Fraud & Anomaly Detection

Add a layer that knows what normal looks like for each of your accounts, on top of the checks your payment gateway already runs.

The problem it solves

Payment gateways screen for fraud patterns that are common across every merchant, which means they aren't tuned to what's unusual specifically for your accounts. A compromised login, an out-of-pattern shipping change, or an order that simply doesn't fit an account's history can pass a generic check without ever standing out.

What it does for your business

Learns each account's normal

Order size, cadence, product mix, and shipping pattern are compared against that account's own history, not a single global threshold.

Complements your existing screening

Sits alongside your payment gateway's checks rather than replacing them, adding a layer tuned to account-level behavior.

Reduces false holds on good customers

Because it knows an account's normal ordering pattern, it's less likely to flag a loyal customer's large seasonal order as suspicious.

Routes instead of just blocking

Scored orders go to a review queue with a stated reason, and only the clearest cases are held automatically, keeping your team in control of the threshold.

Where this moves the needle

Lower fraud loss and chargebacks Fewer false declines Less manual review time Faster time to detect
How it works
An anomaly detection model is trained on order, account, and payment-metadata features to learn each account's normal range, then scores new orders against that learned baseline and flags outliers for review.
What we need from you
Order history with any known fraud, chargeback, or hold outcomes, your account master, and payment metadata available from your gateway.

See Fraud & Anomaly Detection on data like yours

A short walkthrough on how orders are scored and routed, and how the review threshold stays under your control.

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