DP13914 Predicting Consumer Default: A Deep Learning Approach

Author(s):	Stefania Albanesi, Domonkos Vamossy
Publication Date:	August 2019
Keyword(s):	Consumer default, credit scores, deep learning, macroprudential policy
JEL(s):	C45, D1, E27, E44, G21, G24
Programme Areas:	Financial Economics, Monetary Economics and Fluctuations
Link to this Page:	cepr.org/active/publications/discussion_papers/dp.php?dpno=13914

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.