USE CASE
The fraud pattern is in your data. Your batch sees it next week.
A pattern that has appeared in 12 prior claims this quarter shows up again. Your batch report on Monday will surface it. The payout goes out Friday.
npayload pauses the payout while a human reviewer takes a closer look. AI patterns detected in flight. Reasoning chain saved for the dispute. Every decision auditable for the regulator.
WHAT THIS LOOKS LIKE
Six fraud signals that fire faster than batch detection.
Modern fraud moves at the speed of the click. Each one below is a moment your AI can act, your batch cannot.
Your apps
right now
Pattern match on past fraud
Same fingerprint as a known ring
Velocity anomaly
Too many claims, too fast, same metadata
Geographic mismatch
Location data does not match the claim
Document anomaly
Photo or document looks AI generated
Identity reuse
Same identity, different claim, different policy
Settlement chain risk
Money is being moved to high risk destinations
Every signal is a payout your team can save.
WHICH NPAYLOAD WEDGES
Two wedges. The payout pauses.
Primus reads the signal and pauses. Pipes enriches the claim with patterns and history. Both record reasoning for the dispute.
Primus
AI agents pause the payout when a fraud pattern matches. Reasoning chain saved.
See PrimusPipes
Claims enriched with prior pattern data, identity reuse signals, geographic checks.
See PipesEvents
Pause signal delivered to your payment system instantly. Human reviewer notified with full chain.
See EventsONE SUSPICIOUS CLAIM, END TO END
A claim files at 2:47am. The payout pauses by 2:47:00.3am.
Faster than the fraudster can transfer the money.
Claim filed
Claim submitted with metadata and supporting documents.
Pipes enriches
Past patterns, identity history, geographic and document checks attached.
Primus reads
AI compares to 90 days of similar claims. Fraud pattern detected.
Decision committed
Hash chained record. Reasoning chain saved.
Payout paused
Events delivers pause to the payment system. Human reviewer notified.
Three flavors of fraud worth catching live.
Each one a six figure loss prevented per quarter.
Ring fraud
Same fingerprint across multiple claimants. Caught at filing, not at batch.
Document forgery
AI generated or altered evidence flagged before any human reviewer touches it.
Identity reuse
Same identity filing across multiple policies. Pattern surfaces immediately.
What teams ask before they ship.
Direct answers.
"We have a fraud team."
Keep them. They handle the cases Primus pauses for review. The team handles 10x more cases because routine fraud never reaches their desk.
"What about false positives?"
Every pause has a reasoning chain. The team confirms or releases. False positives drive prompt and rule updates. Precision improves quarterly.
"Regulators want explainability."
Hash chained audit on every decision. Reasoning chain attached. Regulators get the why, not just the outcome.
Use case questions.
The fraud is in your data. Catch it before the wire transfer.
Wire one claim flow through npayload. Catch your first ring fraud this afternoon.
Join the waitlist.