Predicting Consumer Default : A Deep Learning Approach

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 provides favorable credit risk assessment to young 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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Year of publication:	2020
Authors:	Albanesi, Stefania
Other Persons:	Vamossy, Domonkos F. (contributor)
Publisher:	[2020]: [S.l.] : SSRN
Subject:	Konsumentenverhalten \| Consumer behaviour \| Prognoseverfahren \| Forecasting model \| Insolvenz \| Insolvency \| Theorie \| Theory \| Künstliche Intelligenz \| Artificial intelligence

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