Working Paper: NBER ID: w26165
Authors: Stefania Albanesi; Domonkos F. Vamossy
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; Deep Learning; Credit Scoring; Machine Learning
JEL Codes: C45; C55; D14; D18; E44; G02
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| credit-related features (G51) | probability of default (G33) |
| number of trades (F19) | probability of default (G33) |
| outstanding delinquencies (G33) | probability of default (G33) |
| length of credit history (G51) | probability of default (G33) |
| probability of default (G33) | systemic risk (E44) |
| consumer credit behavior (G51) | macroeconomic stability (E60) |