Fraud Detection System

Design a machine learning system to detect fraudulent transactions on a payments platform. The round blends ML system design with traditional system design, with a heavy emphasis on the end-to-end architecture and how it scales. Task Overview Design a machine learning system that finds fraudulent transactions on a payment platform. Main Focus Areas This problem mixes two types of design: ML System Design and Traditional System Design. You must be ready to discuss the Machine Learning parts. However, you also need to explain the infrastructure (servers and databases) needed to run fraud detection at a large scale. What to Expect In the interview, you will likely discuss both ML topics and how the system is built: Machine Learning Topics Data Handling: How do you gather, clean, and prepare transaction data? Model Choice: Which models work best for catching fraud? How do you handle class imbalance (when there are very few fraud cases compared to normal ones)? Measuring Success: What metrics will you use to check if the model is working well? Going Live: How do you move the model into production? Maintenance: What problems might happen after the model is live, and how do you fix them? Infrastructure and Setup Besides the ML parts, expect questions about the system setup: Scalability: How will the system handle growth? Reliability: How do you make sure the system stays online and does not crash? Throughput: How do you handle a massive number of transactions at the same time? Diagrams: Be ready to draw the full system architecture. This includes databases, servers, and how they talk to each other. Notes The interview heavily focuses on system architecture: expect to draw a full diagram showing the components, such as databases and servers, and to field key questions about Scalability, Reliability, and Throughput. The ML half can be approached with standard preparation: data processing, model choice, evaluation metrics, and deployment. Role context and interviewer style This has surfaced for the Machine Learning Engineer loop, where the distinguishing feature is the blend of ML design with traditional system-design concepts. The interviewer tends to be hands-on and guiding through the architecture portion — candidates who were not strong in traditional system design were still able to work through it with the interviewer's prompts. Treat the architecture diagram as a collaborative whiteboard exercise rather than a solo monologue: state assumptions, sketch the components, and let the interviewer steer depth. In the Staff loop this design round is paired with an ML coding round built on two regression tasks (see the ML coding inventory entry).