💹 Fintech & Capital Markets · Data Flow
Payment Fraud Screening
How every payment is screened for fraud in real time using device data, velocity counters, rules and a model, with doubtful cases sent for review.
Drawing diagram…
What this diagram shows
- Velocity counters catch many small rapid payments
- A rules engine and a model both give a score
- Borderline payments go to a manual review queue
Prompt used
Data flow for real-time fraud screening: a payment request with device fingerprint goes to the fraud service. It reads velocity counters (payments per card per hour) from Redis and the customer's history, runs a rules engine and a machine learning model, and combines the scores. Low risk is approved, high risk declined, and medium risk sent to a review queue for analysts. Decisions feed back as labels for model training.
Mermaid code
flowchart LR P[Payment Request] -->|Amount, card, device| FS[Fraud Service] DV[(Device Fingerprints)] -->|Known devices| FS VC[(Velocity Counters - Redis)] -->|Payments per hour| FS CH[(Customer History)] -->|Past behaviour| FS FS -->|Features| RE[Rules Engine] FS -->|Features| ML[Fraud Model] RE -->|Rule score| DEC[Decision] ML -->|Model score| DEC DEC -->|Low risk| OK[Approve] DEC -->|High risk| NO[Decline] DEC -->|Medium risk| RQ[Review Queue] RQ -->|Analyst verdict| LBL[(Training Labels)]
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