You are building a model to predict whether an applicant will default on a consumer lending product. An initial baseline model trained on raw application fields performs only moderately well, and your team wants to know how much feature engineering can improve performance without making the solution too complex to maintain.
How would you use feature engineering to improve model performance for this prediction task, and how would you decide which engineered features are actually helping rather than just adding complexity?