UNBALANCED CREDIT FRAUD MODELING BASED ON BAGGING AND BAYESIAN OPTIMIZATION

Abstract

Credit fraud modeling is a crucial area of research that is highly relevant to the credit loan industry. Effective risk management is a key factor in providing quality credit services and directly impacts the profitability and bad debt ratio of leading organizations in this sector. However, when the distribution of credit fraud data is highly unbalanced, it can lead to noise errors caused by information distortion, periodic statistical errors, and model biases during training. This can cause unfair results for the minority class (target class) and increase the risk of overfitting. While traditional data balancing methods can reduce bias in models towards the majority class in relatively unbalanced data, they may not be effective in highly unbalanced scenarios. To address this challenge, this paper proposes using Bagging algorithms such as Random Forest and Bagging to model highly unbalanced credit fraud data. Bayesian optimization is utilized to find hyperparameters and determine the accuracy of the minority class as an optimization function for the model, which is tested with real European credit card fraud data. The results of the proposed packing algorithms are compared with traditional data balancing methods such as Balanced Bagging and Balanced Random Forest. The study found that traditional data balancing methods may not be compatible with excessively unbalanced data, whereas Bagging algorithms show promise as a solution for modeling such data. The proposed method for finding hyperparameters effectively deals with highly unbalanced data. It achieved precision, recall, and F1-score for the minority category of 0.94, 0.81, and 0.87, respectively. The study emphasizes the importance of addressing the challenges associated with unbalanced credit fraud data to improve the accuracy and fairness of credit fraud models