Solutions / AI Anti-fraud

Catch fraud in real time. Clear good customers

Real-time risk scoring that stops fraud at the decision point, while letting legitimate customers through untouched

Real-timeExplainableAdaptive
9:41
Fraud Operationsreal-time risk scoring On-premise
Fintech / Banking · in production
0.18
Approved
cleared in 92 ms · threshold 0.70
tx 0xA82F · low-risk · device known
Recent decisions
Aurora Retail$2480.12
Unverified merchant$9,8000.94
real-time scoring 1.2M / day
iPhone running Zentavor Fraud Operations
−94%false-positive rate
<120 msin the payment path
24/7real-time, in production
Inside the decision

It doesn't just block. It shows why

Every transaction is scored against real fraud signals. Watch a few real patterns get caught, with the reason codes that satisfy compliance

Scored today
1,286,676
Blocked
331
False positives
−94%
vs last 7 days
p90 latency
118 ms
vs last 7 days
TimeMerchantAmountScoreStatus
Case #48326 REVIEW
$138.45 · Zara
new merchant · amount above average
Why flagged
0.67risk · threshold 0.70
AI summary · copilot

A couple of mild anomalies but low overall risk, held for review rather than declined.

Graph intelligence

Catch the ring, not just the transaction

A rules engine sees four clean payments: each under $10k, each on a normal-looking account. We score the connections and see one money-laundering ring. That's the kind of organized fraud single-transaction systems miss

Detected ring
Structuring · 4 accounts · $38k
ACC-4471$9,800London · shared device d8f1··
ACC-7758$9,400Moscow · shared IP
ACC-1130$9,600Dubai · shared device d8f1··
MULE-002$38k inHong Kong · fan-in · cash-out
Individually, each payment clears. Connected, they form one laundering ring, and it gets caught.
From signal to decision

A system that adapts, not a static rulebook

Streaming features feed an ensemble, a calibrated score drives the policy, and analyst feedback retrains the model on new patterns

run #8842 · transaction scoredcompleted · 118 ms end-to-end
streaming features
velocity · device · geo · graph
ensemble model
gradient-boosted + sequence
calibrated score
probability + reason codes
decision policy
approve · step-up · block
analyst feedback
labels & case review
retrain
nightly · tracks new patterns
The challenge

Rules that block your best customers

Legacy rules engine

Legacy rules block good customers along with the bad.
Sub-threshold payments slip through as organized rings.
Opaque allow/deny decisions fail compliance.
New fraud patterns need analysts to hand-write rules.

With Zentavor

Real-time risk scoring clears legitimate customers, reducing false positives by 94%.
Graph intelligence catches the ring, not just the transaction (e.g. a $38k structuring ring).
Reason codes + full audit trail on every decision.
Models retrain nightly and adapt. Scored in <120 ms in the payment path.
FAQ

Your frequently asked questions

Can it score in the payment path without adding latency?
Yes. Scoring runs on precomputed streaming features, returning a decision in well under 200 ms, inside a checkout latency budget.
Is the model explainable enough for compliance?
Every score ships with reason codes and a full audit trail, so compliance can see exactly why a transaction was approved, stepped up or blocked.
How does it keep up with new fraud patterns?
Analyst labels feed a retraining loop, so the model tracks emerging patterns instead of waiting for someone to write a new rule.
Does our data leave our environment?
No. The system runs inside your perimeter, on-premise or private cloud, so transaction data never leaves your environment.
Let's talk

Stop fraud without stopping revenue

Share your transaction profile and risk constraints. We'll scope a model and integration plan

Request a demo