Account takeover, caught at the signal level
Device fingerprint mismatches, credential reuse patterns, and session anomalies scored before the transaction reaches your authorization gateway.
Learn moreTransaction risk scoring API
Riskgrove catches the account-takeover and card-testing patterns that rules-based engines quietly wave through. Built for fintech and marketplace risk teams.
Pattern detected
4 distinct card numbers tested from the same device fingerprint in 8 minutes. Velocity pattern consistent with credential stuffing pre-validation.
Early-access pilot program participants
The problem
Attack patterns evolve faster than rule update cycles. By the time your team encodes a new pattern, attackers have already rotated to a variation your rules don't match.
How it works
Ingest
POST transaction fields to the scoring endpoint before your authorization request completes. Fields: card token, device fingerprint, user ID, amount, merchant category.
Score
Riskgrove runs gradient-boosting and behavioral graph models against real-time and historical signals in under 40 milliseconds median latency.
Act
Response includes a 0-100 risk score, top contributing signal reasons, and a recommended action (approve / review / decline). Wire to your auth logic.
Capabilities
Each detection module targets a distinct attack class with its own signal set and model architecture.
Device fingerprint mismatches, credential reuse patterns, and session anomalies scored before the transaction reaches your authorization gateway.
Learn moreMulti-card velocity scoring across device fingerprints, merchant contexts, and time windows that per-card rate limits can't see.
Learn moreMulti-dimensional rate analysis across entity pairs (card-device, card-merchant, user-IP) surfaces coordinated attack patterns that single-field velocity ignores.
Learn moreEarly results
"We had a card testing attack that cost us around $12K in chargebacks before a colleague pointed us to Riskgrove. The device-fingerprint velocity scoring caught the pattern in the first batch of transactions on the next rotation. That's a signal our rules engine just doesn't have."
"We were seeing ATO on seller accounts every week and couldn't pin it with transaction-level rules alone. Riskgrove's behavioral graph signals flagged session patterns we had no visibility on. Four weeks in and ATO volume on seller accounts dropped to near zero."
Integration
Drop Riskgrove behind your payment gateway. No rule configuration, no ongoing tuning required.
// Request
const score = await fetch('https://api.riskgrove.com/v1/score', {
method: 'POST',
headers: { 'Authorization': `Bearer ${apiKey}` },
body: JSON.stringify({
card_token: 'tok_live_8f2a...',
user_id: 'usr_0019b',
amount_cents: 129900,
device_id: 'dvc_f7c3a9',
merchant_category: '5045'
})
});
// Response (38ms)
{
"risk_score": 87,
"recommendation": "review",
"top_signals": [
"device_multi_card_velocity",
"card_first_use_high_amount",
"session_anomaly_score"
],
"latency_ms": 38
}
Pricing
Ready to start?
Book a 30-minute call with our team. We'll walk through your current fraud stack and show you where Riskgrove fits.