Fraud Detection System

The Fraud Detection System interview evaluates your ability to design and implement machine learning systems that detect fraudulent activity in transactional data. This mock interview mirrors real-world financial and e-commerce scenarios, testing your skills in SQL, exploratory data analysis (EDA), feature engineering, and model development.

You’ll work with realistic datasets to uncover hidden fraud patterns, design data pipelines, and build models that distinguish legitimate behavior from anomalies. The interview emphasizes how to handle imbalanced datasets, data drift, and model evaluation using metrics like precision, recall, and AUC-ROC. It also challenges you to think critically about latency, scalability, and ethical implications when deploying fraud detection systems in production.

By completing this interview, you’ll strengthen your understanding of end-to-end ML workflows, from data collection to deployment, and gain confidence for data science and machine learning interviews focused on fraud detection and anomaly analysis.

Goals of the Interview:

1. Define objectives and business impact of fraud detection.
2. Extract and clean transactional data using SQL.
3. Perform EDA to uncover suspicious patterns.
4. Build and evaluate ML models for fraud prediction.
5. Deploy and monitor a production-ready detection system.