Explain unsupervised fraud and evaluation

Q: Explain unsupervised fraud and evaluation

This question evaluates a data scientist's competency in unsupervised anomaly detection methods, evaluation under label scarcity, and operational decision-making for fraud detection on highly imbalanced data.

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Question

Unsupervised Fraud Detection: Methods, When to Use Them, and How to Evaluate Without Reliable Labels

Context

You are designing fraud detection for a large payments platform. Fraud is rare and evolving, labels (e.g., chargebacks) are delayed or incomplete, and you have a limited manual review budget. You need to:

Explain when you would use unsupervised approaches versus supervised methods.
Compare common unsupervised options: clustering, density estimation, Isolation Forests, autoencoders, and graph-based anomaly detection.
Describe how to evaluate models without reliable labels, including:
- Precision@k, recall at a fixed review budget, PR-AUC vs ROC-AUC under extreme imbalance, and other rank-based metrics.
- Using proxy/delayed labels and calibration checks.
Clarify why raw accuracy is misleading for this problem and how to choose thresholds under operational constraints.

Explain unsupervised fraud and evaluation

Unsupervised Fraud Detection: Methods, When to Use Them, and How to Evaluate Without Reliable Labels

Context

Solution

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Explain unsupervised fraud and evaluation

Overview

Unsupervised Fraud Detection: Methods, When to Use Them, and How to Evaluate Without Reliable Labels

Context

Solution

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