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