TY - JOUR AU - Albanesi, Stefania AU - Vamossy, Domonkos F TI - Predicting Consumer Default: A Deep Learning Approach JF - National Bureau of Economic Research Working Paper Series VL - No. 26165 PY - 2019 Y2 - August 2019 DO - 10.3386/w26165 UR - http://www.nber.org/papers/w26165 L1 - http://www.nber.org/papers/w26165.pdf N1 - Author contact info: Stefania Albanesi Department of Economics University of Pittsburgh 4901 Wesley W. Posvar Hall Pittsburgh, PA 15260 E-Mail: stefania.albanesi@gmail.com Domonkos F. Vamossy 230 S Bouquet St Department of Economics, University of Pittsburgh Pittsburgh, PA 15260 United States Tel: 2023943452 E-Mail: d.vamossy@pitt.edu AB - 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. ER -