Deep Learning for Predicting Credit Card Default

Predicting credit card defaults is pivotal for financial institutions to mitigate risks and implement proactive measures. Despite the monumental successes of deep learning in domains like computer vision and natural language processing, its efficacy in leveraging tabular data structures, especially in credit default prediction, remains contentious. In this manuscript, we systematically investigate various deep learning architectures, juxtaposing their performance with a meticulously optimized Gradient Boosted Decision Tree (GBDT) classifier. Our empirical findings reveal that the apex deep learning model achieved a performance metric of 0.783, slightly under performing against the GBDT model’s 0.791. This reinforces existing literature, accentuating the superior prowess of tree-based algorithms in deciphering the intricacies of credit default prediction over contemporary deep learning paradigms.