Abstract: With the rapid development of social economy,credit business plays an increasingly important role in the financial field,and using machine learning algorithms for credit evaluation has become the mainstream method.However,there are still some problems to be solved,such as the inadequacy of labeled data and model lag caused by delayed labels,and the lack of interpretability in dynamic credit evaluation models.To address these problems,this paper proposes an interpretable credit evaluation model for delayed label scenarios.Built upon the foundation of dynamic model trees,the model incorporates weighted enhancements.It combines delayed label update algorithms and a pseudo-label selection strategy with adaptive thresholds,treating delayed label data as both feedback data and pseudo-label data,effectively mitigating the impacts of insufficient labeled data and model lag.Moreover,the model achieves interpretability.It is finally tested on some synthetic and real credit evaluation datasets,demonstrating superior balance between predictive performance and interpretability compared to other mainstream algorithms.

Key words: Credit evaluation, Delayed label, Interpretability, Dynamic model tree, Pseudo-label selection