Fraud Score API
Event signals in. Fraud probability, the reasons, and a decision out.
Transactional and account fraud risk scoring powered by Brainiall Fraud engine. Send whatever signals you have for a payment, signup or login — amount vs. the account's norm, velocity, device/IP novelty, geo mismatches, AVS/CVV, chargeback history — and get back a 0-1 fraud probability, a risk level, the exact risk factors that drove the score, and a recommended allow / review / deny decision. $0.0075/event. Report real outcomes via /feedback and the model re-calibrates to your own data.
How we compare
The incumbents either make you train and host a model yourself (AWS Fraud Detector), gate the product behind an enterprise sales motion and a percentage-of-GMV contract (Sift, Kount, Signifyd, Riskified), or only score the payments that flow through one processor (Stripe Radar). Brainiall is a flat per-event REST call: send signals, get a scored decision with the reasons attached, improve it with your own labels — self-serve from the first call, no platform to adopt.
| Provider | Shape | Pricing model | Approx. price | Onboarding |
|---|---|---|---|---|
| Brainiall Fraud Score | REST: /score + /feedback — explainable, model-agnostic signals | Per scored event | $0.0075 / event (/feedback free) | Self-serve, instant API key |
| AWS Fraud Detector | Train & host your own model on AWS, then call it | Per prediction (+ training/storage) | ~$0.005–$0.03 / prediction | AWS account + model training |
| Stripe Radar (for Fraud Teams) | Rules + ML, bundled into Stripe payments | Per transaction | ~$0.05 / transaction (Radar for Fraud Teams) | Stripe-processed payments only |
| Sift / Kount / Signifyd / Riskified | Enterprise fraud platform (often chargeback-guarantee) | % of GMV / per-order contract | Enterprise pricing | Sales-led, long onboarding |
Prices are list-price approximations for orientation, not quotes — most fraud vendors price by negotiation. Always check each vendor's current terms.
Pricing
One per-event price for scoring; submitting outcomes via /feedback is unmetered. The free tier is enough to wire scoring into a checkout flow and start collecting feedback.
Free
$0/mo
100 scored events/month · /feedback unmetered · forever free
Starter
$39/mo
~10,000 events/month · custom decision bands · feedback dashboard
Pro
$149/mo
~50,000 events/month · priority queue · 99.5% SLA
Business
$499/mo
~200,000 events/month · re-calibration on your labels · dedicated capacity · email + Slack
PAYG: $0.0075 / scored event (Brainiall Fraud engine). /feedback calls are free. No minimum spend, no contract, no percentage of your GMV.
Two calls: score, feedback
# 1. Score an event — send whatever signals you have; all fields are optional
POST https://api.brainiall.com/v1/fraud/score
{"event_id": "ord_8821", "event_type": "payment",
"amount": 4200, "currency": "USD", "avg_txn_amount_30d": 85,
"txn_count_1h": 7, "distinct_cards_24h": 4, "distinct_countries_24h": 3,
"is_new_device": true, "is_new_ip": true, "is_tor": true,
"card_country": "US", "ip_country": "RU", "account_age_days": 0,
"avs_match": false, "cvv_provided": false, "prior_chargebacks": 1}
-> {"event_id": "ord_8821", "fraud_probability": 0.99, "risk_level": "critical",
"decision": "deny", "risk_score_points": 28.0,
"risk_factors": [
{"factor": "amount_far_above_typical", "weight": 5.0, "direction": "increases_risk",
"detail": "amount is 49.4x the 30-day average (4200.00 vs 85.00)"},
{"factor": "tor_exit_node", "weight": 3.5, "direction": "increases_risk",
"detail": "request originated from a Tor exit node"},
... ],
"decision_bands": {"allow_below": 0.40, "review_below": 0.75},
"model": "Brainiall Fraud engine"}
# override the bands per request: "decision_bands": {"review": 0.30, "deny": 0.80}
# 2. Report the confirmed outcome so the model re-calibrates to your data
POST https://api.brainiall.com/v1/fraud/feedback
{"event_id": "ord_8821", "label": "chargeback", "notes": "issuer dispute, fraud confirmed"}
-> {"event_id": "ord_8821", "label": "chargeback", "accepted": true, "feedback_id": 42}
# label ∈ fraud | legitimate | chargeback | dispute
GET https://api.brainiall.com/v1/fraud/feedback/stats
-> {"total": 1240, "by_label": {"legitimate": 1180, "fraud": 41, "chargeback": 19}}Every field is optional — start with the three or four signals you already have and add more over time; the score and the risk-factor list adjust accordingly. The response is fully explainable: risk_factors lists exactly which signals moved the score and by how much (positive = increases risk, negative = a mitigant), so you can show a human reviewer why an event was flagged.
What it's for
- Checkout / payment risk: score each transaction at authorization time and auto-approve the clearly-good, send the borderline to manual review, and block the clearly-bad — with the reasons attached for the review queue.
- Signup & account-creation abuse: catch fake-account farming and promo abuse from the velocity, device/IP novelty, disposable-email and VoIP-phone signals before the account does anything.
- Account-takeover signals: flag logins and account changes that come from a new device/IP, an anonymizing network, or a country that doesn't match the account's history.
- Card-testing detection: spot bursts of small transactions across many cards on one account — the classic stolen-card validation pattern.
- Tune it to your business: override the allow/review/deny probability bands per request, and feed confirmed outcomes (chargebacks, disputes, confirmed-good) back via
/feedbackso the model re-calibrates to your own fraud rate. - One bill, one key: same Brainiall API key and usage-based billing as the rest of the catalog — no separate fraud vendor to procure, no percentage of GMV.
Press kit & resources
What reviewers, integrators and procurement teams typically ask for.
One-page datasheet
Pricing, the signal list, and copy-pasteable curl snippets on one page — built for buyer review.
Download PDFAPI reference
OpenAPI spec, the full signal schema, the risk-factor catalog, error codes and rate limits.
Read docs →Compare the catalog
How Brainiall's specialty APIs line up against AWS, Stripe and the fraud specialists, use case by use case.
See the comparison →More specialty APIs
Same single API key, same usage-based pricing, different problem solved.



