This study employs machine learning models to predict the failure of Peer-to-Peer (P2P) lending platforms, specifically in China. By employing the filter method and wrapper method with forward selection and backward elimination, we establish a rigorous and practical procedure that ensures the robustness and importance of variables in predicting platform failures. The research identifies a set of robust variables that consistently appear in the feature subsets across different selection methods and models, suggesting their reliability and relevance in predicting platform failures. The study highlights that reducing the number of variables in the feature subset leads to an increase in the false acceptance rate while the performance metrics remain stable, with an AUC value of approximately 0.96 and an F1 score of around 0.88. The findings of this research provide significant practical implications for regulatory authorities and investors operating in the Chinese P2P lending industry.

利用机器学习模型预测中国的P2P借贷平台的失败，通过筛选方法和包装方法，建立了一个严格而实用的预测平台失败的变量的程序。研究确定了一组在不同选择方法和模型中一致出现在特征子集中的可靠变量，表明它们在预测平台失败方面的可靠性和相关性。本研究的发现对于在中国P2P借贷行业中进行监管的机构和投资者具有重要的实际意义。

通过机器学习预测P2P借贷平台的失败：中国案例