Paygilant is a mobile payment fraud detection platform that uses six proprietary intelligence sets to assess transaction risk before a transaction is completed. The platform operates by analyzing multiple data signals collected from the mobile device and user behavior: **Device Attributes & Device DNA:** Observes device-level attributes including device model, screen, memory, UUID, OS, IP address, geolocation, and app permissions to derive a device fingerprint and detect anomalous behavior. **Transaction Data:** Uses proprietary behavioral maps built with machine learning algorithms to model each user's purchasing patterns. These maps are user-specific, lightweight (using a depth-of-field compression approach), and dynamic — updating as behavior changes. They are securely transmitted to the mobile device for on-device risk calculation. **Bio Markers:** Passively captures biometric signals such as touch time, time between touches, touch input size, finger velocity, scrolling pace, drag length, and typing biometrics to identify the legitimate user behind a transaction. **App Interactions:** Monitors how users navigate within the mobile application, flagging anomalies such as direct navigation to high-ticket items without typical browsing behavior, or unusual data entry patterns on payment forms. **User Data Analysis:** Analyzes user data on the device (e.g., account identity, contacts, call logs, media) in a privacy-preserving manner to surface fraud indicators, particularly in new account origination scenarios where no behavioral history exists.