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

• URL: http://arxiv.org/abs/2108.13914v2
• Date: Wed, 1 Sep 2021 07:29:21 GMT
• Title: Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default
• Authors: Lisa Crosato, Caterina Liberati and Marco Repetto
• Abstract summary: This paper relies on a model-agnostic approach to model firms' default prediction. Two Machine Learning algorithms (eXtreme Gradient Boosting and FeedForward Neural Network) are compared to 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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