You are a data scientist at a financial institution tasked with building a model to detect fraudulent transactions. The dataset is highly imbalanced, with only a small percentage of transactions being fraudulent. After experimenting with several models, you decide to implement a boosting technique to improve the model’s accuracy, particularly on the minority class. You are considering different types of boosting, including Adaptive Boosting (AdaBoost), Gradient Boosting, and Extreme Gradient Boosting (XGBoost). Given the problem context and the need to effectively handle class imbalance, which boosting technique is MOST SUITABLE for this scenario?