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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsAI can improve payment-fraud detection, but it cannot reliably identify every fraudulent payment on its own. There is no universal accuracy percentage that applies to all banks, channels, fraud types or models. Reliability depends on the data, labels, fraud definition, decision threshold, monitoring, and human controls around the model. A useful assessment therefore looks at recall, precision, false-positive and false-negative costs, and live operational performance rather than one headline score.
What “reliable” means in AI fraud detection
Payment systems process enormous numbers of legitimate transactions, while fraud is comparatively rare and constantly changing. A model may be very good at ranking suspicious activity yet still miss a new scam or inconvenience many legitimate customers. “Accuracy” by itself hides those trade-offs.
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| Measure | What it tells you | Question to ask |
|---|---|---|
| Detection (recall) | The share of known fraud in the evaluation set that the system found. | How much relevant fraud was caught at the operating threshold? |
| Precision | The share of transactions flagged by the system that were actually fraudulent. | How many alerts represent genuine cases rather than noise? |
| False-positive burden | Legitimate payments interrupted, declined or escalated for review. | How many customers and payments are affected by warnings or blocks? |
| False-negative loss | Fraud that passes through without detection. | What value and customer harm result from missed cases? |
| Operational performance | Whether decisions arrive quickly enough and remain useful after deployment. | Does the system work in the live payment flow as fraud and customer behavior change? |
Increasing the threshold for intervention can catch more suspicious activity, but it usually sends more legitimate payments to review. Lowering it can reduce customer friction while allowing more fraud through. Any published result should identify the fraud type, data period, geography, transaction population, metric and threshold.
What current evidence actually shows
Project Hertha: promising experimental gains
The Bank for International Settlements (BIS) Innovation Hub reported on June 5, 2025, that Project Hertha’s payment-system analytics helped banks and payment service providers identify 12% more illicit accounts than they otherwise would have found. The project also reported a 26% improvement when attempting to identify previously unseen behavior.
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Those figures are not a general AI accuracy rate. The experiments used a simulated synthetic dataset containing 1.8 million bank accounts and 308 million transactions; no real customer data was used. BIS described the approach as a supplementary tool and said practical, legal and regulatory questions were outside the project’s scope. The results indicate that network-level analytics may add useful signals, especially for novel patterns, but they do not show that a deployed system correctly classifies a fixed percentage of all payments.
Fraud remains a substantial problem despite controls
A joint report from the European Banking Authority (EBA) and European Central Bank (ECB), announced December 15, 2025, estimated payment fraud across the European Economic Area (EEA) at €4.2 billion in 2024, compared with €3.5 billion in 2023 and €3.4 billion in 2022. The report put fraud at around 0.002% of total transaction value in 2024.
That 0.002% figure is an aggregate value-based fraud rate, not AI precision, recall, or the percentage of transactions that were fraudulent by count. The report’s headline also notes that strong customer authentication remains effective while fraudsters adapt. In particular, payer-manipulation fraud is rising: the customer may authenticate and approve a transfer after being deceived, so authentication alone cannot prevent every scam.
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Bank adoption is growing, but the sample is limited
ECB Banking Supervision reported increasing AI use cases among significant European banks between 2023 and 2024. Its workshops involved 13 selected banks, so the ECB cautions that the observations should not be generalized to the entire banking sector. Banks reported decision-tree models for uses including credit scoring and fraud detection, while neural networks were used mainly for fraud detection. This describes reported practice, not proof that one model family is universally better.
How AI helps find suspicious payments
Machine-learning systems can examine relationships among transaction attributes that are difficult to evaluate manually at scale. Depending on the institution and legal basis, signals may include payment amount, timing, account history, device or session characteristics, merchant or beneficiary relationships, geographic inconsistencies and links among accounts.
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A single transaction may look ordinary in isolation. Network-wide analysis can reveal that several accounts share unusual counterparties, devices or movement patterns. Project Hertha’s results suggest this broader payment-system view can help with previously unseen behavior, where a rule based only on known fraud signatures may have no matching pattern.
In a typical control system, a model produces a risk score or alert rather than making an irrevocable decision by itself. The payment may receive an extra authentication step, enter a human-review queue, be delayed briefly, or be declined or frozen under defined rules. The institution then records the outcome so investigators can improve labels and future monitoring.
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Incomplete, irrelevant or poor-quality data
The U.S. Government Accountability Office (GAO) warns that insufficient, unrelated or poor-quality input data reduce model reliability. Missing device information, inconsistent transaction records or a data source that does not represent the target customers can make a model appear effective in testing but weak in production.
Labels are equally important. If a confirmed fraud is recorded as legitimate, or a legitimate payment is incorrectly labeled as fraud, the model learns the wrong relationships. Delayed chargeback information can also make recent transactions look undecidable during training.
Rare and changing behavior
Fraud occupies a small part of a large transaction stream. A model can achieve a high overall accuracy by predicting “legitimate” most of the time while missing the cases that matter. Criminals also change tactics in response to controls, creating patterns absent from historical data. The EBA/ECB finding that payer-manipulation fraud is increasing illustrates why a model trained on older unauthorized-transaction patterns may not recognize a customer who has been socially engineered into approving a transfer.
