Banks use AI as one part of a fraud decision system: it can help assess a payment’s risk, add context from a customer’s activity, and guide what happens next. A suspicious score is not proof of fraud, and it does not have to mean an automatic decline. Depending on the situation, the bank may allow the payment, monitor activity, seek confirmation, or take a more restrictive step.
The aim is to catch enough fraud while keeping false alerts, unnecessary declines, and customer friction manageable. There is no published, comparable industry-wide rate showing how often banks’ AI systems falsely decline legitimate transactions, and no single approach is used by every bank.
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What AI contributes to a bank’s fraud decision
Fraud detection is not simply a contest between “AI” and a person deciding whether a payment looks suspicious. It is a process for assessing risk and choosing a proportionate response. A model may help identify patterns that merit attention, but policies, authentication, customer contact, investigation, and operational controls also shape the outcome.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →One official example is the Federal Reserve Board’s commentary on debit-card transactions. It describes an issuer assessing risk before authorization, potentially with an automated mechanism such as a neural network. That is an example of a possible method, not evidence that all banks use neural networks or score every payment the same way.
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The practical distinction is between a risk signal and a final decision. A payment may look unusual without being fraudulent: a customer may be travelling, buying an unfamiliar item, or making a legitimate payment that differs from their usual pattern. Conversely, an individual payment may look ordinary when viewed alone but form part of a suspicious sequence. A model can help surface those cases; it cannot make uncertainty disappear.
How a payment moves through the detection loop
1. Assess the transaction at authorization
When a card transaction is presented for authorization, the issuer can assess its risk before deciding whether to approve it. The evaluation may include details of the transaction and how it relates to other activity. Federal Reserve commentary describes comparing transactions with a cardholder’s typical activity and with common fraud patterns.
Authorization-time scoring matters because it can inform a response while the payment decision is still being made. But the available evidence does not establish one universal set of signals, a universal scoring threshold, or a uniform response across banks and payment products.
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A single transaction can be ambiguous. The Federal Reserve’s Official Board Commentary on Regulation II says an issuer may be unable to determine whether a particular transaction is fraudulent in isolation and may therefore monitor sets of transactions made with a debit card. Looking at a sequence can reveal a pattern that is not apparent from one purchase, while knowledge of a customer’s usual activity can help distinguish some legitimate changes from suspicious ones.
Context also has limits. A customer’s habits can change, and unfamiliar activity is not automatically criminal. The point of using history and patterns is to improve the evidence available for a decision—not to treat “different from usual” as synonymous with “fraud.”
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3. Choose a response to the level and kind of risk
A risk alert can lead to different actions, depending on what the issuer knows and what risk it sees in allowing future activity. The Federal Reserve commentary lists closer monitoring, contacting the cardholder, reissuing a card, and closing an account as possible responses in different circumstances. A bank may also use customer alerts, such as texts, to help a customer report suspicious activity promptly.
These options are not interchangeable. An alert or request for confirmation can add evidence while preserving a legitimate payment where possible. Reissuing a card or closing an account is more disruptive and may be appropriate when the concern is serious or continuing. The cited examples do not establish that every flagged payment is automatically blocked, that a customer can always clear a hold immediately, or that every issuer offers the same options.
4. Use outcomes to improve detection
To remain useful, a detection system needs to be evaluated against what happens after an alert: whether activity was confirmed as fraudulent, whether an alert was a false alarm, and whether new patterns are being missed. The Bank for International Settlements (BIS) Project Hertha report emphasizes labelled training data, a robust feedback loop, and explainable algorithms. Federal Reserve supervisory testimony in March 2026 also describes testing regimes and human-in-the-loop systems among controls used at supervised institutions.
Feedback is not a guarantee of improvement. Confirmed outcomes can be incomplete or delayed, and fraud tactics change. A model that performs well on familiar patterns may not identify a new scheme. Banks therefore need ongoing testing and monitoring rather than relying on a model’s initial results.
When payment-network analytics add a wider view
A bank typically sees activity relevant to its own customers and operations. A payment system that can analyze activity across participating institutions may have a wider view of relationships among accounts and transactions. That broader perspective can help identify coordinated patterns that may be difficult to see from a single institution’s data alone.
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BIS Project Hertha explored this additional, system-level view for financial-crime patterns in real-time retail payment systems. Its results are from an experiment using a simulated synthetic transaction dataset—not a live-bank deployment or a study of real customers. No real customer data were used. The figures describe the project’s experimental detection task; they do not measure how many legitimate payments a bank would decline.
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| Project Hertha figure | What it means | Evidence scope |
|---|---|---|
| 12% more illicit accounts identified | Additional illicit accounts found with payment-system analytics compared with what banks and payment service providers would otherwise have found in the experiment. | BIS Innovation Hub, 2025; experiment using synthetic data, not a live operational result. |
| 26% improvement in identifying previously unseen behaviours | Improvement for the project’s task of detecting behaviours not seen before. | Same synthetic-data experiment; not a real-world service-level or false-decline figure. |
| 1.8 million accounts and 308 million transactions | Size of the simulated dataset used in the experiment. | BIS Innovation Hub, 2025; no real customer data were used. |
Those results show why network-level analysis is worth studying, not what a customer should expect from their own bank. Moving from an experiment to operational use raises practical, legal, regulatory, and privacy questions. Information sharing is not a license for banks to pool all customer data indiscriminately. The Federal Reserve’s July 2026 letter describes voluntary sharing of suspected-fraud information under the statutory context of USA PATRIOT Act Section 314(b), including a safe harbor relevant to identifying or reporting activity that may involve money laundering or terrorist activity.
