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.

翻译：本研究采用机器学习模型预测点对点（P2P）借贷平台的倒闭现象，尤其聚焦中国情境。通过结合过滤法与基于前向选择与后向消除的包装法，我们建立了一套严谨且实用的流程，以确保变量在预测平台倒闭中的稳健性与重要性。研究识别出一组在不同选择方法与模型下始终出现在特征子集中的稳健变量，这表明其在预测平台倒闭方面具有可靠性与相关性。研究强调，减少特征子集中的变量数量会导致误接受率上升，但性能指标保持稳定，AUC值约为0.96，F1得分约为0.88。本研究的发现为中国P2P借贷行业的监管机构与投资者提供了重要的实践启示。