Features
Core Fraud Detection
Transaction Monitoring &
Fraud Detection
Continuously evaluates every transaction in real time across web, mobile, POS, and API channels.
Detects high-risk activity before authorization using rules, risk scores, and contextual signals. Ensures compliance while minimizing friction for legitimate customers.
AI/ML Pattern Recognition
Leverages supervised machine learning models to identify evolving fraud patterns beyond rule-based detection.
Continuously adapts to new behaviors by learning from confirmed fraud and legitimate activity. Reduces false positives and enhances detection accuracy.
Unsupervised Anomaly
Detection
Identifies unusual transactions or behaviors without requiring labeled data.
Flags hidden risks like mule account networks, structuring attempts, or sudden spending deviations. Helps organizations catch emerging fraud tactics early with self-learning models.
Behavioral Biometrics (typing, mouse, swipe, gestures)
Analyzes how users type, click, or swipe to differentiate between genuine customers and bots.
Detects anomalies in typing cadence, mouse trajectories, or swipe patterns that signal automation or account takeover. Adds an invisible, frictionless security layer.
Device Fingerprinting & Device Reputation
Generates unique device IDs from multiple parameters to track consistency and reputation.
Flags devices linked to known fraud, rooted/jailbroken phones, or emulators. Enables cross-account correlation to uncover fraud rings reusing the same device.
Velocity Checks &
Risk Scoring
Applies configurable thresholds to detect abnormal frequency or value of transactions.
Identifies structuring attempts, rapid-fire logins, or excessive retries. Assigns real-time risk scores that drive decisioning—approve, hold, or decline.
