percentage is rarely enough to judge whether a fraud system protects customers well. Ask what the number measures, what evidence it comes from and what happens after an alert.
- Timing: Is the claim about authorization-time risk assessment, post-authorization monitoring, or investigation?
- Context: Does the system assess one payment, a customer’s activity over time, known patterns, or a wider payment-system view?
- Outcome: Does “detected” mean a model generated an alert, an investigator confirmed fraud, an account was identified, or a loss was prevented?
- Customer impact: Are false alerts, requests for verification, declines and later reversals measured separately?
- Evidence: Is the result based on operational outcomes or an experiment? If simulated data were used, does the claim say so?
- Scope: Which payment type, geography, time period, institution and authentication conditions are included?
- Governance: What testing, validation, monitoring, feedback, explainability and vendor oversight support the model?
These distinctions make it possible to separate a promising experiment from an operational performance claim and a fraud-volume statistic from a customer-protection measure.
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