Fraud decisioning network scoring transaction risk in real time to block bad actors and disputes.
Sift is a fraud decisioning platform that scores payment transactions, account creations, and logins in real time, using machine learning trained on a network of customer data. Risk and payments teams use it to block stolen-card fraud, account takeover, and abuse before a transaction settles, and to manage the disputes that get through anyway. In the revenue stack it sits in the payment path: every checkout or top-up flows through its risk score before the processor is charged, protecting both revenue and the merchant's standing with card networks.
Which of the capability map's modules Sift covers — each links to the module's own page, with every tool that supports it.
| Module | Phase | Depth | Note |
|---|---|---|---|
| Run Revenue Operations | |||
| Fraud / Payment Risk Scoring | Collect & Recover | Core | real-time ML risk scores informed by cross-network fraud signals |
| Chargeback Management | Collect & Recover | Supported | dispute workflow and evidence management for the fraud that gets through |
Sift's network model is the differentiator: signals learned from fraud observed across its whole customer base inform scores for each merchant, so you benefit from attack patterns you have not personally seen yet. It also covers abuse types beyond payments — fake accounts, content spam, promo abuse — which matters for PLG products where fraud starts upstream of the first charge.
Card testing and stolen-card signups are endemic for anything with a self-serve checkout, and usage-based products add a twist — fraudsters burn metered resources like GPU time before the chargeback arrives. Scoring signups and payments up front is much cheaper than eating the usage cost plus the dispute fee.
Processor-native tools score only what that processor sees. A dedicated platform like Sift scores events across your whole product — signups, logins, content, payments on any processor — and gives risk teams a workbench for rules and review queues. Teams typically graduate to it when fraud becomes an operational function, not just a checkout filter.
By overlap on the capability map — computed, not curated.