Real-time Payment Fraud Detection System

Design a high-scale, low-latency payment risk scoring system for a global fintech platform. The system must process 10k+ QPS with a P99 latency under 50ms. Address the end-to-end ML lifecycle: specifically, how you handle real-time feature engineering (velocity features), extreme class imbalance, and the inherent 30-90 day label delay (delayed feedback loop). Your design should include robust data and monitoring pipelines to detect adversarial drift and ensure system reliability via fallback mechanisms.

LightGBMKafkaFlinkRedisSparkONNXTreeliteProtobufS3Prometheus

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Real-Time Online Fraud Detection System

Design a high-scale, real-time fraud detection system for a global payment processor. The system must handle 10k+ QPS with sub-100ms P99 latency while minimizing both financial loss and false positives for legitimate users. Address the challenges of extreme class imbalance (0.1% fraud), significant label delay (30-90 days for chargebacks), and the need for near real-time feature updates (velocity signals). Explain your choice of model architecture, feature engineering strategy (online/offline consistency), and how you would evaluate and monitor the system in an adversarial environment where fraud patterns evolve rapidly.

LightGBMXGBoostKafkaFlinkRedisSparkONNXSHAPFeast

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Real-time Payment Fraud Detection System

LightGBMKafkaFlinkRedisSparkONNXTreeliteProtobufS3Prometheus

Read

Real-Time Online Fraud Detection System

LightGBMXGBoostKafkaFlinkRedisSparkONNXSHAPFeast

Read