Credit card fraud is a big problem in the world of e-commerce, and a difficult one to eliminate. In 2024, it was reported that consumers lost more than $12.5 billion to fraud. As such, it is imperative that the fraudulent transaction detection system is efficient. This study uses boosted decision trees (BDT) to predict whether a transaction is fraudulent or not. The dataset used is obtained from IEEE-CIS Fraud Detection Kaggle competition. The evaluation of the models is done using area under the curve (AUC) of receiver operating
characteristic (ROC) curve via submission of fraud probability scores of test set to kaggle.