Detect fraudulent transactions before they happen using real-time behavioral signals, transaction monitoring, and anomaly detection.
Payment fraud is any attempt to move money using a card, wallet, bank transfer, or local rail without the legitimate owner’s consent—or with intent to abuse your product’s rules. That includes stolen cards, synthetic identities, and compromised accounts used only to extract funds.
It also covers instant-payment abuse where fraudsters probe limits with small authorizations, then escalate once a path looks “clean.” In markets with UPI, wallets, or real-time rails, speed helps users—but it also shrinks the window where you can intervene.
Friendly fraud (chargebacks after a legitimate purchase) is a separate but related problem: the payment cleared, but the outcome is still a loss for you. A solid payment fraud detection system focuses on signals at authorization time, while chargeback workflows handle disputes after settlement. For login and session-level abuse, see our guide on account takeover fraud detection.
For every scenario in one place, browse our fraud detection use cases.
Signals we look for map to how real attackers behave—not just static rules.
Low-amount pings to verify a stolen instrument works before a larger pull. Often clustered in time or across many accounts.
Many attempts in minutes—humans rarely pay that fast. Automation and card testing stand out in velocity and spacing.
IP, device locale, and card BIN or shipping geography don’t line up with the customer’s normal footprint.
New device, new browser fingerprint, or emulator signals right before a high-value payment from a “trusted” user.
Unusual navigation, atypical session length, or checkout paths that match scripted flows rather than normal shopping. Combined with amount and timing, these patterns separate fraud from honest edge cases.
Most stacks weren’t built for sub-second decisions across cards, wallets, and account context at once.
Batch reconciliation and chargeback queues tell you what went wrong last week. They don’t stop the next authorization from clearing when milliseconds matter.
Static thresholds and allow/block lists are easy to bypass. Attackers rotate instruments, amounts, and timing until they find a gap.
If you only look at the payment object, you miss how the user got there—device history, session quality, and whether this action fits their baseline.
Four layers that feed a single risk decision before completion.
Recognize devices and browsers consistently without slowing checkout.
Compare current activity to that user’s history and to normal session patterns.
Combine amount, velocity, merchant context, and route-specific signals into one score.
Approve, step-up, or block in line with your policy—before funds commit.
We’re built for teams that need a clear score and a defensible reason code—not a black box and not a spreadsheet of rules to babysit.
Score at authorization so you can decline, route to 3DS, or manual review while the payment is still in flight.
Card acquirers, wallets, bank transfers—send the fields you have; we return a score and signals your backend can act on.
One API path for scoring—no need to rip out your processor or rebuild checkout to get modern fraud checks.
Designed for low-latency paths so you’re not choosing between speed and safety.
Start detecting suspicious transactions in real time.
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