Fraud Prevention Real-Time, AI-Native
Stop fraud before it happens with a sub-50ms scoring engine that combines graph neural networks, behavioral analytics, and real-time feature computation — decisioning millions of events per day with surgical precision.
92%
Fraud detection rate
<50ms
Real-time scoring latency
0.3%
False positive rate
$18M+
Average annual loss prevented
How It Works
From raw data to decisions — step by step
A transparent look at our technical methodology, so your engineering and architecture teams know exactly what they're getting.
Multi-Source Signal Aggregation
We ingest transactional data, device fingerprints, behavioral biometrics, IP reputation, and third-party enrichment (e.g., LexisNexis, Socure) into a unified event stream. All signals are normalized and deduplicated at ingestion.
Real-Time Feature Computation
A feature store computes 200+ risk signals in real time — velocity checks, device history, geolocation anomalies, behavioral patterns, and network graph relationships. Features are versioned and reproducible for model retraining.
Ensemble Risk Scoring Engine
A stacked ensemble of gradient boosted trees (XGBoost, LightGBM) and a Graph Neural Network evaluates each event against learned fraud patterns. Scores are calibrated to your risk appetite with configurable thresholds.
Decision Orchestration & Case Management
Scores feed a rule-based orchestration layer that auto-approves low-risk events, challenges medium-risk ones (step-up auth, OTP), and blocks or queues high-risk events for analyst review in a purpose-built case management UI.
Continuous Model Feedback & Retraining
Analyst verdicts and confirmed fraud labels flow back into the training pipeline via an MLOps loop, retraining models weekly. Concept drift monitoring ensures model accuracy doesn't degrade as fraud patterns evolve.
Architecture
Reference Architecture
A layered view of the platform stack — from source systems to actionable outputs.
Event Sources
Streaming & Feature Layer
AI Scoring Engine
Decision & Response
Use Cases
Designed for every fraud vector
Payment Fraud
Detect card-not-present fraud, account takeovers, and synthetic identity fraud across payment rails in under 50ms per transaction.
Account Fraud
Identify new account fraud, credential stuffing, and bonus abuse at onboarding — before fraudsters gain a foothold in your platform.
Insider & Claims Fraud
Surface suspicious internal access patterns and fraudulent insurance or benefit claims using behavioral baselines and graph anomaly detection.