False positives and false negatives
A false negative lets fraud proceed. A false positive can delay or deny a legitimate payment. GAO testimony published April 9, 2025, specifically warned that false positives can delay or deny payments to rightful recipients, including small businesses and beneficiaries of Social Security and Medicare.
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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →The right balance depends on the payment and the harm involved. A high-value international transfer may justify additional review, while a routine low-value purchase may not. Institutions should report alert volume, review capacity, customer abandonment, blocked value and confirmed losses alongside detection metrics.
Adversarial or contaminated data
GAO identifies data poisoning as a risk: someone alters training or operational data so that system behavior changes. Fraudsters may also probe a system indirectly, learning which actions trigger friction and adapting their behavior. Controls therefore need data-access restrictions, integrity checks, monitoring and a process for investigating abrupt performance changes.
Limited explainability and weak governance
Financial institutions told GAO they are more cautious about AI for decisions requiring high reliability or explainability. If staff cannot understand why a payment was flagged, they may struggle to resolve a customer complaint, detect a broken data feed or challenge a mistaken result.
ECB Banking Supervision’s workshop findings describe governance policies, model review, human oversight and data-quality practices among the banks consulted. Reliability is not only a model property: it also depends on version control, documented approval, access permissions, audit trails, incident response and regular validation.
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Deployment constraints
A model that is accurate in a retrospective analysis may be too slow for an authorization decision, may generate more alerts than investigators can handle, or may rely on data unavailable at the moment a payment is initiated. Live testing should measure latency, alert queues, override rates, customer outcomes and performance by channel, not just a laboratory score.
Authorized-payment scams need different controls
In an unauthorized transaction, the account holder did not approve the payment. In an authorized-payment scam, the customer personally approves a transfer after being manipulated by an impersonator, fake investment opportunity, romance scam or other deception. The transaction can therefore look consistent with the customer’s normal authentication and device history.
GAO notes that generative AI can make scams harder for victims to recognize. Financial institutions and payment apps may use customer education, staff training, warnings, confirmation questions and short payment delays so the customer can verify a beneficiary. These measures complement transaction scoring; no risk model can infer every human decision made under pressure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to evaluate an AI fraud-detection claim
Use the following process when comparing vendors, bank disclosures or internal systems:
- Define the fraud population. Separate card-not-present fraud, account takeover, transfer fraud and authorized-payment scams. Do not combine unlike channels into one score.
- Request the operating threshold. Ask for precision, recall, false-positive rate, review volume and prevented loss at the threshold actually used in production, not only the best result found during testing.
- Check the evidence setting. Distinguish live production outcomes from retrospective analysis or synthetic-data experiments. Record the date range, geography, transaction volume, customer segment and independent validation method.
- Examine the response path. Find out whether an alert triggers step-up authentication, human review, a temporary delay, an automatic decline or an account freeze. Ask how customers appeal and how legitimate payments are released.
- Test for drift. Ask how often labels and performance are reviewed, what triggers retraining, and how model versions and threshold changes are audited.
- Review human oversight. Investigators should be able to inspect relevant evidence, override a decision when justified and record the reason. High-impact actions need escalation and recovery procedures.
- Assess data scope and privacy. Identify every data source, what information is shared across institutions, the legal basis for sharing, retention periods and security controls.
This framework is a practical way to interpret limitations identified by GAO, BIS and ECB sources; it is not a published industry rating standard.
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Why your legitimate payment may be flagged
A legitimate payment can resemble a known fraud pattern: an unusual location, a new device, a first-time beneficiary, an atypical amount, rapid changes in behavior or a link to an account that has triggered other alerts. Because the system is managing uncertainty, a warning does not prove that you committed fraud.
Use the institution’s official contact channel, verify the payment and beneficiary independently, and provide requested information promptly. Do not share one-time codes or approve a transfer merely because someone claiming to be from a bank tells you to. If a payment is delayed, ask what review or confirmation is required and whether a documented appeal path exists.
What a trustworthy disclosure should include
A credible performance statement identifies the fraud definition, channel, evaluation period, geography, data source, sample size, metric and threshold. It explains how confirmed outcomes were obtained, how often the model is recalibrated, and what happens when the model is uncertain. It also distinguishes improvements in finding suspicious accounts from transaction-level classification accuracy.
Statements such as “AI stops fraud” or “the model is 99% accurate” are incomplete without those details. The BIS Project Hertha figures are useful evidence of potential improvement under a defined experiment, while the EBA/ECB totals describe the scale of fraud in the EEA; neither is a universal score for deployed AI.
Bottom line
AI is valuable for screening large payment flows, connecting activity across accounts and surfacing new patterns for investigators. It is not a guarantee. Reliable protection requires representative data, accurate labels, threshold-specific measurement, drift monitoring, explainable review, privacy controls and human intervention. Authorized-payment scams add a separate challenge because a manipulated customer may approve the transaction. Treat AI as one component of a layered fraud-control program, and judge it by transparent outcomes and customer recovery—not by a single accuracy number.
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