Design an ML pipeline for fraud detection
Last updated: November 25, 2025
Quick Overview
Design an end-to-end ML system for fraud detection, covering data collection, feature engineering, model selection, training, and serving.
Salesforce
November 25, 202510
7
3,669 solved
Design an end-to-end ML system for fraud detection, covering data collection, feature engineering, model selection, training, and serving.
Salesforce asks this during the Phone Screen to assess your depth in ML. They expect you to discuss the mathematical foundations, practical considerations, and common pitfalls when applying these techniques in production.
What the Interviewer Expects
- Explain the concept clearly with intuitive examples
- Discuss when and why to use this technique
- Identify common pitfalls and how to avoid them
- Compare with alternative approaches at a high level
Key Topics to Cover
How to Approach This
- Understand the bias-variance trade-off. High training accuracy but low test accuracy signals overfitting.
- Choose evaluation metrics carefully based on the problem. Accuracy alone is often insufficient.
- Feature engineering is often more impactful than model selection.
- Know when to use tree-based models (tabular data) vs neural networks (unstructured data).
- Handle class imbalance with SMOTE, class weights, or appropriate loss functions.
Possible Follow-up Questions
- What regularization technique would you use and why?
- How would you handle a highly imbalanced dataset?
- When would you prefer a simpler model over a complex one?
- How would you explain this model's predictions to a non-technical stakeholder?
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Explore ML Interview PrepSample Answer
Core Concept: Supervised Learning for Fraud Detection
Fraud detection typically employs supervised learning where we train a model using labeled datasets that contain both fraudulent and non-fraudulent transactions. The core idea is to learn a mapping fr...
How It Works: Mathematical Foundations
In supervised learning, we use a loss function to quantify the difference between predicted and actual labels. For example, in logistic regression, we often use cross-entropy loss, which is defined as...
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