Design a Fraud Detection System
Company: Plaid
Role: Machine Learning Engineer
Category: ML System Design
Difficulty: medium
Interview Round: Technical Screen
Design a machine learning system for a fintech platform such as Plaid to detect fraudulent activity involving bank account linking, identity verification, or suspicious financial behavior.
The system should help identify high-risk users, connections, or transactions in near real time while minimizing false positives that could block legitimate users.
Address the following:
- What fraud scenarios would you target?
- What data and labels would you use?
- What features would you build?
- What model or modeling approach would you choose?
- How would the online serving architecture work?
- How would you evaluate the model offline and online?
- How would you handle delayed labels, class imbalance, adversarial behavior, privacy, and monitoring?
Quick Answer: This question evaluates skills in end-to-end machine learning system design for fraud detection, including competencies in data selection and labeling, feature engineering, model selection, near-real-time serving, evaluation, and operational concerns such as class imbalance, delayed labels, adversarial robustness, privacy, and monitoring.