In 2025, American consumers alone reported US$15.9 billion in fraud losses, and attacks are continuously getting more sophisticated. Stolen card data sells in bulk on dark web markets, bot networks run thousands of card tests per hour, and organised rings file coordinated chargebacks after receiving goods. The best e-commerce fraud prevention software blocks fraudulent transactions without rejecting the legitimate customers who look slightly unusual.

Below, we'll explore the main fraud types affecting online retailers, how the three categories of prevention tools differ, and how to match a solution to your business's specific risk profile.

Key takeaways

• E-commerce fraud spans several distinct attack types, each requiring different detection mechanisms.

• Fraud prevention tools work through two mechanisms: blocking fraudulent transactions before they complete and generating the evidence to win disputes when they are filed.

• The right e-commerce fraud prevention solution depends on your transaction volume, where your fraud is concentrated, and what your technical infrastructure can support.

What is e-commerce fraud prevention software?

E-commerce fraud prevention software is a system of automated tools that identify fraudulent transactions and either block them before they complete or flag them fast enough to intervene before goods ship or chargebacks occur. It's what online businesses use to mitigate payment fraud.

What are the common types of e-commerce fraud?

Online retailers face a concentrated set of fraud patterns, and a handful of attack types drive losses.

Here are the most common kinds of e-commerce fraud:

• Card-not-present (CNP) fraud: A criminal obtains card data through phishing, data breaches, or dark web purchases and uses it to place orders before the cardholder notices. Because there's no physical card involved, standard chip and PIN protections don't apply. Detection depends on behavioural and contextual signals.

• Card testing: Fraudulent actors use a business's checkout as a verification tool. They run small transactions – sometimes under a dollar – to confirm whether stolen card numbers are still active before selling them or using them for larger purchases elsewhere. Without rate limiting and velocity rules in place to combat card testing, a business's authorisation costs climb and fraud signal data is polluted.

• Friendly fraud: The real cardholder places the order, receives the goods, then disputes the charge with their bank, claiming they didn't authorise it or that the item never arrived. Winning a dispute requires documentation such as delivery confirmation, internet protocol (IP) logs, and device fingerprints.

• Refund and promotion abuse: Customers return used or counterfeit items or stack promotion codes in ways a business's system wasn't designed to prevent. Refund fraud and promo abuse don't always look like fraud in transaction data, which makes it harder to catch without tools built specifically for account-level behavioural analysis.

What types of e-commerce fraud prevention solutions are available?

The fraud prevention software market has three distinct categories. Each has different trade-offs on coverage, cost, and integration complexity. Consider the following.

PSP-integrated fraud tools

Fraud tools built into your payment-service provider (PSP)'s offerings operate at the point of transaction, with direct access to payment data your provider has already collected. There's no application programming interface (API) call to a third party adding latency to your checkout, and the fraud signals (e.g., card history, dispute rates, transaction velocity across the provider's network) feed directly into the decision engine. Integration is minimal if you're already processing through a provider that includes fraud tooling. The constraint is that coverage is typically intended specifically for payment fraud; account-level abuse, promotion fraud, and post-transaction disputes often need additional tooling.

Stand-alone fraud prevention platforms

Third-party platforms sit between your checkout and your payment processor. They consume transaction data, device signals, and behavioural data from your site, then return a risk score or decision before a payment is authorised. These platforms offer broader coverage: they're built to handle fraud across the full customer lifecycle, from account creation through refunds, rather than only at the payment layer. They also tend to offer more configurable rules engines, which matters if your fraud patterns are unusual or your product catalogue creates atypical risk signals. The trade-off is integration work up front and an additional vendor relationship to manage.

Chargeback guarantee providers

Some vendors offer a different commercial model. Rather than charging a flat fee for fraud detection, they approve or decline orders and assume full chargeback liability for the orders they approve. If a fraud-related dispute comes through on a transaction they cleared, they cover it. This shifts financial risk but changes your decision-making control. You're accepting their approval decisions on orders they guarantee, which can create tension if their model declines customers you would have accepted.

What features should you look for in e-commerce fraud prevention software?

The specific features you look for in fraud prevention software depend on your transaction volume and fraud exposure.

In general, it's worth considering the following:

• Real-time decisions: Fraud tools that return decisions after your checkout completes are too slow. The evaluation needs to happen before authorisation, while the session is still active. Check whether the tool's average decision latency fits inside your checkout flow without adding perceptible delay.

• Machine learning (ML) models trained on network data: An ML model trained only on your transactions starts with limited signal, especially if you're an earlier-stage business. Tools that draw on network-wide transaction data across thousands of businesses can identify fraud patterns you wouldn't see at your own volume alone.

• Configurable rules engine: Prebuilt models catch broad fraud patterns, but your business has specific risk characteristics that no generic model fully captures. The ability to write custom rules (e.g., block orders shipping to addresses tied to prior fraud, flag high-value orders with mismatched billing and shipping countries, require 3D Secure authentication (3DS) for transactions above a certain threshold) lets you tune the system to your actual exposure.

• Chargeback rate impact data: Any vendor should be able to show you what their tool does to dispute rates for businesses like yours. If they can't provide reference data or case studies with actual numbers, treat that as a red flag.

• Integration with your stack: Whether you're on Shopify, WooCommerce, or a custom platform determines which tools are practical. A solution requiring deep API integration isn't viable if your engineering team can't support it.

How do e-commerce fraud prevention tools affect your chargeback rate?

Chargebacks are a direct financial consequence of fraud, and they compound. Card networks monitor your chargeback rate: if you exceed the networks' thresholds consistently, you can face fines or increased scrutiny, or even lose the ability to process cards altogether.

Fraud prevention tools affect your chargeback rate by blocking fraudulent transactions before they complete, which prevents disputes from ever being filed. They also generate the evidence you need to win in the representment process when disputes are filed.

The ratio of false positives matters too. Tools that decline too aggressively reduce fraud but also reject legitimate customers, which appears in authorisation rate data rather than dispute data. Tools typically let you adjust sensitivity thresholds, and many will route uncertain cases to a manual review queue rather than automatically decline. Finding the right calibration is where most of the ongoing management work happens.

How do you choose the right e-commerce fraud prevention solution?

The right e-commerce fraud prevention solution depends on factors specific to your business. A few structural issues narrow the field:

• Transaction volume: ML tools improve with data. If you're processing fewer than a few hundred transactions per month, a stand-alone ML platform trained on your own history won't have enough signal to be accurate. You'll depend more on network-level data from platforms with broad coverage or on the integrated fraud tools from your payment provider.

• Fraud concentration: If most of your losses come from CNP fraud at checkout, a payment layer solution might cover most of your exposure. If you're seeing promotion abuse or return fraud, you need a tool that operates across the full customer lifecycle.

• Technical infrastructure: A third-party fraud platform with a full-featured API is powerful, but only if you have engineers to integrate and maintain it. Businesses without dedicated engineering resources are often better served by tools that integrate natively with their e-commerce platform or payment provider.

• False positive tolerance: If you sell commoditised goods at low margins with high competition, declining a legitimate customer carries a different cost than if you sell high-margin products with long purchase cycles. Businesses that rely heavily on customer relationships must seriously consider false positive rates in their evaluation.

• Current chargeback exposure: If your card network is monitoring your business or about to do so, a chargeback guarantee model might make more financial sense than a detection-only tool, even at higher cost. If fraud is a manageable but growing concern, a detection-focused tool with good rules customisation gives you more control over outcomes.

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 insights.
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.