Correct Options: C and E

Explanation:
In this scenario, feature engineering is crucial for transforming raw financial data into meaningful features that enhance the model's ability to predict loan defaults accurately. It also plays a key role in improving model interpretability, which is essential for regulatory compliance. While option C highlights the improvement in performance and accuracy, option E additionally emphasizes the importance of interpretability, making both options correct under the given constraints.

Why other options are incorrect:

• A. Feature engineering does not automate the training process; it requires domain knowledge and manual effort to create effective features.
• B. Feature engineering does not eliminate the need for data preprocessing; instead, it is a part of the preprocessing step that may require computational resources.
• D. Increasing dimensionality is not the primary goal of feature engineering; the focus is on creating meaningful features that improve model performance, not necessarily on adding more features.