Fraud Detection

Design a machine learning system to detect fraudulent transactions for a payments platform, covering both the ML pipeline and the surrounding production architecture. Problem Statement Design a machine learning system to detect fraudulent transactions for a payments platform. ML System Design Data Processing: How would you collect, clean, and preprocess transaction data? Model Design: Which models are suitable for fraud detection? How do you handle class imbalance? Evaluation Metrics: What metrics would you use to evaluate model performance? Deployment: How would you deploy the model to production? Post-Deployment: What issues might arise after deployment and how would you address them? System Architecture Beyond the ML components, expect questions on traditional system design: Scalability: How would you make the system scalable? Reliability: How would you ensure system reliability? Throughput: How would you handle high transaction volumes? System Diagram: Be prepared to draw the entire system architecture showing different components including databases, servers, and how they interact.