Explainable Domain Adaptation Learning Framework for Credit Scoring in Internet Finance Through Adversarial Transfer Learning and Ensemble Fusion Model

Adversarial transfer learning is extensively applied in computer vision owing to its remarkable capability in addressing domain adaptation. However, its applications in credit scoring remain underexplored due to the complexity of financial data. The performance of traditional credit scoring models relies on the consistency of domain distribution. Any shift in feature distribution leads to a degradation in model accuracy. To address this issue, we propose a domain adaptation framework comprising a transfer learner and a decision tree. The framework integrates the following: (1) feature partitioning through Wassertein relevance metric; (2) adversarial training of the transfer learner using features with significant distributional differences to achieve an inseparable representation of the source and target domains, while the remaining features are utilized for decision tree model training; and (3) a weighted voting method combines the predictions of the transfer learner and the decision tree. The Shapley Additive Explanations (SHAP) method was used to analyze the predictions of the model, providing the importance of individual features and insights into the model’s decision-making process. Experimental results show that our approach improves prediction accuracy by 3.5% compared to existing methods.