Predicting Consumer Default: A Deep Learning Approach / Stefania Albanesi, Domonkos F. Vamossy.

Format:

Book

Author/Creator:

Albanesi, Stefania.

Contributor:

National Bureau of Economic Research.

Vamossy, Domonkos F.

Series:

Working Paper Series (National Bureau of Economic Research) no. w26165.

NBER working paper series no. w26165

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Other Title:

Predicting Consumer Default

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2019.

Summary:

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.

Notes:

Print version record

August 2019.