What Is Fraud & Risk Analytics?
Fraud & Risk Analytics scores transactions using a machine learning model trained on your own historical transaction data, including known fraud cases, and validated against held-out data before deployment so its real-world accuracy is understood upfront rather than assumed. Device fingerprinting adds a second signal — flagging patterns like the same device transacting across many unrelated accounts — without requiring invasive device permissions. Flagged transactions route into a case management queue with the model's confidence score and contributing factors shown, so risk teams can make faster, better-informed decisions instead of investigating blind.
What's Inside Fraud & Risk Analytics
ML-Based Fraud Scoring
A model trained on your own transaction history, validated on held-out data before it ever scores live traffic.
Device Fingerprinting
Flags suspicious patterns like one device transacting across many accounts, without invasive permissions.
Case Management Queue
Flagged transactions route to risk teams with confidence scores and contributing factors attached.
Model Drift Monitoring
Tracks model accuracy over time so degrading performance gets caught, not discovered after losses mount.
ML Fraud Scoring vs. Static Rule-Based Checks
Common Questions About Fraud & Risk Analytics.
Other FinTech Products.
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