Predicting Consumer Default: A Deep Learning Approach

Stefania Albanesi and Domonkos F. Vamossy

No 26165, NBER Working Papers from National Bureau of Economic Research, Inc

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.

JEL-codes: C45 C55 D14 D18 E44 G0 G2 (search for similar items in EconPapers)
Date: 2019-08
New Economics Papers: this item is included in nep-big, nep-mac, nep-ore, nep-pay and nep-rmg
Note: EFG ME
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Citations: View citations in EconPapers (38)

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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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