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Address Fraud Detection with Imbalance and Concept Drift Solutions

NetflixData Scientist · Onsite
MediumData ScientistOnsiteMachine Learning

Last updated

Evaluates end-to-end fraud detection ML system design for online payments under drift and imbalance. Strong answers handle delayed labels, class imbalance, feature engineering, cost-sensitive validation, real-time deployment, threshold actions, monitoring, retraining, and concept drift.

Scenario

You are tasked with building a fraud-detection model for an online payments product.

Question

Outline the end-to-end ML workflow: data collection, feature engineering, model selection, validation, deployment, and monitoring. How would you handle severe class imbalance and concept drift in this context?

Hints

Discuss resampling, cost-sensitive learning, ROC-AUC, sliding windows, and automated retraining triggers.