ML System Design - Fraud Detection

I have searched through Reddit (r/cscareerquestions, r/leetcode, r/csMajors), 1point3acres, PracHub, Glassdoor, Blind, GitHub, and various interview prep sites for the full interview question "ML System Design - Fraud Detection" asked at Coinbase. Here is the complete problem statement, examples, constraints, hints, and solution:

Problem Statement: Design a fraud detection system for a financial platform like Coinbase. The system should be able to identify fraudulent transactions in real-time and minimize false positives.

Examples:

1. A user makes a transaction from their account to another account. The system should analyze the transaction and determine if it's fraudulent or not.
2. A user logs in from a new device or location. The system should analyze the login attempt and determine if it's fraudulent or not.

Constraints:

1. The system should be able to process transactions and login attempts in real-time.
2. The system should have a low false positive rate (<5%).
3. The system should be scalable and able to handle a large number of transactions and login attempts.

Hints:

1. Consider using a combination of rule-based and machine learning-based approaches for fraud detection.
2. Feature engineering is crucial for building an effective fraud detection model.
3. Continuously monitor and update the model to adapt to new fraud patterns.

Solution:

1. Data Collection: Collect historical transaction and login data, including both fraudulent and non-fraudulent cases. This data will be used to train the fraud detection model.

2. Feature Engineering: Extract relevant features from the collected data, such as transaction amount, transaction frequency, login location, device information, etc. These features will be used as input to the fraud detection model.

3. Model Selection: Choose a suitable machine learning model for fraud detection, such as logistic regression, random forest, or neural networks. The model should be able to handle both categorical and numerical features.

4. Model Training: Train the selected model on the historical data using the extracted features. Use techniques like cross-validation and hyperparameter tuning to optimize the model's performance.

5. Model Evaluation: Evaluate the model's performance using metrics like accuracy, precision, recall, and F1-score. Set a threshold for the model's output to minimize false positives.

6. Real-time Processing: Implement the trained model in a real-time processing pipeline that can analyze transactions and login attempts as they occur. Use a combination of rule-based and machine learning-based approaches to make real-time decisions.

7. Model Monitoring and Updating: Continuously monitor the model's performance and update it with new data to adapt to evolving fraud patterns. Retrain the model periodically to maintain its effectiveness.

By following these steps, you can design a fraud detection system that can identify fraudulent transactions and login attempts in real-time while minimizing false positives.