This is basically the canonical ML system design question for fintech, so I'd prepped for it, but there's still a lot of surface area to cover.
Start by clarifying the business context and requirements, such as fraud types, data availability, and latency constraints. Then outline an end-to-end ML pipeline covering data ingestion, feature engineering, model training, evaluation, and deployment with monitoring. Emphasize trade-offs between precision and recall, real-time vs batch processing, and explainability for compliance.
Pro tip: Highlight the importance of handling class imbalance and concept drift, and propose a feedback loop where human reviewers label uncertain cases to continuously improve the model. Mention that fraud detection often requires a combination of rule-based and ML models for robustness.
Ask questions to understand the scale, latency needs, data sources, and regulatory constraints. Define success metrics like precision, recall, or cost savings.
Identify relevant data sources (transactions, user behavior, device info) and design features that capture fraud patterns. Address data quality, missing values, and temporal aspects.
Choose models suitable for imbalanced data (e.g., gradient boosting, neural networks) and consider ensemble methods. Use techniques like SMOTE, class weighting, and cross-validation with time-based splits.
Select appropriate metrics (e.g., AUC-ROC, precision-recall curve) and simulate real-world performance. Consider business impact and threshold tuning.
Design a scalable serving architecture (real-time or batch) with monitoring for drift, performance, and anomalies. Implement a feedback loop for continuous learning.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.