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Discussion Paper Details

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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.

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Bibliographic Reference

Albanesi, S and Vamossy, D. 2019. 'Predicting Consumer Default: A Deep Learning Approach'. London, Centre for Economic Policy Research. https://cepr.org/active/publications/discussion_papers/dp.php?dpno=13914