Information is key to fighting fraud with machine learning. Supplied from numerous sources, Sift continues to provide actionable data from the signals available and continues to open up new avenues.
In the Summer 2020 Release, we’re launching some critical enhancements to our fraud-fighting tools. The included enhancements in this release simplify ingesting data, sharing knowledge with team members, and notifying customers when something suspicious is going on with their account.
Additional features for thorough payment fraud detection
Payment fraud is a constant challenge for companies across verticals and operating models. Sift has enhanced its excellent Payment Protection product with some additional functionality that makes our customers even more effective at fighting multiple vectors of fraud.
Going live at the end of June, customers can fight payment fraud and card testing without negatively affecting growth. We are standardizing return codes for improved pattern recognition and detection, as well as adding new counting features for superior anomaly detection, allowing you to elevate risk without blocking good orders.
Transfer knowledge effectively with case comments
Case comments are an upgrade to the existing notes functionality within the Sift Console that improves team communication during investigations and increases transparency and accountability into why decisions were made.
Trust and Safety analysts, managers, customer support reps, and others can leave detailed notes when doing a review and view a full history whenever an audit is needed.
Quickly configure Verification emails
Triggering a Verification Email with Sift is now easier to manage and configure. The addition of a new Verification editor allows you to manage and draft the verification with automatic visualization and real-time testing—no coding needed.
Stripe connector for automatic ingestion of dispute data
For merchants using Stripe as their payment service provider, our connector enables Sift to ingest chargebacks and disputes in just a few clicks. Chargeback data flowing freely improves the quality of data in Sift—subsequently improving score accuracy and giving analysts visibility to make more informed decisions.
