用新方法解决信贷风险评估中的数据少和不平衡问题。
Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment
- 基于Wasserstein距离加权对齐源域与目标域特征分布
- 在真实数据集上提升跨域分类准确率与模型稳定性
- 适合金融领域冷启动场景下的风险建模应用
本文研究对抗域自适应（ADA）在金融机构信用风险评估中的应用，针对历史借贷数据稀缺的冷启动问题以及高风险交易样本不足的数据不平衡问题，提出一种改进的ADA框架——Wasserstein距离加权对抗域自适应网络（WD-WADA）。该方法利用Wasserstein距离有效对齐源域与目标域特征分布，并引入创新的加权策略，同时考虑类别分布差异与预测难度，缓解数据不平衡。实验在真实信贷数据集上验证了模型有效性，结果表明WD-WADA不仅缓解了冷启动问题，还更准确衡量域间差异，在跨域学习、分类准确率和模型稳定性方面显著优于传统方法。
原文摘要 · Abstract (English)
This paper delves into the application of adversarial domain adaptation (ADA) for enhancing credit risk assessment in financial institutions. It addresses two critical challenges: the cold start problem, where historical lending data is scarce, and the data imbalance issue, where high-risk transactions are underrepresented. The paper introduces an improved ADA framework, the Wasserstein Distance Weighted Adversarial Domain Adaptation Network (WD-WADA), which leverages the Wasserstein distance to align source and target domains effectively. The proposed method includes an innovative weighted strategy to tackle data imbalance, adjusting for both the class distribution and the difficulty level of predictions. The paper demonstrates that WD-WADA not only mitigates the cold start problem but also provides a more accurate measure of domain differences, leading to improved cross-domain credit risk assessment. Extensive experiments on real-world credit datasets validate the model's effectiveness, showcasing superior performance in cross-domain learning, classification accuracy, and model stability compared to traditional methods.
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