AI0-001 Machine Learning and Deep Learning Practice Question
A financial institution is developing a fraud detection model using historical transaction data. The dataset contains over 10 million records, but only 0.01% of transactions are fraudulent. The current model uses a neural network trained with standard cross-entropy loss, and the team applies random undersampling of the majority class to create a balanced training set. However, the model still produces a high number of false positives (legitimate transactions flagged as fraud) and misses approximately 30% of actual fraud cases. The business requires that at least 95% of frauds be caught, and the false positive rate must be below 1% to avoid overwhelming fraud analysts. The team has limited resources to collect additional data and cannot change the model architecture significantly. Which approach should the team take to best meet the business requirements?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.
Cost-sensitive learning adjusts the loss function to penalize false negatives more heavily, directly addressing the need to catch more frauds while controlling false positives. Collecting more data is impractical and may not resolve the imbalance. Anomaly detection models treat fraud as outliers but often have high false positive rates in this context. Feature selection does not inherently solve the imbalance or performance metric trade-off.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use cost-sensitive learning by assigning a higher misclassification cost to the fraud class.
Why this is correct
This directly penalizes false negatives more, encouraging the model to catch more frauds while maintaining a low false positive rate through tuning.
- ✗
Apply feature selection to remove noisy predictors and then retrain the current model.
Why it's wrong here
Feature selection may help but does not directly address the class imbalance or the specific performance targets for recall and false positive rate.
- ✗
Switch to an anomaly detection algorithm such as Isolation Forest or One-Class SVM.
Why it's wrong here
Anomaly detection typically assumes outliers are rare and distinct, but transaction fraud can be very similar to legitimate behavior, leading to high false positive rates.
- ✗
Collect more transaction data, especially fraudulent examples, to naturally balance the classes.
Why it's wrong here
Collecting more data is resource-intensive and may not be feasible; oversampling existing fraud data could cause overfitting.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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