Predicting Consumer Default: A Deep Learning Approach
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.
This research was supported by the National Science Foundation under Grant No. SES 1824321. This research was also supported in part by the University of Pittsburgh Center for Research Computing through the resources provided. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.