Example-dependent cost-sensitive credit cards fraud detection using SMOTE and Bayes minimum risk

This paper presents fraud detection problem as one of the most common problems in secure banking research field, due to its importance in reducing the losses of banks and e-transactions companies. Our work will include: applying the common classification algorithms such as logistic regression (LR), random forest (RF), alongside with modern classifiers with state-of-the-art results as XGBoost (XG) and CatBoost (CB), testing the effect of the unbalanced data through comparing their results with and without balancing, then focusing on the savings measure to test the effect of cost-sensitive wrapping of Bayes minimum risk (BMR), we will concentrate on using F1-score, AUC and Savings measures after using the traditional measures duo to their suitability to our problem. The results show that CB has the best savings (0.7158) alone, (0.971) when using SMOTE and (0.9762) with SMOTE and BMR, while XG has the best savings (0.757) when using BMR without SMOTE.