Identify Unsupervised Techniques for Detecting Fraudulent Transactions

Q: Identify Unsupervised Techniques for Detecting Fraudulent Transactions

This question evaluates competence in unsupervised anomaly detection and model evaluation when labels are unavailable, focusing on the ability to surface suspicious transactions and assess model effectiveness without ground truth.

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Question

Unsupervised Fraud Detection: Modeling and Evaluation Without Labels

Scenario

You receive millions of historical transactions with no fraud labels. Management wants an unsupervised system to surface potentially fraudulent transactions and a way to evaluate its effectiveness.

Task

Which unsupervised learning approaches would you use to flag suspicious transactions, and why?
Without labels, how would you measure the accuracy or performance of your model? Name concrete evaluation techniques or proxy metrics.

Hints: Consider clustering, distance/density-based anomaly detection, isolation methods, autoencoders, human review samples, precision from post-labeling, and business KPIs.

Identify Unsupervised Techniques for Detecting Fraudulent Transactions

Unsupervised Fraud Detection: Modeling and Evaluation Without Labels

Scenario

Task

Solution

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Identify Unsupervised Techniques for Detecting Fraudulent Transactions

Overview

Unsupervised Fraud Detection: Modeling and Evaluation Without Labels

Scenario

Task

Solution

Comments (0)