Algorithm comparison for data mining classification: assessing bank customer credit scoring default risk - Elaf Adel Abbas,; Nisreen Abbas Hussein, (Penerbit Universiti Kebangsaan Malaysia, 2024)
Rating consumer credit risk involves assessing credit application risks. Thus, every business must appropriately identify debtors and non-debtors. This study uses machine learning approaches to simulate consumer credit risk and compares the results to the logistic model, determining if machine learning improves client default ratings. The study examines how customer attributes affect virtual experiences. Despite advances in machine learning models for credit assessment, unbalanced datasets and some algorithms’ failure to explain forecasts remain major issues. This study used 2005 Taiwanese credit card consumers’ education, age, marital status, payment history, and sex. The default experience is modeled using Logistic Regression, K neighbors, Support Vector Machine, Decision Tree, Random Forest, Ada Boost Classifier, and Gradient Boosting. The models’ Accuracy, precision, recall, receiver operating characteristic (ROC) curve, and precision-recall curve were evaluated. Random Forest’s 97% ROC metric rating outperformed all other accuracy metrics. The logistic model underperformed, while machine learning improved the default categorization.