Hidden Culprit

You are a data scientist at CreditWise, a credit card company building a fraud detection model. Out of 100,000 historical transactions, only 1,000 are fraudulent, causing a severe class imbalance in the training data.

Your team reports that the model shows high overall accuracy but performs poorly on actual fraud cases.

How would you address this class imbalance to improve fraud detection performance?

Explain:

1. The impact of class imbalance on model behavior and evaluation
2. The techniques you would use to handle it
3. The trade-offs these techniques introduce

Note: The business prioritizes catching fraud over minimizing false alerts.