Mastering Fraud Detection: Advanced Machine Learning Challenges Quiz

1. Interpreting Model Performance

   In a financial dataset with a 0.5% fraud rate, which metric best assesses a classifier’s ability to detect fraud, and why is 'accuracy' misleading in this scenario?

   1. A. Precision, because it measures the percentage of predicted fraud cases that are truly fraudulent
   2. B. Recall, because it measures the percentage of actual frauds correctly detected
   3. C. F1-score, as it balances both precision and recall in the evaluation
   4. D. Accuracy, because it shows the overall correct predictions including both fraud and non-fraud
   5. E. Area Under the ROC Curve (AUC-ROC), which is not impacted by class imbalance

2. Handling Imbalanced Classes

   Suppose a dataset for fraud detection has a 1:200 ratio of fraudulent to legitimate transactions. Which technique most effectively addresses this class imbalance during training?

   1. A. Downsampling the majority class to match the minority class
   2. B. Removing outlier points entirely
   3. C. Randomly duplicating all data points for the minority class without modification
   4. D. Shuffling the dataset before training
   5. E. Using the original unbalanced dataset with no changes

3. Feature Engineering in Fraud Detection

   When building a machine learning model to detect fraudulent transactions, which of the following feature engineering steps can help capture sudden changes in a user’s behavior?

   1. A. Calculating the moving average of transaction amounts per day
   2. B. Encoding user IDs as raw numerical values
   3. C. Removing all categorical variables related to geography
   4. D. Replacing timestamps with random numbers
   5. E. Scaling all features to the range [0, 1] without investigating distributions

4. Unsupervised Techniques for Unknown Frauds

   If labels for fraudulent transactions are unavailable, which machine learning approach is best suited to detecting potential new types of fraud?

   1. A. Logistic regression on labeled data
   2. B. Decision trees with grid search
   3. C. Unsupervised anomaly detection using clustering algorithms
   4. D. Oversampling using SMOTH
   5. E. Stacking multiple supervised classifiers

5. Concept Drift and Model Updating

   In fraud detection systems, what is the primary risk if a model trained on last year’s data is not periodically updated, especially when fraud patterns evolve over time?

   1. A. The model may start predicting only legitimate transactions as fraud
   2. B. The model could suffer from concept drift, leading to reduced effectiveness
   3. C. The model’s computational cost will increase automatically
   4. D. The model’s recall will improve continually
   5. E. The model will eventually require more storage space than the dataset