In its most recent product update, Sift has introduced ThreatClusters, a pioneering data science innovation designed to enhance fraud detection.
ThreatClusters improves decision-making accuracy in fraud detection by integrating industry-specific model insights. This approach not only refines customer-specific risk models but also leverages the extensive intelligence of a global model, crafting unique risk signals for each industry.
Fraud threats are becoming more sophisticated, with perpetrators increasingly employing AI-driven techniques that surpass traditional fraud prevention measures. Standard detection models often miss the mark by either overly focusing on data from a single entity or applying generic insights across varied industries. In response, ThreatClusters effectively groups companies with similar fraud experiences into cohorts, considering the distinct risk patterns of each group, thereby enabling more precise fraud detection.
Sift’s proprietary technology allows clients to apply a model specifically tailored to their cluster, while simultaneously benefiting from insights into emerging fraud threats from other clusters. This dual approach not only heightens accuracy but also speeds up the integration and realization of benefits, helping businesses rapidly adapt to new fraud patterns.
Raviv Levi, Sift’s Chief Product Officer, explained. “ThreatClusters represents a significant leap forward in our mission to help businesses stay ahead of fraudsters.
“By introducing industry-specific consortium models, we can provide our customers with unprecedented insights into the fraud patterns that are unique to their industry while protecting against emerging ones from other industries. As a result, our customers are better able to assess risk, protect revenue, and grow fearlessly.”
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