You work for a major banking firm as a machine learning specialist. As part of the bank’s fraud detection team, you build a machine learning model to detect fraudulent transactions. Using your training dataset, you have produced a Receiver Operating Characteristic (ROC) curve, and it shows 99.99% accuracy.Your transaction dataset is very large, but 99.99% of the observations in your dataset represent non-fraudulent transactions. Therefore, the fraudulent observations are a minority class. Your dataset is very imbalanced.
You have the approval from your management team to produce the most accurate model possible, even if it means spending more time perfecting the model. What is the most effective technique to address the imbalance in your dataset?

Answer options:

A.Synthetic Minority Oversampling Technique (SMOTE) oversampling
B.Random oversampling
C.Generative Adversarial Networks (GANs) oversampling
D.Edited Nearest Neighbor undersampling