Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910622963204096 |
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| author | Jiang, Mohan Lin, Jiating Ouyang, Hongju Pan, Jingming Han, Siyuan Liu, Bingyao |
| author_facet | Jiang, Mohan Lin, Jiating Ouyang, Hongju Pan, Jingming Han, Siyuan Liu, Bingyao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_18544 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment Jiang, Mohan Lin, Jiating Ouyang, Hongju Pan, Jingming Han, Siyuan Liu, Bingyao Machine Learning 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. |
| title | Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2409.18544 |