Why fewer false alerts is a design goal, not a guaranteed outcome
Fraud detection involves trade-offs. A system tuned to flag more suspicious activity may also produce more alerts about legitimate transactions. A system that is more conservative about raising alerts may reduce friction but miss some fraud. In model design, this is often described as balancing recall (finding a larger share of relevant cases) and precision (making a larger share of alerts genuinely relevant). Project Hertha discusses that balance, but its report does not establish a live-bank target or performance guarantee.
For consumers, the key question is not whether a bank uses AI in the abstract. It is how the bank combines signals, what it does when evidence is uncertain, and how it handles a legitimate customer who is challenged or inconvenienced. In some situations, an issuer may monitor, contact the customer, or use another response rather than simply declining a payment. No detection process can promise both that all fraud will be stopped and that every legitimate transaction will go through without interruption.
The official sources cited here do not give an industry-wide percentage of legitimate transactions falsely declined by banks’ AI systems. That number should not be inferred from a fraud-loss total, a model’s experimental detection uplift, or a vendor’s performance claim. Those measures answer different questions.
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Fraud patterns and authentication keep changing
Authentication can add evidence that a payment is being made by the customer, but it does not eliminate fraud. In a joint report published in December 2025, the European Banking Authority (EBA) and European Central Bank (ECB) said that strong customer authentication helped with some payment-fraud patterns while also describing attacks that exploit exemptions or manipulate users into approving fraudulent payments.
The same report found that, in its 2024 comparison, card payment fraud was 17 times higher when the recipient was outside the European Economic Area (EEA). Strong customer authentication was not legally required in that outside-EEA recipient case and was often not used. This is a specific comparison about EEA payment reporting and authentication context; it should not be treated as a universal multiplier for every card or geography.
These findings illustrate why a bank cannot rely on a single signal. Authentication, transaction context, and fraud-pattern monitoring address different aspects of risk. Attackers may target gaps in authentication coverage, exploit exceptions, or persuade customers to approve payments. Detection and customer protection therefore need to adapt as tactics change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the latest EEA fraud totals do—and do not—show
The EBA and ECB reported €4.2 billion in payment fraud for the EEA in 2024, compared with €3.5 billion in 2023 and €3.4 billion in 2022. These are reported fraud amounts for the EEA, not global figures and not a measure of AI accuracy or false declines.
| Measure | Reported amount or change | Scope |
|---|---|---|
| Payment fraud in 2024 | €4.2 billion | EEA payments; EBA and ECB report published 15 December 2025. |
| Payment fraud in 2023 | €3.5 billion | EEA payments; as reported by the EBA and ECB in 2025. |
| Payment fraud in 2022 | €3.4 billion | EEA payments; as reported by the EBA and ECB in 2025. |
| Credit-transfer fraud losses in 2024 | €2.200 billion; up 16% year over year | EBA and ECB payment-fraud reporting. |
| Card-payment losses in 2024 | €1.329 billion; up 29% | Cards issued in the EU/EEA; EBA and ECB payment-fraud reporting. |
The figures are useful for understanding reported fraud by payment instrument and region. They do not reveal whether AI caused losses to rise or fall, whether a particular bank blocks too many valid payments, or how any one customer’s transaction will be handled.
How banks should govern fraud models
Fraud models can affect customers, operations, and a bank’s risk exposure. The appropriate controls depend on the model and how important it is to the institution; oversight is not one-size-fits-all. The OCC, Federal Reserve Board, and Federal Deposit Insurance Corporation issued revised interagency model-risk guidance on 17 April 2026. It addresses development and use, testing, validation and monitoring, governance and controls, and vendor or third-party products.
The guidance is risk-based: practices appropriate for one bank may not suit another. It does not set enforceable standards or prescriptive requirements, and it excludes generative and agentic AI from its scope. Its general expectation is that it will be most useful for models supporting significant business lines, operations, services, and functions. That distinction matters: the guidance informs risk management but should not be described as a rule requiring every bank to use one specified fraud model or control.
In March 2026 testimony, Federal Reserve Director Randall D. Guynn said that machine-learning methods had been in use for years and that banks often deploy mature, time-tested capabilities. He also described controls including development practices, testing, and human-in-the-loop systems, with governance and oversight growing alongside AI use. This is a supervisory perspective, not a census proving that every bank uses the same controls.
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Controls that matter in practice
- Testing and validation: assess whether a model behaves appropriately for its intended use, including whether it detects relevant patterns without producing an unacceptable volume of unhelpful alerts.
- Monitoring: check whether performance and patterns change over time, rather than assuming initial validation remains sufficient as fraud tactics evolve.
- Explainability and feedback: keep the basis for model decisions understandable enough to support review, and use credible outcome information to inform improvements.
- Human involvement: establish where people review alerts or decisions, especially where context or the consequences of action warrant additional judgment.
- Vendor oversight: apply appropriate attention to third-party models and products; outsourcing a tool does not remove the need for institutional governance.
What this means if a legitimate payment is challenged
A bank’s fraud controls may delay, challenge, or decline a payment when it sees risk, but an alert alone does not establish that the customer did anything wrong. If a transaction is questioned, follow the bank’s official contact and verification process rather than responding to an unsolicited message that claims to resolve the issue. A customer alert may help bring suspicious activity to the bank’s attention; the Federal Reserve’s examples do not establish that every bank uses text alerts or that every alert can be cleared instantly.
For banks, the practical challenge is to make responses proportionate to the evidence and potential harm. A low-confidence anomaly may call for more monitoring or a request for confirmation; evidence of ongoing account compromise may justify more disruptive action. The right decision depends on the case, the payment type, the institution’s policies, and applicable rules. There is no single AI score that can substitute for those considerations.
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