Predicting Consumer Default: A Deep Learning Approach

74 Pages Posted: 20 Aug 2019

See all articles by Stefania Albanesi

Stefania Albanesi

University of Pittsburgh

Domonkos F. Vamossy

University of Pittsburgh

Multiple version iconThere are 3 versions of this paper

Date Written: August 2019

Abstract

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.

Keywords: Consumer default, credit scores, deep learning, macroprudential policy

JEL Classification: C45, D1, E27, E44, G21, G24

Suggested Citation

Albanesi, Stefania and Vamossy, Domonkos F., Predicting Consumer Default: A Deep Learning Approach (August 2019). CEPR Discussion Paper No. DP13914. Available at SSRN: https://ssrn.com/abstract=3439468

Stefania Albanesi (Contact Author)

University of Pittsburgh ( email )

135 N Bellefield Ave
Pittsburgh, PA 15260
United States

HOME PAGE: http://https://sites.google.com/site/stefaniaalbanesi/

Domonkos F. Vamossy

University of Pittsburgh ( email )

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