Predicting failure of P2P lending platforms through machine learning: The case in China

Jen Yin Yeh, Hsin Yu Chiu, Jhih Huei Huang

研究成果: 雜誌貢獻期刊論文同行評審

3 引文 斯高帕斯(Scopus)

摘要

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.

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文章編號104784
期刊Finance Research Letters
59
DOIs
出版狀態已出版 - 1月 2024

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