Machine learning detects credit card fraud by turning transaction and account information into a risk score, then combining that score with rules and a decision policy. Depending on the score and the institution’s operating thresholds, a payment may be approved, challenged, sent for review, or declined. There is no single algorithm or feature set used by every bank.
How a transaction becomes a fraud decision
Detection is a pipeline rather than a one-step prediction. A payment system represents a transaction using features that may include its amount, merchant and category, time, geography, device or channel, account history, transaction velocity, and connections to recent transactions. The exact data available and the rules for acting on it vary by institution.
- Represent the transaction. Convert available payment and account context into features the model can evaluate.
- Estimate risk. A model produces a score or classification based on patterns it learned from labeled transactions, deviations from modeled normal behavior, or both.
- Apply decision controls. Combine the score with rules and thresholds to decide whether to approve, challenge, review, or decline the payment.
- Investigate and learn. Later outcomes, such as investigations or chargebacks, can inform future training and evaluation, although those labels may arrive late or be imperfect.
The score is not itself proof of fraud. It is an input to a decision that must balance payment security, customer friction, investigation capacity, and the cost of mistakes.
Which algorithms are used, and which is best?
There is no universally best fraud-detection algorithm. Candidate models should be compared on the same appropriately time-separated data and judged against the costs and operating limits of the payment system—not just by a single headline accuracy figure.
#1 Best Overall
- With Square Terminal, you can ring up sales, accept payments, and print receipts, all with one device. Use it at the counter or ring up customers anywhere in your store.
- Accept all major credit and debit cards and pay one low rate with no hidden fees and no long-term contracts.
- Process chip cards in just two seconds.
- Get your money as soon as the next business day.
- Use it cordlessly with the built-in battery, designed to last all day.
| Model family | What it contributes | What to assess |
|---|---|---|
| Logistic regression | A traditional supervised-classification baseline for estimating fraud risk from labeled examples. | Recall at an acceptable false-positive rate, calibration, inference latency, and whether the resulting decisions are useful to investigators. |
| Decision trees and random forests | Tree-based supervised approaches included among traditional fraud-detection baselines. | Precision-recall performance, calibration, latency, interpretability needs, and investigation cost. |
| Support-vector machines and nearest neighbors | Other traditional model families used as candidate supervised approaches. | Performance under class imbalance, operational latency, retraining burden, and how well performance holds as behavior changes. |
| CNN, RNN, LSTM, and GRU architectures | Deep-learning families covered in research on credit-card fraud detection. | Recall and precision-recall performance, calibration, latency, interpretability, retraining burden, and resilience to drift. |
| Anomaly or unsupervised methods | Model normal behavior or transaction structure and flag deviations, rather than relying only on confirmed fraud labels. | False alarms, detection quality, calibration where applicable, and robustness as normal and fraudulent behavior evolve. |
These are candidates, not a ranking. A conference experiment reported 94.98% random-forest accuracy on its selected dataset; that result describes that experiment, not a general benchmark or a guarantee for a live payment system. Because fraud is rare relative to legitimate activity, accuracy alone can obscure poor fraud detection or an impractical volume of false alarms.
Why fraud data and labels are difficult
Fraud is a small minority of transactions, so a model can appear accurate simply by predicting the majority class while missing many fraudulent payments. Confirmed labels may also arrive only after a chargeback or investigation, and some labels can be noisy. At the same time, fraud tactics and ordinary customer behavior change, while privacy restrictions limit access to real transaction records.
Rank #2
- Get your money as soon as the next business day.
- Get set up quickly with no long-term commitments. Download the Square Point of Sale app for free, create an account, and start taking payments anywhere.
- Run your business all in one place with the free Square Point of Sale app. Track your sales, manage inventory, accept tips, send receipts digitally, and more.
- Works with Apple devices with a Lightning connector.
Researchers and practitioners address these constraints with approaches such as class weighting, intelligent sampling, self-supervised representation learning, and dynamic thresholds. These techniques do not remove the need for careful validation: a model must be tested on data that reflects when it would actually have made decisions. An ACM study published March 28, 2024, discusses inadequate transaction representation, noisy labels, and data imbalance as challenges in this field.
Access to payment data is also a public-research constraint. The Federal Reserve has reported that sensitive and economically valuable payment data are scarce for public research. Its CardSim work, published in 2025, offers a flexible, scalable simulator calibrated to public payment-survey data for reproducible testing of fraud workflows, machine-learning methods, and interpretability frameworks. Simulated results can support controlled experiments, but should not be treated as proof of performance on every institution’s live transactions.
