FRAUD PROTECTION

Stop Fraud
Before It Happens

Our AI fraud engine evaluates 200+ signals per transaction in under 50ms — blocking card testing, fraud rings, account takeovers, and chargeback scams before they reach your ledger.

0.04%
Fraud rate (vs 0.11% industry avg)
67%
Reduction vs rules-only systems
50ms
End-to-end scoring latency
200+
Signals evaluated per transaction
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Live Transaction Scoring
BLOCKED
txn_4f2a91c7 · €847 · 43ms
Risk: 96
stolen card pattern velocity anomaly new device
APPROVED
txn_8b3e05f1 · €124 · 31ms
Risk: 7
known device normal velocity trusted IP
REVIEW
txn_c1d7a22e · €2,100 · 47ms
Risk: 61
  The Fraud Problem

Why Rules-Only Fraud Systems Fail

Fraud evolves faster than static rule sets. Without ML, you're always one step behind.

$48.7B

Global fraud losses in 2025

Card fraud, ATO, and synthetic identity attacks combined — Nilson Report 2025.

3×

Cost of false positives vs real fraud

Every declined legitimate customer costs more than the fraud it prevented — lost revenue, churned customer, support cost.

67%

Fraud reduction with AI vs rules-only

Our ML models catch 67% more fraud than traditional rule-based systems — measured across 10,000+ merchants.

  How It Works

Three Layers of Defence

Stacked protection that catches fraud at every stage of the transaction lifecycle.

1
Layer 1

Pre-Auth Screening

Instant high-speed filters eliminate obvious fraud before the AI model is even invoked — zero latency penalty.

  • Device fingerprinting (150+ signals)
  • IP reputation & geolocation delta
  • Velocity checks (card, email, device)
  • BIN intelligence & issuer lookup
2
Layer 2

AI Risk Scoring

340M-parameter transformer processes 200+ features and returns a 0–100 risk score in under 50ms.

  • 200+ engineered features
  • Transformer architecture, 50ms end-to-end
  • Trained on 14B+ real transactions
  • Monthly retraining on new fraud patterns
3
Layer 3

Post-Auth Monitoring

Continuous monitoring after authorisation catches evolving fraud that exploits legitimate approvals.

  • Network graph & fraud ring analysis
  • Account takeover detection
  • Manual review queue & case mgmt
  • Chargeback pre-dispute alerts
  Signal Intelligence

200+ Signals. Every Transaction.

Our model evaluates an exhaustive feature set — from device telemetry to behavioral biometrics to cross-merchant network graphs.

Model Size
340M
parameters
Training Data
14B+
real transactions
Retrained
Monthly
on new fraud patterns
Scoring Latency
50ms
end-to-end

Signal Categories (sample of 200+)

transaction amountmerchant categorycard BINissuer countrydevice OSbrowser fingerprintIP geolocation deltatime since last txntxn count 1htxn count 24havg amount 30demail domain ageshipping distancebilling/shipping matchuser-agent entropysession depthscroll patternkeystroke timingmouse movementtouch pressurescreen resolutiontimezone offsetlanguage matchproxy detectionTor exit nodedatacenter IPcard velocitydevice velocityemail velocitychargeback historydispute rateprevious declinesnetwork graph linksshared device usersaccount age3DS resultCVV matchAVS resultissuer auth timerefund velocitytrial abuse scorefraud ring affinity+158 more…
  Capabilities

Full Fraud Defence Suite

Real-time Scoring

50ms end-to-end scoring including device, network, and AI layers. Zero latency tax on your checkout conversion.

Device Fingerprinting

150+ device signals — hardware, software, browser, network — combined into a persistent device graph that survives browser resets.

Behavioral Biometrics

Typing cadence, mouse dynamics, and touch pressure create an invisible behavioral fingerprint — impossible for bots to replicate.

Network Graph Analysis

Graph-based fraud ring detection links cards, emails, devices, and IPs across merchants — exposing coordinated attack networks in real time.

Chargeback Prevention

Flag high-risk transactions likely to dispute before the chargeback window opens — enabling proactive refunds and Ethoca/Verifi alerts.

Custom Rules Engine

Build velocity rules, allowlists, blocklists, and challenge logic with a no-code editor. Deploy in seconds — no redeploy required.

Integration

One API call.
Full fraud score.

Pass transaction context to our fraud endpoint. Receive a structured risk score in <50ms. Works with any backend — Node, Python, Go, Ruby, PHP, Java, or direct REST.

Read the Docs
fraud-score.py 
# POST /v1/fraud/score
import paymentgate

response = paymentgate.fraud.score(
  transaction={
    "amount": 8750,
    "currency": "EUR",
    "merchant_id": "mer_x9k2",
  },
  customer={
    "email": "[email protected]",
    "ip": "82.194.11.42",
    "device_token": device_token,
  }
)
# Response arrives in <50ms
# {
#   "risk_score": 12,
#   "decision": "approve",
#   "signals": ["known_device",
#               "normal_velocity"]
# }
if response["risk_score"] > 70:
    handle_high_risk(response)
  Results

What Our Merchants Achieve

Measured across 10,000+ merchants in the first 6 months after onboarding.

0.04%

Fraud rate (vs 0.11% industry avg)

73% below industry average — meaning less revenue lost and fewer chargebacks to dispute.

67%

Reduction vs rules-only systems

Merchants migrating from rule-based fraud tools see an average 67% reduction in fraud losses within 90 days.

43%

Fewer false positive declines

Better precision means fewer legitimate customers blocked — directly recovering conversion rate.

  Customer Stories

Trusted by Nordic & Baltic Leaders

"We went from a 0.8% chargeback rate to 0.06% in three months. The AI catches fraud rings our old rule system never would have flagged — because it sees patterns across all merchants, not just ours."

TM
Tuomas Mäkinen
Head of Risk · HelsiPay Oy

"The 50ms scoring latency means our checkout flow is completely unaffected. Customers don't even know fraud scoring is happening. We recovered 43% more legitimate transactions that our old system was blocking."

EB
Elīna Bērziņa
CTO · Riga Marketplace SIA

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