Discussion Paper Details

Please find the details for DP13914 in an easy to copy and paste format below:

Full Details   |   Bibliographic Reference

Full Details

Title: Predicting Consumer Default: A Deep Learning Approach

Author(s): Stefania Albanesi and Domonkos Vamossy

Publication Date: August 2019

Keyword(s): Consumer default, credit scores, deep learning and macroprudential policy

Programme Area(s): Financial Economics and Monetary Economics and Fluctuations

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.

For full details and related downloads, please visit:

Bibliographic Reference

Albanesi, S and Vamossy, D. 2019. 'Predicting Consumer Default: A Deep Learning Approach'. London, Centre for Economic Policy Research.