预测消费者违约：一种深度学习方法
Predicting Consumer Default: A Deep Learning Approach
摘要：
我们开发了一个基于深度学习的消费者违约预测模型。我们表明，该模型始终优于标准的信用评分模型，即使它使用相同的数据。我们的模型是可解释的，能够提供一个分数，以更大的一类借款人相对于标准的信用评分模型，同时准确地跟踪系统性风险的变化。我们认为，这些属性可以提供有价值的见解，旨在减少消费者违约，减轻其对借款人和贷款人的负担，以及宏观审慎监管的政策设计。
英文摘要：
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.
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