Corporate default forecasting with machine learning

Mirko Moscatelli (), Simone Narizzano (), Fabio Parlapiano and Gianluca Viggiano ()
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Mirko Moscatelli: Bank of Italy
Simone Narizzano: Bank of Italy
Gianluca Viggiano: Bank of Italy

No 1256, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: We analyze the performance of a set of machine learning (ML) 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 financial indicators, we find that ML models provide substantial gains in discriminatory power and precision compared with statistical models. This advantage diminishes when high quality information, such as credit behavioral indicators obtained from the Credit Register, is also available, and becomes negligible when the dataset is small. We also evaluate the consequences of using an ML-based rating system on the supply of credit and the number of borrowers gaining access to credit. ML models channel a larger share of credit towards safer and larger borrowers and result in lower credit losses for lenders.

Keywords: Credit Scoring; Machine Learning; Random Forest; Gradient Boosting Machine (search for similar items in EconPapers)
JEL-codes: C52 C55 D83 G2 (search for similar items in EconPapers)
Date: 2019-12
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ore and nep-rmg
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Citations: View citations in EconPapers (22)

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