Real-time AI risk scoring that adapts to sophisticated threats while maintaining seamless customer experiences. Reduce fraud by 60% without creating friction that erodes trust.
Dynamic machine learning models analyze behavioral patterns, transaction context, device fingerprinting, and network effects in real-time while maintaining strict privacy compliance.
Tiered authentication applies appropriate security based on risk levels. Low-risk transactions pass instantly. Medium-risk triggers step-up auth. High-risk requires enhanced verification—all without frustrating legitimate customers.
Cross-industry fraud intelligence sharing with privacy-preserving cryptography. Federated learning enables collective defense against sophisticated fraud rings while maintaining competitive boundaries and regulatory compliance.
Clear security communication turns friction into trust. Real-time explanations of security decisions, rapid dispute resolution pathways, and customer education that builds confidence in your security measures.
AI models continuously adapt through analyst feedback, false positive analysis, and successful fraud pattern incorporation. Stay ahead of evolving attack vectors with automated model retraining every 6 hours.
Test real-time fraud detection with behavioral analysis
End-to-end system addressing security, privacy, customer experience, and continuous adaptation
End-to-end fraud detection system with AI model specifications, real-time data pipelines, and seamless integration points across your entire transaction ecosystem.
Comprehensive data governance with encryption, anonymization, and full regulatory compliance across GDPR, CCPA, and emerging privacy regulations worldwide.
Detailed user flow diagrams showing customer experiences across different risk scenarios with appropriate authentication requirements at each level.
Strategy for cross-industry fraud intelligence sharing with clear governance, legal frameworks, and participation incentives that benefit all parties.
Communication strategies that build security awareness and trust while reducing support burden through proactive education and transparency.
Feedback mechanisms where AI models continuously adapt to emerging threats, incorporating analyst insights and successful fraud patterns automatically.
XGBoost ensemble with behavioral biometrics & network graph analysis
XGBoost with 1000 trees processes 150+ features in under 50ms. Behavioral patterns, device fingerprints, transaction context, velocity checks, and network graph analysis for comprehensive risk assessment.
PCA-transformed features maintain GDPR compliance. Differential privacy in model training. Homomorphic encryption for cross-institution intelligence sharing. Zero raw PII storage.
Dynamic risk thresholds adjust based on user history, device trust, location patterns, and time-of-day risk profiles. Seamless for legitimate users, strict for anomalies.
# POST /score - Returns comprehensive risk assessment curl -X POST https://api.kyren.ai/score \ -H "Authorization: Bearer YOUR_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "transaction_id": "txn_12345", "amount": 149.62, "user_id": "user_abc", "device_fingerprint": "fp_xyz", "behavioral_data": { "typing_pattern": [...], "mouse_dynamics": [...] } }' # Response (47ms) { "risk_score": 12.4, "risk_level": "low", "decision": "approve", "friction_required": "none", "risk_factors": { "device_trust": 0.95, "behavioral_match": 0.92, "velocity_check": "pass", "network_reputation": 0.88 }, "explanation": "Known device, normal behavior" }
Interactive technical demo with real-time risk scoring, behavioral analysis, and adaptive authentication flows
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