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In recent years, the increasing prevalence of credit card usage has raised concerns about accurately predicting and managing credit card defaults. While machine learning and deep learning methods have shown promising results in default prediction, the black-box nature of these models often limits their interpretability and practical adoption. This study presents a new method for predicting credit card default using a combination of deep learning and explainable artificial intelligence (XAI) techniques. Integrating these methods aims to improve the interpretability of the decision-making process involved in credit card default prediction. The proposed approach is evaluated using a real-world dataset and compared to existing state-of-the-art models. Results show that the proposed approach achieves competitive prediction accuracy while providing meaningful insights into the factors driving credit card default risk. The present investigation adds to the increasing body of literature on explainable artificial intelligence (AI) in the realm of finance. Besides, it provides a pragmatic approach to assessing credit risk, balancing precision and comprehensibility. In conclusion, the model demonstrates strong potential as a credit risk assessment tool, with an accuracy of 0.8350, sensitivity of 0.8823, and specificity of 0.9879. Among the most important features identified by the model are payment delays and outstanding bill amounts. This study is a step toward more interpretable and transparent credit scoring models. [ABSTRACT FROM AUTHOR]