  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.
