Predicting Consumer Default: A Deep Learning Approach

Stefania Albanesi and Domonkos F. Vamossy

Papers from arXiv.org

Abstract: 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.

Date: 2019-08, Revised 2019-10
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-for, nep-mac, nep-pay and nep-rmg
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Citations: View citations in EconPapers (41)

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http://arxiv.org/pdf/1908.11498 Latest version (application/pdf)

Related works:
Working Paper: Predicting Consumer Default: A Deep Learning Approach (2019)
Working Paper: Predicting Consumer Default: A Deep Learning Approach (2019)
Working Paper: Predicting Consumer Default: A Deep Learning Approach (2019)
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