Corporate default forecasting with machine learning

Abstract We analyze the performance of a set of machine learning models in predicting default risk, using standard statistical models, such as the logistic regression, as a benchmark. When only a limited information set is available, for example in the case of an external assessment of credit risk, we find that machine learning models provide substantial gains in discriminatory power and precision, relative to statistical models. This advantage diminishes when confidential information, such as credit behavioral indicators, is also available, and it becomes negligible when the dataset is small. Moreover, we evaluate the consequences of using a credit allocation rule based on machine learning ratings on the overall supply of credit and the number of borrowers gaining access to credit. Machine learning models concentrate a greater extent of credit towards safer and larger borrowers, which would result in lower credit losses for their lenders.

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