Predicting Consumer Default: A Deep Learning Approach

Stefania Albanesi and Domonkos Vamossy

No 2019-056, Working Papers from Human Capital and Economic Opportunity Working Group

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.

Keywords: consumer default; credit scores; deep learning; macroprudential policy (search for similar items in EconPapers)
JEL-codes: D14 E44 G21 (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-ban, nep-cmp, nep-mac, nep-ore and nep-pay
Note: FI
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (36)

Downloads: (external link)
http://humcap.uchicago.edu/RePEc/hka/wpaper/Albane ... consumer-default.pdf First version, August 29, 2019 (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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