In 2024, US customers alone lost $12.5 billion to fraud, a 25% increase compared to the year prior. In many cases, the fraud looks like a normal transaction until it doesn’t: a card testing bot can submit dozens of small charges across different cards in minutes, a fraudulent actor can use stolen credentials to place a high-value order with expedited shipping, or a customer might dispute a charge they actually authorized.

The best fraud detection software helps catch these patterns before they become chargebacks, using rules-based logic, machine learning (ML), and real-time transaction signals to separate legitimate purchases from fraudulent ones. Below, we’ll explain how fraud detection software works, which features matter when you evaluate a fraud prevention solution, and how to match a system to your business’s fraud exposure.

Key takeaways

• Modern fraud detection uses rules-based logic, ML, or both to catch known fraud patterns as well as emerging tactics that static rules would miss.

• False positive rates matter as much as detection rates, since blocking legitimate customers creates its own revenue loss.

• Businesses that are already using payment providers with native fraud detection can often skip third-party integration. Built-in fraud detection tools draw on network-level signals that stand-alone platforms often struggle to replicate.

What is fraud detection software?

Payment fraud detection software evaluates transactions to identify and block fraudulent activity before it results in chargebacks, stolen goods, or compromised accounts. Generally, it sits between a transaction attempt and its approval and applies a combination of rules, models, and signals to decide whether a payment should go through.

How does fraud detection software work?

Fraud detection systems differ in how they decide what to block. There are rules-based systems and ML-based systems.

Rules-based detection

Rules-based systems apply conditional logic to incoming transactions: if a transaction exceeds a certain dollar amount, originates from a high-risk region, or triggers a velocity check (e.g., more than five purchases from the same card in 10 minutes), it gets flagged or blocked.

The advantage is transparency: you can see exactly why a transaction was flagged. The limitation is that rules catch only what you anticipated when you wrote them. A fraud pattern you haven’t seen before won’t trip any rule until you update your logic.

ML models

ML-powered detection learns from historical transaction data to identify what “normal” looks like and flag deviations. A single transaction that’s from a new device or in a new country or made at an unusual hour for an atypical amount might look fine with only one of those characteristics. But when they’re combined, the transaction might score as high-risk. The model weights those signals based on what’s actually predictive in your transaction history.

The meaningful advantage over rules is adaptability: fraud tactics shift constantly and ML models can detect emerging patterns before you’ve written a rule for them. The trade-off is opacity since it’s harder to explain exactly why a specific transaction was declined, which complicates false positive review.

In either case, these systems should operate in real time at the moment the transaction is submitted. They should work fast enough to accept or decline it before the checkout session times out and without introducing noticeable checkout delays. Some systems also track behavior throughout the checkout flow and incorporate session-level signals into the risk score before the payment is even submitted.

What types of payment fraud can fraud detection software catch?

There are a few different types of payment fraud commonly faced by businesses: card-not-present (CNP) fraud, card testing, account takeover (ATO), and friendly fraud. Each exploits a different vulnerability in the transaction flow and can be detected by fraud prevention software.

• CNP fraud: A fraudulent actor uses stolen card data to make purchases where no physical card is required. Detection relies on signals the cardholder wouldn’t typically trigger, such as mismatched billing and shipping addresses, unusual device fingerprints, and abnormal behavior patterns.

• Card testing: Fraudulent actors who obtain stolen card numbers verify which ones are active, generally by submitting small or zero-dollar transactions across many cards in quick succession. Velocity checks and device fingerprinting are the primary countermeasures.

• ATO fraud: In ATO fraud, bad actors gain access to a legitimate customer account (often through credential stuffing, using leaked username and password pairs) and use stored payment methods to place orders. Detection makes use of behavioral signals (e.g., login from an unfamiliar device, sudden password changes followed by purchases, address changes right before a high-value order).

• Friendly fraud: A customer completes a legitimate transaction, receives the goods, and then disputes the charge with their bank. Fraud software can help by flagging accounts with prior dispute history or purchases that fit known patterns, such as high-value goods with expedited shipping that can be resold easily.

Which features matter in fraud detection software?

