Explainable Deep Tabular Learning for Credit Risk Assessment: An Information-Theoretic Cross-Attentional Transformer Approach
This study develops an explainable machine learning framework for modeling loan approval decisions on heterogeneous tabular data, centered on a Cross-Attentional Tabular Transformer that applies bidirectional cross-attention between numerical and categorical feature groups.