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Data Pipelinescomplex complexity
Fraud Detection System
Architecture for real-time fraud detection - feature engineering, scoring, rules, and feedback loops.
Architecture
System Components
Key building blocks of this architecture, layered from infrastructure up
01
Event Capture
Capture user, device, and transaction events.
KafkaSegmentCustom SDK
02
Feature Store
Online and offline feature stores for model serving.
FeastTectonRedis
03
Scoring Service
Real-time ML scoring of incoming events.
XGBoostONNXTriton
04
Rules Engine
Deterministic rules layered on top of ML scores.
OPACustom DSLRule Sheets
05
Case Management
Queue suspicious cases for human review.
Custom UIWorkflowAudit
06
Feedback Loop
Capture analyst decisions to retrain models.
LabelsTraining PipelineDrift Detection
Planning
Key Considerations
Important factors to keep in mind when implementing this architecture
Always combine ML and rules - pure ML is too brittle for compliance
Build for explainability so analysts trust the scores
Plan for adversarial drift - fraud changes faster than your model
Options
Alternatives to Consider
Other approaches that might fit your specific needs
Sift or Forter for managed fraud platforms
Stripe Radar for Stripe-native fraud screening
Sardine for crypto and account fraud
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