Design Real-Time Fraud Detection with XGBoost Model

Q: Design Real-Time Fraud Detection with XGBoost Model

This question evaluates competency in designing and operationalizing latency-sensitive machine learning systems, covering real-time feature engineering, training with delayed labels, class imbalance strategies, metric selection, deployment architecture, and post-deployment monitoring.

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Question

Real-Time Fraud Detection with XGBoost (Subscription Payments)

Scenario

You need to build and operate a real-time system that flags potentially fraudulent subscription-payment transactions with sub-second latency. Historical labels come from chargebacks/refunds with a delay of weeks. Data includes transaction attributes, user/account metadata, device/network signals, and historical behavior.

Task

Outline the end-to-end approach, covering:

End-to-end workflow

Data ingestion, labeling, feature engineering (batch + streaming), training/validation protocol, hyperparameter tuning, offline–online feature parity, deployment architecture, and a feedback loop.

Evaluation metrics

Which metrics you would prioritize in an imbalanced, high-stakes setting and why.

Handling severe class imbalance

Approaches such as class weighting, sampling, threshold tuning, and any loss/metric choices.

Monitoring for model drift post-deployment

Describe one concrete strategy to detect and respond to drift.

Design Real-Time Fraud Detection with XGBoost Model

Real-Time Fraud Detection with XGBoost (Subscription Payments)

Scenario

Task

Solution

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