In its latest product update, Sift introduced ThreatClusters, a pioneering data science innovation designed to enhance fraud detection.
ThreatClusters improves decision-making accuracy in fraud detection by integrating insights from industry-specific models. This approach not only refines customer-specific risk models, but also leverages the broad intelligence of global models to create industry-specific risk signals.
Fraud threats are becoming more sophisticated, and criminals are increasingly employing AI-driven techniques that go beyond traditional fraud prevention measures. Standard detection models often miss the mark by overly focusing on a single entity's data or applying general insights across different industries. In contrast, ThreatClusters effectively groups companies with similar fraud experiences into cohorts, taking into account each group's distinct risk patterns, enabling more accurate fraud detection.
Sift's proprietary technology allows clients to apply customized models specific to their own clusters while simultaneously gaining insights into emerging fraud threats from other clusters. This dual approach not only improves accuracy, but also accelerates integration and benefit realization, allowing companies to quickly adapt to new fraud patterns.
Raviv Levi, Chief Product Officer at Sift, explained: “ThreatClusters represents a major step forward in our mission to help businesses stay one step ahead of fraudsters.
“By implementing an industry-specific consortium model, we are able to provide our clients with unprecedented insight into industry-specific fraud patterns while simultaneously protecting them from fraud occurring in other industries. As a result, our clients are able to better assess risk, protect their revenue, and grow without fear.”
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