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Credit Risk Assessment with Stacked Machine Learning

Author

Listed:
  • Francesco Columba

    (Bank of Italy)

  • Manuel Cugliari

    (Bank of Italy)

  • Stefano Di Virgilio

    (Bank of Italy)

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.

Suggested Citation

  • Francesco Columba & Manuel Cugliari & Stefano Di Virgilio, 2026. "Credit Risk Assessment with Stacked Machine Learning," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems) 73, Bank of Italy, Directorate General for Markets and Payment System.
    • Handle: RePEc:bdi:wpmisp:mip_073_26
    as

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    File URL: https://www.bancaditalia.it/pubblicazioni/mercati-infrastrutture-e-sistemi-di-pagamento/approfondimenti/2026-073/N.73-MISP.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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