Locating Criminal Activity: Using geolocation technology and multi-layered, automated systems that leverage machine learning and scoring models to combat fraud based on confirmed fraudulent accounts to identify physical hotspots where fraudsters frequently load stolen credit cards into mobile wallets. Geofencing: When an event occurs within a geofenced area, the use of service tags with associated attributes automates blocking of user accounts, tokens, cards, and devices to harness and proactively prevent fraud across 53% of the convenience retail and fuels marketing industry. Using AI and Continuous Learning Models: Automation and direct integration with acquirers to pull chargeback data, blocking fraudsters and continuously updating fraud models, and ensuring constant adaptation to new fraud patterns based on real-time fraud data, dynamically adapting to evolving payment methods, behaviors, patterns, and locations to detect fraud rings. Supporting Law Enforcement: Sharing insights and data with local law enforcement task forces to stop fraudsters and extend fraud prevention efforts with situational and geolocation data.
