Lost in a black‐box? Interpretable machine learning for assessing Italian SMEs default

• Published in: Applied stochastic models in business and industry Vol. 39; no. 6; pp. 829 - 846
• Main Authors: Crosato, Lisa, Liberati, Caterina, Repetto, Marco
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.11.2023
• Subjects: accumulated local effects; Algorithms; Artificial neural networks; Cognitive tasks; default prediction; interpretability; Machine learning; small and medium sized enterprises; Small business; Task complexity
• ISSN: 1524-1904; 1526-4025
• DOI: 10.1002/asmb.2803

Academic research and the financial industry have recently shown great interest in Machine Learning algorithms capable of solving complex learning tasks, although in the field of firms' default prediction the lack of interpretability has prevented an extensive adoption of the black‐box type of models. In order to overcome this drawback and maintain the high performances of black‐boxes, this paper has chosen a model‐agnostic approach. Accumulated Local Effects and Shapley values are used to shape the predictors' impact on the likelihood of default and rank them according to their contribution to the model outcome. Prediction is achieved by two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Networks) compared with three standard discriminant models. Results show that our analysis of the Italian Small and Medium Enterprises manufacturing industry benefits from the overall highest classification power by the eXtreme Gradient Boosting algorithm still maintaining a rich interpretation framework to support decisions.