EconPapers    
Economics at your fingertips  
 

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
http://www.nber.org/papers/w26165.pdf (application/pdf)
Access to the full text is generally limited to series subscribers, however if the top level domain of the client browser is in a developing country or transition economy free access is provided. More information about subscriptions and free access is available at http://www.nber.org/wwphelp.html. Free access is also available to older working papers.

Related works:
Working Paper: Predicting Consumer Default: A Deep Learning Approach (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nbr:nberwo:26165

Ordering information: This working paper can be ordered from
http://www.nber.org/papers/w26165
The price is Paper copy available by mail.

Access Statistics for this paper

More papers in NBER Working Papers from National Bureau of Economic Research, Inc National Bureau of Economic Research, 1050 Massachusetts Avenue Cambridge, MA 02138, U.S.A.. Contact information at EDIRC.
Bibliographic data for series maintained by ().

 
Page updated 2020-06-20
Handle: RePEc:nbr:nberwo:26165