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