This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credit scoring models. The approach aligns financial statement reference dates with evaluation dates, mitigating bias arising from publication delays and asynchronous data sources. It is based on a two-step temporal decomposition that at first estimates annual probabilities of default (PDs) anchored to balance-sheet reference dates (December 31st) through a static model. Then it models the monthly evolution of PDs using higher-frequency behavioral data. Finally, we employ stacking-based architecture to aggregate multiple scoring systems, each capturing complementary aspects of default risk, into a unified predictive model. In this way, first level model outputs are treated as learned representations that encode non-linear relationships in financial and behavioral indicators, allowing integration of new expert-based features without retraining base models. This design provides a coherent and interpretable solution to challenges typical of low-default environments, including heterogeneous default definitions and reporting delays. Empirical validation shows that the framework effectively captures credit risk evolution over time, improving temporal consistency and predictive stability relative to standard ensemble methods.

翻译：本文提出一种用于意大利中小企业信用风险评估的元学习框架，该框架明确解决了信用评分模型的时间错位问题。该方法通过将财务报表参考日期与评估日期对齐，有效缓解了由数据发布延迟和异步数据源引起的偏差。其核心是基于两步时间分解：首先通过静态模型估计以资产负债表参考日期（12月31日）为锚点的年度违约概率；随后利用高频行为数据建模PD的月度演化过程。最后，我们采用基于堆叠的架构将多个评分系统（每个系统捕捉违约风险的不同互补维度）聚合为统一预测模型。在此设计中，第一层模型的输出被视为编码财务与行为指标间非线性关系的学习表征，使得无需重新训练基础模型即可整合新的专家特征。该框架为低违约环境中的典型挑战（包括异质违约定义和报告延迟）提供了连贯且可解释的解决方案。实证验证表明，相较于标准集成方法，本框架能有效捕捉信用风险的时序演化规律，显著提升时间一致性与预测稳定性。