🏦 Finance & Banking · Data Flow
Real-Time Fraud Detection Data Flow
How a bank scores every transaction for fraud in milliseconds: features from history, an ML model, rules, and a case queue for analysts.
Drawing diagram…
What this diagram shows
- Each transaction is scored before it is approved
- Features combine the live transaction with recent history
- Borderline cases go to analysts; their decisions retrain the model
Prompt used
Data flow for real-time fraud detection: card and UPI transactions stream through Kafka to a scoring service, which pulls features (spend velocity, device, location, merchant risk) from a feature store, runs an ML model and business rules, and returns approve, decline or step-up authentication to the payment switch. High-risk cases go to an analyst case queue; analyst decisions are labelled and feed model retraining.
Mermaid code
flowchart LR TX[Card and UPI transactions] -->|Events| K[Kafka] K --> SC[Scoring Service] FS[(Feature Store: velocity, device, location)] -->|Features| SC SC -->|Score| ML[ML Model] SC -->|Checks| RU[Rules Engine] SC -->|Approve / decline / step-up| SW[Payment Switch] SC -->|High-risk cases| CQ[Analyst Case Queue] CQ -->|Labelled decisions| LB[(Labelled Data)] LB -->|Retraining| TR[Model Training] TR -->|New model| ML K -->|History| FS
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