Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerability to financial distress. Predicting SME default is therefore crucial for financial institutions, policymakers, and researchers. Recent advances in machine learning (ML) have improved predictive performance in credit risk modeling. Yet, the limited interpretability of complex models raises concerns regarding transparency and regulatory compliance. This study investigates SME's default predictors and applies explainable artificial intelligence (XAI) techniques to them. Using a panel of 50,718 Italian SME over the period 2015-2024, we compare traditional econometric approaches with several ML classifiers. The empirical results show that ML models significantly outperform the traditional logistic regression benchmark in terms of Balanced Accuracy and PR-AUC. To address the interpretability challenge, we introduce DEXiRE-EVO, a novel evolutionary rule extraction framework that combines multi-objective optimization with the Contextual Importance and Utility (CIU) explainability method. The extracted rules reveal economically meaningful patterns associated with SME financial distress, highlighting the roles of weak internal liquidity generation, internal capital erosion, high leverage, and operational inefficiency. Additionally, contextual macroeconomic conditions and the persistence of financial instability contribute to identifying high-risk firms. In general, the results show that combining ML with evolutionary rule extraction can improve both predictive performance and interpretability in credit risk modeling, thus supporting more transparent, data-driven decision-making in financial environments.

翻译：中小企业（SMEs）在多数经济体中占据企业主体地位，常面临财务约束且更易陷入财务困境。预测中小企业违约对金融机构、政策制定者及研究人员至关重要。机器学习领域的最新进展提升了信用风险建模的预测性能，但复杂模型可解释性有限，引发了对透明度和监管合规性的担忧。本研究探究了中小企业的违约预测因子，并对其应用可解释人工智能技术。基于2015-2024年间50,718家意大利中小企业的面板数据，我们比较了传统计量方法与多种机器学习分类器的表现。实证结果表明，机器学习模型在平衡准确率和PR-AUC指标上显著优于传统逻辑回归基准模型。为解决可解释性挑战，我们提出了DEXiRE-EVO——一种融合多目标优化与情境重要性和效用可解释方法的新型进化规则提取框架。提取的规则揭示了与中小企业财务困境相关的具有经济意义的模式，突显了内部流动性生成不足、内部资本侵蚀、高杠杆及运营效率低下等关键因素。此外，宏观经济环境与财务不稳定的持续性特征有助于识别高风险企业。总体而言，研究结果表明，将机器学习与进化规则提取相结合，可同时提升信用风险建模的预测性能与可解释性，从而支持金融环境中更透明、数据驱动的决策制定。