Summary of Wasserstein Distance-weighted Adversarial Network For Cross-domain Credit Risk Assessment, by Mohan Jiang et al.
Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment
by Mohan Jiang, Jiating Lin, Hongju Ouyang, Jingming Pan, Siyuan Han, Bingyao Liu
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents an innovative approach to enhancing credit risk assessment in financial institutions by applying adversarial domain adaptation (ADA) techniques. The proposed method, Wasserstein Distance Weighted Adversarial Domain Adaptation Network (WD-WADA), addresses two critical challenges: the cold start problem and data imbalance. WD-WADA leverages the Wasserstein distance to align source and target domains effectively and includes a weighted strategy to tackle data imbalance. Experimental results on real-world credit datasets demonstrate the model’s superiority in cross-domain learning, classification accuracy, and model stability compared to traditional methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps financial institutions better assess credit risk by using special computer techniques called adversarial domain adaptation. It solves two big problems: when there’s not much data to start with (the cold start problem) and when some types of transactions are much more common than others (data imbalance). The new method, WD-WADA, uses a clever way to match the old and new data together. It also has a special trick to deal with the imbalance problem. The results show that this approach is better at predicting credit risk and making accurate predictions. |
Keywords
» Artificial intelligence » Classification » Domain adaptation
