How do you handle feature engineering for imbalanced datasets in real-time fraud detection models?

I'm working on a machine learning model for credit card fraud detection, and I’m struggling with the massive class imbalance. I’ve tried SMOTE, but it seems to be creating too much noise. Are there better feature engineering techniques or specific ensemble learning methods like XGBoost that you recommend for handling extremely rare events in high-velocity data streams?