A fraud detection application programming interface (API) is a software tool that can look at signals such as card details, device fingerprint, and behavioural patterns to assess whether a transaction is likely fraudulent. Some payment providers build this safeguard directly into the standard payment flow. That way, businesses don't need to create a separate system to get real-time fraud assessment on every charge.

A 2026 survey found that 76% of organisations experienced payment fraud attempts in the previous year. Below, we'll discuss what a fraud detection API does, how to customise it, and how to check its performance.

Key takeaways

• Some payment providers evaluate transactions automatically as part of the standard payment flow and don't require a separate API call.

• Custom rules and blocklists and allow lists can handle fraud patterns specific to your business.

• Tracking fraud rate, authorisation rate, and false positives can help you find the right balance between blocking fraud and approving real customers.

What is a fraud detection API?

A fraud detection API is a programmatic interface that assesses transaction risk, which can help your business decide whether to approve certain payments. The API evaluates card details, device fingerprint, internet protocol (IP) address, and whether billing and shipping addresses match, and then it immediately delivers a score or recommendation.

How can you customise a fraud detection API with rules and authentication?

You can customise a fraud detection API using a combination of three types of controls:

• Custom rules: These set specific conditions for when the API will review a transaction for your business. For example, it can block transactions above a certain amount from a country you don't ship to or require 3D Secure (3DS) for first-time customers whose cards don't match their billing addresses.

• 3DS: Having the customer complete this authentication step shifts liability for certain disputes away from your business.

• Blocklists and allow lists: APIs check transactions against these lists, using card details, email addresses, or other identifiers. If a specific card keeps showing up on fraudulent orders, you can add it to a blocklist, and every future transaction using that card will get stopped. Conversely, allow lists let all transactions from included cards through, which is useful for a repeat customer whose unusual purchase pattern would otherwise prompt a review.

Rules can be used in tandem with the risk score. A rule can instantly block a fraudulent transaction pattern you've already seen, while the score alerts you to new fraud patterns. If you run both, then you don't have to rely on one system to catch everything. Requiring 3DS authentication can add friction for the customer, which is why many businesses reserve this protocol for transactions that score high enough to justify the extra step.

How do you monitor and improve a fraud detection API's performance?

A fraud detection API is only as good as your ability to measure what it's doing. Here are four metrics to pay attention to:

• Fraud rate: The percentage of transactions that are flagged as fraudulent, usually measured against total transaction or dollar volume

• Authorisation rate: The percentage of legitimate transactions that get approved. A system that blocks too aggressively cuts fraud but also turns away real customers

• False positive rate: How many legitimate transactions your rules or scoring flagged incorrectly, which acts as a measure of the cost of fraud prevention efforts

• Dispute outcomes: How many flagged transactions turn into chargebacks compared with how many get reviewed and cleared

Tightening rules to cut fraud will likely raise false positives and lower authorisation rates. The goal isn't to maximise any single number. You'll want to balance the trade-offs given your margins and what your customers will tolerate.

Is a fraud detection API worth building?

If you have a newer business without years of transaction data, you might benefit from using a model trained across an entire network of payments. Businesses that already process payments through Stripe can use Stripe Radar instead of creating a parallel infrastructure. If your business processes a large payment volume, you might need a dedicated fraud team.

How Stripe Radar can help

Stripe Radar helps detect fraud and unlock growth, using artificial intelligence (AI) trained on data from Stripe's global network. Radar helps protect your business from fraud throughout the customer lifecycle before it affects your bottom line, while helping you approve more legitimate customers and payments.

Radar can help your business:

• Prevent fraud losses: Radar's AI learns from more than US$1.9 trillion in annual transactions across Stripe's global network, helping it identify and block fraud patterns that individual businesses might not catch on their own.

• Unify protection across evolving fraud attacks: Radar brings protection against transaction fraud, account fraud, and customer abuse all into one solution, so you gain unified protection across major fraud types.

• Adapt as fraud evolves: Radar continuously adapts to changing fraud patterns, from first-party abuse to agentic transactions, helping protect your business against emerging threats.

• Work with your existing tech stack: Use Radar with Stripe payments with no integration required, or access Radar's intelligence through programmable APIs, whether you process payments on Stripe or not.

Learn more about Stripe Radar or get started today.

FAQs about fraud detection APIs