Fraud & risk analytics · Sift Science, Inc.

Sift

ML fraud-scoring platform that leans on a cross-customer consortium network plus device and behavioral signals.

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Sift is a real-time fraud decisioning platform covering payment fraud, account takeover and account abuse across the customer journey. Its core signal is consortium data: events from its full customer base (billions of devices and identities) feed proprietary machine learning models that score a user or transaction, supplemented by device fingerprinting and behavioral monitoring across signup, login and checkout. Decisions are exposed as a risk score with 'clearbox' explanations of which signals drove it, plus a rules builder and automation workflows so teams can act without waiting on a data science team. It is delivered as a cloud API/dashboard with an included console for fraud analysts. Compared with Sardine or SEON, Sift's differentiator is the scale and identity-centric nature of its shared network rather than behavioral biometrics or digital-footprint lookups.

At a glance

Vendor Sift Science, Inc.
Pricing model Quote only
Free tier —
Deployment Cloud
Open source No
Best for Digital businesses that want fraud scoring backed by a large shared consortium network rather than building models from their own data alone.

Pricing

Usage-based enterprise pricing scaled to event/transaction volume; rates are negotiated per account and not published.

Pricing has not been verified yet — see the vendor's site.

Features

  • Device fingerprinting and identification
  • Behavioral monitoring across signup, login and transaction events
  • Cross-customer consortium data network
  • Proprietary ML risk scoring with explainable signal breakdowns
  • Customizable rules engine and automated decision workflows
  • Analyst case queue and investigation workflows
  • Chargeback and account-abuse protection
  • Real-time scoring API

Profile last reviewed September 21, 2026

Head to head

Alternatives

Sardine Fraud and AML platform leaning on behavioral biometrics, device intelligence and payment/on-chain risk signals. SEON Fraud prevention and AML platform built around digital footprint analysis and device fingerprinting rather than ML alone. Ravelin ML fraud-scoring platform for e-commerce that leans on per-merchant custom models plus graph/link analysis of fraud rings. Unit21 No-code rules and case-management platform that lets fraud/AML ops teams build detection logic and investigate without engineers.

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Terms to know

Anomaly detection Identifying data points or patterns that deviate significantly from expected or normal behavior. Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data quality The degree to which data is accurate, complete, consistent and fit for the purpose it is being used for. Browser fingerprinting Identifying a device from browser and device characteristics, such as screen size or fonts, without setting a cookie. Risk scoring Assigning a numeric score to a customer, transaction, or entity that estimates the likelihood of a specific negative outcome.

Related guides

How to choose a fraud detection tool Fraud platforms split by primary signal — biometrics, device data, consortium networks, compliance case management — and by who they're built for.