Real-Time Fraud Detection Systems
Real-Time Fraud Detection Systems score and decide on incoming
payment, authentication, or account actions within milliseconds (typically <100ms).
Unlike batch scoring systems, real-time architectures block fraudulent activity
in-band before authorization finishes.
Key Operational Components
1. Ingestion & Event Streaming Layer
- Technologies: Apache Kafka, AWS Kinesis,
Redpanda.
- Function: Ingests high-throughput
transaction payloads, geolocation telemetry, device fingerprints, and user
interaction logs concurrently.
- Latency Budget: ~5–10 ms.
2. Stateful Stream Processing
- Technologies: Apache Flink, Kafka Streams.
- Function: Maintains dynamic behavioral
windows over streaming events. Calculates sliding velocity indicators
(e.g., number of card swipes in 5 minutes, distance between
consecutive transactions).
- Latency Budget: ~10–20 ms.
3. Ultra-Low Latency Feature Store
- Technologies: Feast, Tecton, Redis,
Aerospike.
- Function: Serves online feature vectors
(e.g., historical user averages, IP risk ratings, device ID counts) to
model servers with single-digit millisecond lookup times.
- Latency Budget: ~5 ms.
4. Hybrid Machine Learning & Inference Engine
- Models: LightGBM/XGBoost (tabular
transaction attributes), Graph Neural Networks (GNNs for money mule/fraud
ring entity relations), and Isolation Forests (anomaly detection).
- Serving: Triton Inference Server,
TorchServe, ONNX Runtime.
- Latency Budget: ~20–40 ms.
5. Rules & Policy Decision Engine
- Technologies: Drools, Camunda, custom
expression evaluators.
Function: Combines ML probability scores with hard corporate business rules (e.g., Instant block if KYC unverified or Require MFA if risk score > 0.85).