Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default

• Main Authors: Crosato, Lisa, Liberati, Caterina, Repetto, Marco
• Format: Journal Article
• Language: English
• Published: 31-08-2021
• Subjects: Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2108.13914

Academic research and the financial industry have recently paid great
attention to Machine Learning algorithms due to their power to solve complex
learning tasks. In the field of firms' default prediction, however, the lack of
interpretability has prevented the extensive adoption of the black-box type of
models. To overcome this drawback and maintain the high performances of
black-boxes, this paper relies on 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 Network) 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 without
giving up a rich interpretation framework.