The features that matter most depend heavily on your transaction volume, fraud exposure, and technical setup. But a few capabilities consistently matter.

Look into the following:

• Detection accuracy: A system that declines 85% of fraud and 15% of legitimate orders is trading one revenue loss for another. Look for platforms that publish false positive rates alongside detection rates since the two numbers together tell you far more than detection rate alone.

• Rule customization: Prebuilt models are a starting point. You’ll almost always need to layer in business-specific logic, such as blocking certain geographies, requiring 3D Secure for orders above a certain threshold, and adjusting risk scores for known, safe customers. Platforms that let you write and test custom rules without engineering support are considerably more flexible in practice.

• Integration with payments infrastructure: A fraud tool that requires you to pass transaction data through a separate application programming interface (API) introduces latency and failure points. Systems embedded in the payment flow or built natively into your payment provider avoid that complexity.

• Reporting and case management: When your team reviews flagged transactions, they need context—why the transaction was flagged, what signals contributed, and what similar transactions looked like. Fraud management software that shows this clearly makes manual review faster and helps you refine your rules over time.

• Scalability: A system that handles your current volume needs to handle 10 times that volume without degrading in speed or accuracy. Busy traffic times such as Black Friday often see fraud peaks; these time periods are also when you least want slowdowns.

What challenges come with fraud detection software?

Fraud detection software has two jobs: accurately flagging fraud attempts and not blocking legitimate transactions. One of the most persistent challenges in fraud detection is finding a balance. The software should rigorously attend to fraud signals without creating frustration for legitimate customers.

Beyond that ongoing work, rules-based systems and ML-based systems face distinct challenges:

• Rules become outdated: Fraud tactics change constantly. A rule set that was effective six months ago might have notable gaps today. Without regular review, rules-based systems drift towards obsolescence.

• Data quality for ML models varies: ML models are only as good as the data they’re trained on. A platform with transaction signals from millions of businesses across many industries will have a meaningfully different model from one with narrower coverage. When you evaluate vendors, ask specifically about their training data scope.

How do you choose fraud detection software for your business?

The right choice for fraud detection software depends on how your business works. You need a system that’s easy to integrate, easy to run, and matches the kind of fraud you encounter.

Consider these variables:

• Transaction volume: At lower volumes, ML model accuracy can be inconsistent if there’s less historical data to train on. And rules-based or hybrid approaches might outperform pure ML at smaller scale. High-volume businesses likely benefit more from ML because the models have enough signals to be genuinely predictive, but small businesses can still benefit from a network-level ML model.

• Technical setup: If you use a payment provider with a built-in fraud detection service, that’s often the right starting point. Integration complexity is minimal and fraud signals are already embedded in the payment flow. If you operate across multiple processors or have complex checkout infrastructure, you might need a stand-alone fraud platform with broad integration support.

• Internal resources: Some fraud platforms require meaningful engineering work to configure and maintain them; others are designed for nontechnical ops teams to manage directly. If you don’t have a dedicated fraud analytics or risk team, lean towards platforms with strong default analysis models and simple rule interfaces.

• Reporting and workflow integration: Verify that any platform you’re evaluating connects to your dispute management workflow and exports data in formats compatible with your reporting stack. Fraud tools that don’t integrate cleanly create manual work that can offset the automation benefit.

How Stripe Radar can help

Stripe Radar uses AI models to detect and prevent fraud, trained on data from Stripe’s global network. It continuously updates these models based on the latest fraud trends, protecting your business as fraud evolves.

Stripe also offers Radar for Fraud Teams, which allows users to add custom rules addressing fraud scenarios specific to their businesses and access advanced fraud insight.

Radar can help your business:

• Prevent fraud losses: Stripe processes over $1 trillion in payments annually. This scale uniquely enables Radar to accurately detect and prevent fraud, saving you money.

• Increase revenue: Radar’s AI models are trained on actual dispute data, customer information, browsing data, and more. This enables Radar to identify risky transactions and reduce false positives, boosting your revenue.

• Save time: Radar is built into Stripe and requires zero lines of code to set up. You can also monitor your fraud performance, write rules, and more in a single platform, increasing efficiency.

Learn more about Stripe Radar, or get started today.