Features
Core Fraud Detection
Transaction Monitoring &
Fraud
Detection
Uses real-time fraud monitoring across web, mobile, POS, and API channels to evaluate each transaction.
Detects threats before authorization using risk-based fraud scoring and contextual signals. Supports compliance while ensuring low friction for genuine users.
AI/ML Pattern Recognition
Applies machine learning fraud detection to catch evolving threats beyond standard rule engines.
Learns continuously from past outcomes to refine predictive fraud analytics. Reduces false positives and increases detection accuracy.
Unsupervised Anomaly
Detection
Uses behavior-based anomaly detection to identify unusual patterns without labeled data.
Reveals hidden risks like mule networks, spending bursts, or sudden deviations. Helps detect new tactics early with self-learning fraud models.
Behavioral Biometrics (typing, mouse, swipe, gestures)
Employs behavioral biometric analysis of typing, mouse flow, and swipe patterns to separate humans from bots.
Flags changes in cadence or gestures linked to account takeover attempts. Provides invisible, frictionless user protection.
Device Fingerprinting & Device Reputation
Uses device fingerprinting technology to create persistent IDs and assess device trustworthiness.
Identifies risky, rooted, or emulated devices tied to fraud. Enables cross-device fraud detection to expose coordinated rings.
Velocity Checks &
Risk Scoring
Uses velocity-based fraud checks to catch abnormal transaction frequency or activity spikes.
Detects fast retries, login bursts, or structuring attempts. Generates real-time transaction risk scores for automated approvals and declines.