Predicting Consumer Default: A Deep Learning Approach
This work addresses consumer default prediction for lenders and policymakers, with incremental improvements in performance and interpretability.
The paper tackles predicting consumer default by developing a deep learning model that outperforms standard credit scoring models using the same data, offering interpretability and broader borrower coverage while tracking systemic risk variations.
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.