Suspect Score: AI-powered fraud scoring trained on your own data

Fraud detection is becoming increasingly complex as attack patterns continually evolve, making it difficult for fraud teams to maintain effective defenses solely through manual configuration and intuition. Traditionally, teams have relied on a combination of device intelligence, behavioral indicators, and risk signals to identify suspicious activities, but these methods require constant manual tuning and often become outdated. The introduction of AI-powered recommendations for Suspect Score offers a solution by allowing businesses to utilize their own labeled fraud data to generate optimized signal weightings, enhancing detection accuracy while maintaining transparency and control. This approach tailors fraud scoring to the specific traffic and fraud patterns of each business, using machine learning to adapt configurations dynamically as new data is uploaded. The system provides clear recommendations and allows full visibility and control over the decision-making process, enabling businesses to respond more swiftly and confidently to emerging threats without increasing operational complexity. AI-driven Suspect Score recommendations are available to users of Smart Signals on the Fingerprint dashboard, providing a streamlined way to update and optimize fraud detection strategies.

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