Design payment fraud detection

Quick Overview

This question evaluates a candidate's ability to design end-to-end machine learning systems for payment fraud detection, covering competencies in labeling, imbalanced learning, feature engineering across users/merchants/cards/devices/IPs and graph relationships, model selection, online serving, latency and feature freshness, monitoring, experimentation, and adversarial robustness. As an ML System Design problem for a Machine Learning Engineer role, it is commonly asked to probe trade-offs between fraud loss, false positives, user friction and latency and requires both conceptual understanding and practical application of production ML concerns such as evaluation metrics, thresholding, retraining, and operational constraints.

Design payment fraud detection

Company: Stripe

Role: Machine Learning Engineer

Category: ML System Design

Difficulty: medium

Interview Round: Technical Screen

Design a machine learning system for fraud detection in an online payment platform. The system should score transactions before or shortly after authorization and help decide whether to approve, block, challenge, or send a transaction to manual review. Cover the following: - Product goals and trade-offs between fraud loss, false positives, user friction, and latency. - Labels and training data construction. - Feature engineering for users, merchants, cards, devices, IPs, transaction history, and graph relationships. - Model choices and how to handle severe class imbalance and delayed fraud labels. - Online serving architecture, feature freshness, and latency constraints. - Evaluation metrics, thresholding, experimentation, monitoring, and retraining. - Abuse/adversarial considerations and how the system should adapt over time.

Overview: This question evaluates a candidate's ability to design end-to-end machine learning systems for payment fraud detection, covering competencies in labeling, imbalanced learning, feature engineering across users/merchants/cards/devices/IPs and graph relationships, model selection, online serving, latency and feature freshness, monitoring, experimentation, and adversarial robustness. As an ML System Design problem for a Machine Learning Engineer role, it is commonly asked to probe trade-offs between fraud loss, false positives, user friction and latency and requires both conceptual understanding and practical application of production ML concerns such as evaluation metrics, thresholding, retraining, and operational constraints.

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Apr 18, 2026
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Design a machine learning system for fraud detection in an online payment platform. The system should score transactions before or shortly after authorization and help decide whether to approve, block, challenge, or send a transaction to manual review.

Cover the following:

  • Product goals and trade-offs between fraud loss, false positives, user friction, and latency.
  • Labels and training data construction.
  • Feature engineering for users, merchants, cards, devices, IPs, transaction history, and graph relationships.
  • Model choices and how to handle severe class imbalance and delayed fraud labels.
  • Online serving architecture, feature freshness, and latency constraints.
  • Evaluation metrics, thresholding, experimentation, monitoring, and retraining.
  • Abuse/adversarial considerations and how the system should adapt over time.

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