摘要翻译:
我们开发了一个基于深度学习的预测消费者违约的模型。我们表明,即使使用相同的数据,该模型的表现也始终优于标准信用评分模型。我们的模型是可解释的,能够提供一个相对于标准信用评分模型的更大类别的借款人的评分,同时准确地跟踪系统风险的变化。我们认为,这些性质可以为旨在减少消费者违约、减轻借款人和贷款人负担的政策设计以及宏观审慎监管提供有价值的见解。
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英文标题:
《Predicting Consumer Default: A Deep Learning Approach》
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作者:
Stefania Albanesi and Domonkos F. Vamossy
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最新提交年份:
2019
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分类信息:
一级分类:Economics 经济学
二级分类:General Economics 一般经济学
分类描述:General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类:Computer Science 计算机科学
二级分类:Machine Learning 机器学习
分类描述:Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文(有监督的,无监督的,强化学习,强盗问题,等等),包括健壮性,解释性,公平性和方法论。对于机器学习方法的应用,CS.LG也是一个合适的主要类别。
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一级分类:Quantitative Finance 数量金融学
二级分类:Economics 经济学
分类描述:q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学,包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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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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PDF链接:
https://arxiv.org/pdf/1908.11498