  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.
