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  -