Sift, an AI-powered fraud prevention platform, has introduced updates to enhance risk decision-making in response to increasing fraud attacks. These innovations include pre-built workflows and improved policy abuse detection.

Sift offers identity trust solutions for global businesses through its investments in machine learning and user identity management, with a data network evaluating one trillion events annually.

Streamlined Workflow Templates for Fraud Teams

Accessible via Sift’s user-friendly interface, pre-built workflow templates provide immediate access to industry-specific strategies tailored to combat payment fraud. These tools enhance efficiency and accuracy while reducing the operational burden on fraud teams.

Preventing Incentive Abuse with Specialized Tools

Sift’s Specialised Console Functionality for Incentive Abuse uses policy rules to identify potential loyalty, promotion, and referral fraud early in the transaction process. This approach helps businesses prevent first-party fraud and fake account creation, contributing to lower customer acquisition costs (CAC).

Enhanced Investigation and Reporting Features

The Fall ’25 release includes three key upgrades: global identity search filters for simplified case reviews; an ATO overview dashboard with actionable insights on login activities; and historical chargeback import features for improved fraud analysis and model accuracy.

Fraud teams will benefit from more intelligent, user-friendly tools designed to address complex risks swiftly and effectively.