- Access Control
- Authentication
- Authorization
- Cybersecurity
- Enterprise
- Machine Learning
- Orchestration
- Processors
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Who is Riskified?
Riskified is an enterprise fraud management and chargeback guarantee platform (fraud prevention / payments risk) for eCommerce merchants.
- Machine learning–based fraud detection for online transactions (fraud prevention).
- Chargeback guarantee services that assume financial liability for approved transactions (payments risk management).
- Account protection and policy abuse detection, including account takeover and promotion abuse (account security / abuse prevention).
- Identity-centric analysis using behavioral and device data to assess transaction legitimacy (identity risk analytics).
- Integration with eCommerce platforms and payment flows via APIs and partner connectors (payments / eCommerce integration).
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More About Riskified
Riskified provides fraud management and chargeback guarantee services to online merchants, with a focus on large and enterprise-scale eCommerce environments. Its core platform (fraud prevention / payments risk) evaluates card-not-present transactions in real time, using Machine Learning (ML) models trained on historical transaction, behavioral, and device-level signals. Merchants route checkout traffic to Riskified via APIs or platform connectors, receive approve/decline decisions, and can offload financial liability for chargebacks on transactions that Riskified approves under its guarantee model.
The company positions its technology as a layer within the digital payments and order orchestration stack, sitting between the merchant’s eCommerce front end and payment processors or gateways. Typical architectures use RESTful APIs, webhooks, and SDKs to pass order, device, and user-context data, and to receive risk decisions that can be applied before authorization. This design is used to support omnichannel commerce flows such as web, mobile web, and mobile apps, and to integrate with order management and customer service systems for post-transaction workflows.
Riskified’s ML models (risk analytics) assess signals such as historical customer behavior, past transaction performance, device characteristics, and network indicators. The identity-centric approach focuses on building a view of the underlying consumer across merchants, rather than relying only on static payment or device identifiers. This method is intended to distinguish legitimate returning customers from fraud patterns and to increase approval rates on high-value or cross-border orders that might otherwise be declined.
In addition to transaction fraud screening, Riskified offers capabilities for account protection and abuse prevention (account security / fraud abuse). These cover scenarios such as account takeover, credential-stuffing outcomes, and misuse of policies like coupons, promotions, and return programs. By analyzing login behaviors, session data, and order activity, the platform can flag suspicious accounts or actions for step-up verification or blocking, complementing traditional authentication or access-control tools.
From a marketplace and taxonomy perspective, Riskified fits into categories such as fraud detection and prevention, chargeback management, identity risk analytics, and eCommerce payments optimization. Enterprise users include payments, risk, and operations teams that need to balance fraud losses, chargeback operational costs, and authorization approval rates. The platform’s guarantee model changes fraud costs from variable loss to a contracted fee structure, and its analytics outputs can also inform upstream business rules, customer experience design, and payment routing strategies within the broader payments technology stack.
Our description of Riskified. Updated December 2025.
