A Novel Weighted Loss TabTransformer Integrating Explainable AI for Imbalanced Credit Risk Datasets

Credit risk assessment often faces significant challenges due to class imbalance and the opaque nature of machine learning models, which can result in biased predictions and hinder trust among stakeholders. To address these issues, this study proposes a framework combining the TabTransformer model with weighted loss techniques to balance class distributions and improve predictive accuracy. Applied to the BISAID and German Credit datasets, the method demonstrated notable improvements in accuracy, from 86.35% to 89.27% and 93% to 95%, respectively, along with improved minority class AUC and precision-recall metrics. To ensure transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed, highlighting critical predictors such as “Financing Needs” and “Credit Amount.” By integrating fairness mechanisms through weighted loss and explainability via XAI, the proposed framework and weighted loss TabTransformer mitigate bias, enhance model performance, and provide actionable insights for borrowers and stakeholders. These findings establish a reliable, equitable, and transparent approach to credit evaluation.