Your question is Real-Time Fraud Graph Processing. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you architecture a real-time graph neural network system to detect fraud across millions of daily streaming transactions?
Explain the end-to-end design, including streaming graph construction, feature freshness, GNN training and inference, online versus batch processing, evaluation, monitoring, and failure handling. Address how you would balance detection quality, inference latency, cost, explainability, and false-positive risk.