Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning

Giuseppe Cascarino (), Mirko Moscatelli () and Fabio Parlapiano
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Mirko Moscatelli: Bank of Italy

No 674, Questioni di Economia e Finanza (Occasional Papers) from Bank of Italy, Economic Research and International Relations Area

Abstract: Forecasting models based on machine learning (ML) algorithms have been shown to outperform traditional models in several applications. The lack of an easily interpretable functional form, however, is a major challenge for their adoption, especially when a knowledge of the estimated relationships and an explanation of individual forecasts are needed, for instance due to regulatory requirements or when forecasts are used in policy making. We apply some of the most established methods from the eXplainable Artificial Intelligence (XAI) literature to shed light on the random forest corporate default forecasting model in Moscatelli et al. (2019) applied to Italian non-financial firms. The methods provide insight into the relative importance of financial and credit variables to predict firms’ financial distress. We complement the analysis by showing how the importance of these variables in explaining default risk changes over time in the period 2009-19. When financial conditions deteriorate, the variables characterized by a more complex relationship with financial distress, such as firms’ liquidity and indebtedness indicators, become more important in predicting borrowers’ defaults. We also discuss how ML models could enhance the accuracy of credit assessment for those borrowers with less developed credit relationships such as smaller firms

Keywords: explainable artificial intelligence; model-agnostic explainability; artificial intelligence; machine learning; credit scoring; fintech (search for similar items in EconPapers)
JEL-codes: C52 C55 D83 G2 (search for similar items in EconPapers)
Date: 2022-03
New Economics Papers: this item is included in nep-ban, nep-big, nep-cmp, nep-for and nep-rmg
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Citations: View citations in EconPapers (10)

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