How do you detect payment fraud?

Payment fraud detection scores the risk of each checkout session in real time, before the transaction clears, by analyzing the buyer and the device rather than only the card. A typical approach combines device and connection signals, persistent identity, velocity and reuse patterns, and a risk score that combines these into a single number with the reasons behind it. ShieldLabs returns this score and breakdown on the first visit.

What type of fraud is most common in online payments?

Card testing and account takeover are the most common online payment fraud types, and both leave the same trace: one device acting as many different buyers or accounts. Card testing: small amounts submitted on many stolen cards to find which ones still work.; Account takeover: a compromised account accessed from an unrecognized device or anonymized connection. ShieldLabs ties each session to a persistent identifier, so this one-device-many-buyers pattern surfaces across cookie clears and IP rotation before any transaction clears.

What types of payment fraud does ShieldLabs catch?

ShieldLabs surfaces the anonymity signals and patterns behind common checkout fraud. Anonymous checkout sessions: VPN, proxy, and anti-detect browser traffic.; Patterns indicating account takeover from new or unrecognized devices.; Patterns indicating fraud rings running many buyer accounts from one device.; Returning fraudulent buyers who clear cookies or rotate IPs.

How can payment fraud prevention solutions help?

Payment fraud prevention solutions cut chargebacks and flag risky checkouts by adding a layer of analysis on top of the transaction itself. Payment processors analyze the transaction, card details, billing address, velocity checks. Device intelligence analyzes the visitor and device on the session, before the transaction is submitted. ShieldLabs covers the session layer: it flags anonymous buyers and returning fraudulent buyers in real time.

Will ShieldLabs block legitimate buyers at checkout?

No. ShieldLabs never blocks anyone automatically; it returns a risk score and a signal breakdown, and the decision logic stays with your team. A VPN alone raises the score modestly, not enough to block on its own. A high score usually comes from several signals firing together. You choose which thresholds trigger extra verification and which sessions pass straight through.

How does device fingerprinting prevent payment fraud?

Device fingerprinting gives every checkout session a persistent identifier, so one device acting as many buyers becomes visible even when it clears cookies or rotates IPs, a direct indicator of card testing or account takeover. Builds a DeviceID from 100+ device, OS, browser, and network signals.; Survives cleared cookies, incognito mode, and IP rotation.; Ties repeated card-testing attempts to one identifier across sessions.; Flags logins from unrecognized devices on a VPN or anti-detect browser. ShieldLabs surfaces these high-risk sessions with up to 99% accuracy before the transaction completes.

Payment Fraud Prevention

Stop payment fraud before the transaction clears

Prevent card testing, anonymous checkout abuse, and account compromise without adding friction for real buyers, through persistent identification and real-time risk scoring

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Stop card testing at checkout

Catch card testers cycling stolen cards across multiple checkout sessions

Flag anonymous checkout sessions

Identify buyers hiding behind VPN, proxy, and anti-detect browser infrastructure

Reduce fraudulent chargebacks

Stop fraudulent transactions before they reach payment processor dispute thresholds

Keep real buyers friction-free

Legitimate buyers complete checkout without verification friction or added steps

How ShieldLabs prevents payment fraud

ShieldLabs combines 100+ device, OS, browser, IP and network layer signals, surfacing anonymity signals and abuse patterns at checkout in real time

Accurate Identification

Identify returning visitors and users across sessions, cleared cookies, rotated IP and incognito mode

Anonymous Traffic Detection

VPN, proxy, Tor, anti-detect browser, geolocation spoofing, IP reputation and other anonymity signals

Ready Risk Score

A ready-to-use risk model that scores every session 0–100 with signal breakdown

Detect abuse with in-depth pattern analytics

Pre-built patterns indicating account takeover and other abuse patterns

Real-Time Analytics

Aggregate traffic risk score, per-session signal breakdown, and active abuse patterns

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Integrate into any checkout or payment flow

1
Sign up
Sign up and get 5,000 free identifications
2
Install snippet
Install one JavaScript snippet on the checkout page
3
View analytics
View detailed analytics on anonymity signals, risk score, and potential abuse in checkout traffic
4
Use API & Webhooks
Use the API and Webhooks to flag or block high-risk sessions before payment is submitted

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api.shieldlabs.ai/v1/visits/latest

{
  "request_id": "0c284695-cf0b-4755-8beb-0a2e9536595e",
  "visitor_id": "aa8c616a-8a25-4a5e-bee2-a9a08e5128a4",
  "device_id": "6a45967d-1371-9652-ba99-b01ea3992208",
  "user_hid": "u_9f2a41c7",
  "ip": "62.197.149.124",
  "country": "Lithuania",
  "connection_type": "vpn",
  "os": "Windows",
  "browser": "Chrome",
  "device_type": "desktop",
  "risk_score": 85,
  "signals": [
    { "name": "antidetect_browser", "weight": 60 },
    { "name": "vpn", "weight": 15 },
    { "name": "timezone_mismatch", "weight": 10 }
  ]
}

Catch fraudulent buyers before the charge goes through

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