Predicting Consumer Default: A Deep Learning Approach
Stefania Albanesi () and
No 13914, CEPR Discussion Papers from C.E.P.R. Discussion Papers
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: C45 D1 E27 E44 G21 G24 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ban, nep-big, nep-cba, nep-cmp, nep-fmk, nep-mac, nep-ore and nep-pay
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)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at email@example.com
Working Paper: Predicting Consumer Default: A Deep Learning Approach (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:13914
Ordering information: This working paper can be ordered from
http://www.cepr.org/ ... rs/dp.php?dpno=13914
Access Statistics for this paper
More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().