Credit Risk Assessment with Stacked Machine Learning

Francesco Columba, Manuel Cugliari () and Stefano Di Virgilio ()
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Manuel Cugliari: Bank of Italy
Stefano Di Virgilio: Bank of Italy

No 73, Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) from Bank of Italy, Directorate General for Markets and Payment System

Abstract: The Banca d’Italia’s system for the credit assessment of non-financial firms for collateral purposes in monetary policy consists of a statistical model (S-ICAS) and of the analysts’ evaluation. We compare the performance of S-ICAS with that of artificial intelligence – machine learning (ML) – models, including deep learning. We find that deep learning improves the discriminatory power; decision tree ensembles yield a further improvement, as well as a meta-model that stacks the random forests, extreme gradient boosting, and deep learning models. We apply eXplainable Artificial Intelligence (XAI) techniques to the meta-model predictions and show that XAI can support analysts in understanding the key factors behind the differences between ML and S-ICAS predictions, thus helping refine their assessment. While interpretability issues prevent ML-based models from being a full alternative to traditional models, XAI allows for their integration within the overall credit assessment process, thus increasing its effectiveness.

Keywords: credit risk; machine learning; deep learning; explainable artificial intelligence (search for similar items in EconPapers)
JEL-codes: C52 C55 G24 G32 (search for similar items in EconPapers)
Date: 2026-01
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Persistent link: https://EconPapers.repec.org/RePEc:bdi:wpmisp:mip_073_26

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