Free tools Windows power users keep installed
One-click scans. No signup required.
Rank #3
- Accept all major credit and debit cards and pay one low rate
- No hidden fees and no long-term contracts
- Mobile card reader that accepts payments anywhere & anytime
- Use the free SumUp App on your smartphone or tablet to start accepting transactions
- Simply pay 2.6% +10 per in-person transaction
How to evaluate a fraud model
Evaluation should reflect the real consequences of the decisions. A missed fraudulent payment has a cost, but so does declining a legitimate purchase, challenging a cardholder, or sending too many cases to a limited review team. The appropriate operating threshold depends on those costs and the capacity to investigate.
- Recall: How much fraud the system catches. High recall matters, but pursuing it without regard to false positives can create unacceptable customer friction or review volume.
- Precision and false-positive rate: How many flagged transactions are actually fraudulent and how often legitimate transactions are flagged. These measures help quantify unnecessary declines, challenges, and investigations.
- Precision-recall performance: A useful way to compare models when fraudulent cases are much rarer than legitimate ones.
- Calibration: Whether risk scores correspond meaningfully to observed outcomes, which matters when thresholds determine different actions.
- Latency and workload: Whether the model can return a decision in time for payment processing and whether the resulting queue is manageable for investigators.
- Cost-weighted outcomes: Whether the combined results reduce losses and operational costs after accounting for missed fraud, legitimate declines, customer challenges, and manual review.
Where possible, use time-based validation splits so that future behavior does not leak into training or testing. Report the measures above together, including analyst workload and detection latency, rather than relying on raw accuracy. The IEEE review of deep-learning approaches, published July 11, 2024, covers metric selection and the challenges of class imbalance.
Rank #4
- Pay one transparent rate per swipe for Visa, Mastercard, Discover and American Express.
- Works in conjunction with most downloadable Square point-of-sale apps on your device. Customers can pay, tip and sign directly on your device. Track payments in cash, gift cards and more. Also lets you send receipts via e-mail or text message, makes it easy to apply discounts, keeps a data and sales history log and more.
- Accepts magstripe credit card payments, including those from Visa, Mastercard, Discover and American Express (fees apply).
- App sends deposits to your bank account within 1 to 2 business days, or enjoy instant deposits (fees apply).
Why false alarms happen
A legitimate purchase can look unusual compared with an account’s history or the patterns a model has learned. A new location, different merchant or channel, unusual timing, or sudden change in transaction velocity may raise a risk score even when the cardholder authorized the payment. These are examples of possible signals, not a universal list of rules applied by banks.
Thresholds make the trade-off explicit. A lower threshold may catch more fraud but also flag more legitimate payments; a higher one may reduce friction while allowing more fraud through. Institutions can also route different scores to different actions—such as an authentication challenge or a review queue—instead of treating every flagged transaction as an automatic decline.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Best Value
- An intuitive interface to easily accept payments and manage your sales.
- Strong, reliable Wi-Fi connection. Free SIM card and mobile data so you can process payments anywhere.
- Great battery capability with an additional charging station.
- A truly portable device. Stay in control of your business, wherever you go.
- Support when you need it. Get in touch with our US-based support through phone, email and chat.
Why models need monitoring and layered defenses
Fraud patterns and cardholder behavior change, so performance can degrade after deployment. Monitoring should look for shifts in transaction populations and features, changes in performance once delayed labels become available, and deterioration in the threshold’s practical results. Retraining and threshold review may be needed as those patterns change.
Models also face adversarial behavior. An INFORMS study published in 2023 found that adversarial examples could substantially reduce the ability of the supervised credit-card-fraud models it tested to identify fraud, while the unsupervised models tested were less affected. That finding is specific to the study; it does not establish that unsupervised systems are immune to attacks or that any approach remains accurate indefinitely.
For that reason, machine learning is one layer of a broader control system. Rules, authentication, and human investigation can complement model scores and provide other ways to respond when a transaction is suspicious or a model’s judgment is uncertain. The Federal Reserve’s 2025 CardSim discussion paper describes financial institutions and authorities as using AI extensively for fraud detection, prevention, and response; the particular methods and policies remain institution-specific.
How widespread is card-related fraud?
In figures reported by the Board of Governors of the Federal Reserve System in 2025, 11.5 percent of credit-card owners and 9.4 percent of debit-card owners experienced card-related theft or fraud in 2023. The same report says FTC credit-card-fraud reports were 113 percent higher in 2023 than in 2019. These figures describe reported experiences and reports over the stated periods; they are not a measure of the accuracy of any fraud-detection model.
Recommended Free Tools
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
