| 来源类型 | Discussion paper |
| 规范类型 | 论文 |
| 来源ID | DP13914 |
| DP13914 Predicting Consumer Default: A Deep Learning Approach | |
| Stefania Albanesi; Domonkos Vamossy | |
| 发表日期 | 2019-08-07 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation. |
| 主题 | Financial Economics ; Monetary Economics and Fluctuations |
| 关键词 | Consumer default Credit scores Deep learning Macroprudential policy |
| URL | https://cepr.org/publications/dp13914 |
| 来源智库 | Centre for Economic Policy Research (United Kingdom) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/542795 |
| 推荐引用方式 GB/T 7714 | Stefania Albanesi,Domonkos Vamossy. DP13914 Predicting Consumer Default: A Deep Learning Approach. 2019. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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