WhiteBox runs every transaction through multiple AI models. When they all say fraud, block it. When they disagree, route it to your fraud team with the full breakdown. Stop blocking good customers. Start catching real fraud.
Example: Customer buys a $2,100 luxury handbag as a gift. Ships to a different address. VPN detected (they are at work). Single model flags it as fraud. Customer gets blocked, calls support, closes their account.
WhiteBox: 3 models say legitimate, 1 says suspicious. Consensus: let it through. The one dissenter gets logged for pattern review.
Example: Small $47 test charge from a new device at 2AM, followed by a $890 electronics purchase 4 minutes later. Amount is under the threshold. Single model says legitimate because the first charge was small.
WhiteBox: 2 models flag the pattern, 2 miss it. Disagreement triggers escalation. Fraud team catches it before the $890 ships.
Example: Third laptop claim in 14 months. Different shipping addresses each time. Customer has a legitimate 8-year account history.
One model says fraud (pattern), one says legitimate (account age), two say suspicious. No single model captures the full picture. WhiteBox shows the split and lets your fraud analysts decide.
Every run, every log-prob, every disagreement -- recorded. Replay any decision from its ID.
Score every transaction in real time. Block confirmed fraud, approve clear transactions, escalate the gray zone.
Detect suspicious logins, password changes, and shipping address updates. Multi-model consensus catches patterns single models miss.
Flag high-risk transactions before they ship. Reduce chargebacks by catching fraud early without blocking legitimate customers.
Detect serial returners, wardrobing, and refund fraud patterns. Escalate suspicious patterns for manual review.
Identify fake accounts created to exploit promotions. Multi-model agreement catches sophisticated abuse that single models miss.
Flag suspicious seller behavior: fake listings, shill reviews, dropshipping from prohibited sources.
Every false positive costs you $150-300 in lost revenue + support costs.
At 1,000 flagged transactions/day with 90% false positive rate, that is $135,000-270,000/month in lost revenue.
WhiteBox reduces false positives by routing disagreements to humans instead of auto-blocking.
Even a 20% reduction in false positives pays for WhiteBox 100x over.
| Feature | Rules-based | Single AI model | WhiteBox |
|---|---|---|---|
| False positives | High (rigid rules) | Medium (one opinion) | Low (consensus) |
| Sophisticated fraud | Missed (no pattern learning) | Sometimes caught | Caught by disagreement |
| Gray zone | Auto-block or auto-allow | Random | Routed to fraud team |
| Explainability | Rule name | Black box | "3 of 4 models said fraud" |
| Audit trail | Rule log | No | Every model vote logged |
| Real-time | Yes | Yes | Yes (< 2 seconds) |
20 free to start. No credit card.
That's 1,000 fraud checks for $10.
20 free checks. Then $0.01 each. Full audit trail from day one.