Design a fraud detection system

Scenario

You are designing an end-to-end fraud detection system for an online platform (e.g., e-commerce marketplace, payments, account signup, or ad traffic). The system should detect and prevent fraudulent activity while minimizing impact on legitimate users.

Requirements

1. Goal : Predict whether an event (transaction / login / signup / ad click) is fraudulent and decide what action to take.
2. Latency : Support near-real-time decisioning (e.g., sub-second to a few seconds) for high-risk actions.
3. Cold start : Handle new users / new devices / new merchants with little or no historical data.
4. Imbalanced data : Fraud rate is low (e.g., <1%), so the dataset is highly class-imbalanced .
5. Actions : Decide between actions such as allow , step-up verification (2FA / OTP) , manual review , or block .
6. Learning loop : Incorporate delayed labels (chargebacks, user reports, investigation outcomes) and retrain/refresh models.

What to cover

• Data sources and feature engineering (real-time + batch)
• Model choice(s) and how you handle cold start + imbalance
• Evaluation metrics and offline/online validation
• System architecture for training, serving, monitoring
• Abuse/adversarial considerations and how you prevent model exploitation