Machine Learning-Based Profit Modeling for Credit Card Underwriting: Implications for Credit Risk

Abstract

Accurate profit modeling is crucial for effective credit risk management in credit card underwriting. This study explores how machine learning methods can improve profit models in credit card underwriting, using data from a major Chinese bank as a case study. We employed three primary machine learning algorithms—Random Forest, Gradient Boosting Trees, and Neural Networks—to analyze a credit card customer dataset comprising 500,000 customers and 8 million transaction records. Model performance was evaluated using 10-fold cross-validation. The results show that the Gradient Boosting Trees model outperformed the others across all evaluation metrics. Specifically, the Gradient Boosting Trees model achieved an accuracy of 89.2%, a recall of 85.3%, an F1 score of 87.1%, and an AUC of 0.927, significantly surpassing the other models. Furthermore, the confusion matrix results indicated that the Gradient Boosting Trees model had the highest true positive and true negative values, and the lowest false positive and false negative values, further validating its superior classification performance. In summary, the Gradient Boosting Trees model demonstrated higher accuracy and stability in predicting profit in credit card underwriting, providing a robust basis for financial institutions to select appropriate machine learning models for credit risk management.