Lending activities, especially for small and medium enterprises (SMEs), are increasingly based on financial technologies, facilitated by the availability of advanced machine learning (ML) methods that can accurately predict the financial performance of a company from the available data sources. However, despite their high predictive accuracy, ML models may not give users sufficient interpretation of the results. Therefore, it may not be adequate for informed decision-making, as stated, for example, in the recently proposed artificial intelligence (AI) regulations. To fill the gap, we employed Shapley values in the context of model selection. Thus, we propose a model selection method based on predictive accuracy that can be employed for all types of ML models, those with a probabilistic background, as in the current state-of-the-art. We applied our proposal to a credit-scoring database with more than 100,000 SMEs. The empirical findings indicate that the risk of investing in a specific SME can be predicted and interpreted well using a machine-learning model which is both predictively accurate and explainable.

贷款活动，尤其是中小型企业 (SME) 的贷款活动，越来越多地基于金融技术，先进的机器学习 (ML) 方法可以根据可用数据源准确预测公司的财务业绩，从而促进了这一点。然而，尽管机器学习模型的预测准确性很高，但它可能无法为用户提供对结果的充分解释。因此，它可能不足以做出明智的决策，例如最近提出的人工智能（AI）法规中所述。为了填补这一空白，我们在模型选择中采用了 Shapley 值。因此，我们提出了一种基于预测准确性的模型选择方法，该方法可用于所有类型的机器学习模型，即具有概率背景的模型，就像当前最先进的技术一样。我们将我们的建议应用于包含超过 100,000 家中小企业的信用评分数据库。实证结果表明，使用机器学习模型可以很好地预测和解释投资特定中小企业的风险，该模型既预测准确又可解释。