An explainable fairness-aware deep learning framework for credit score classification on imbalanced financial data

Abstract

Deep learning-based credit scoring systems face three interrelated challenges typically addressed in isolation: unavoidable class imbalance, model opacity, and demographic inequalities. This paper proposes the Explainable Fairness-Aware Deep Learning (EFADL) framework, which is a unified end-to-end pipeline designed to mitigate these risks simultaneously. The EFADL framework integrates three novel components: FC-SMOTE, a fairness-constrained oversampling module that preserves intra-group demographic balance; a multi-objective joint training loss combining focal imbalance correction with differentiable multi-attribute fairness penalties; and a dual-level SHAP module providing both instance-level adverse action explanations and group-level fairness attribution. Extensive experiments on the German Credit, Taiwan Credit, and LendingClub datasets demonstrate that EFADL achieves a better accuracy-fairness trade-off surface. Results indicate a 79% reduction in statistical parity and equal opportunity differences and a 70.5% decrease in maximum intersectional disparity, with a negligible AUC-ROC cost of only 1.2 percentage points. Furthermore, the framework reduces the fairness attribution gap by 71%, which provides evidence that it achieves fairness by suppressing reliance on demographic proxies rather than post-hoc calibration. By delivering stable, economically interpretable explanations, the EFADL framework aligns with the transparency requirements of the EU AI Act and US CFPB guidance and offers a deployable solution for regulatorily-compliant algorithmic lending.