Sift’s Fall ’25 Release: Advanced Fraud Investigation Tooling to Simplify Fraud Management & Strengthen Identity Trust
By Lauren Towner · 20 August 2025

Sift, the AI-powered fraud platform delivering identity trust, announced innovations to simplify risk decisioning as fraud attacks escalate, adding pre-built workflows, refined policy abuse detection, and expanded investigation tools.
These enhancements enable Sift customers to deploy fraud protection faster, analyze risk more efficiently, and accelerate time-to-value while delivering standout consumer experiences.
Pre-Built Workflow Templates Reduce Operational Pressure
Delivered through Sift’s award-winning user interface, Pre-Built Workflow Templates provide fraud teams with immediate access to industry-specific templates for preventing payment fraud. Expert-level fraud strategies improve time-to-value and reduce pressure on fraud teams, allowing for greater efficiency and accuracy at scale.
Incentive Abuse Tools Target Loyalty, Promo, and Referral Fraud
By leveraging policy rules to detect loyalty, promo, and referral abuse before a transaction occurs and revenue is at risk, Specialized Console Functionality for Incentive Abuse allows businesses to block first-party fraud and fake account creation while helping to lower customer acquisition cost (CAC).
Expanded Investigation & Reporting
Three additional upgrades give fraud teams deeper context and clearer visibility into high-risk activity. This set of data-rich, precision-focused enhancements equip fraud teams with deeper insights and faster investigation tools:
- Global Identity Search Filters – Reveal linked users with advanced search and filtering across Sift’s Global Data network, streamline case reviews, and prioritize high-risk accounts.
- ATO Overview Dashboard – Track account takeover trends with new widgets and drilldown reports, including actionable Logins by 2FA and Logins by Notifications reports.
- Historical Chargeback Import – Import and review historical chargeback data via CSV to view past fraud activity, improve model accuracy over time, and leverage AI decisioning faster than ever before.
Companies in this story: Sift
People in this story: Raviv Levi