Fraud Detection — Simple

Problem statement (interviewer prompt)

Design a real-time fraud detection system that scores every payment / login / signup in <100ms. Combine rules + ML + graph signals, ingest a near-real-time feature store, and feed labels (chargebacks, complaints) back into retraining.

flowchart LR
  E[Event]
  FE[Feature Store]
  ML([Model Server])
  RULE[Rule Engine]
  DEC[Decision]
  REV[[Review Queue]]
  E --> FE --> ML --> DEC
  E --> RULE --> DEC
  DEC --> REV

    classDef client fill:#dbeafe,stroke:#1e40af,stroke-width:1px,color:#0f172a;
    classDef edge fill:#cffafe,stroke:#0e7490,stroke-width:1px,color:#0f172a;
    classDef service fill:#fef3c7,stroke:#92400e,stroke-width:1px,color:#0f172a;
    classDef datastore fill:#fee2e2,stroke:#991b1b,stroke-width:1px,color:#0f172a;
    classDef cache fill:#fed7aa,stroke:#9a3412,stroke-width:1px,color:#0f172a;
    classDef queue fill:#ede9fe,stroke:#5b21b6,stroke-width:1px,color:#0f172a;
    classDef compute fill:#d1fae5,stroke:#065f46,stroke-width:1px,color:#0f172a;
    classDef storage fill:#e5e7eb,stroke:#374151,stroke-width:1px,color:#0f172a;
    classDef external fill:#fce7f3,stroke:#9d174d,stroke-width:1px,color:#0f172a;
    classDef obs fill:#f3e8ff,stroke:#6b21a8,stroke-width:1px,color:#0f172a;
    class E,RULE,DEC service;
    class FE datastore;
    class REV queue;
    class ML compute;