AI-Powered Reduced-Form Model for Default Rate Forecasting

Jacopo Giacomelli ()
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Jacopo Giacomelli: SACE S.p.A., Piazza Poli 42, 00187 Rome, Italy

Risks, 2025, vol. 13, issue 8, 1-20

Abstract: This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving significant levels of explained variance. The results obtained show that enhancing a reduced-form model by integrating it with neural networks is possible and practical for multivariate forecasting of future default frequencies. In our analysis, we utilize the recently proposed RecessionRisk + , a reduced-form latent-factor model developed for default and recession risk management applications as an improvement of the well-known CreditRisk + model. The model has been empirically verified to exhibit some predictive power concerning future default rates. However, the theoretical framework underlying the model does not provide the elements necessary to define a proper estimator for forecasting the target default rates, leaving space for the application of a neural network framework to retrieve the latent information useful for default rate forecasting purposes. Among the neural network models tested in combination with RecessionRisk + , the best results are obtained with shallow LSTM networks.

Keywords: credit risk; default forecasting; neural networks; reduced-form models; recession risk; machine learning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2025
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