Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective

Abstract

Peer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge connecting managerial research with some most advanced natural language processing (NLP) technologies, and examines the risk assessing power of automatic learning text classifiers based on data of hazard status and top management team profile texts of the P2P-INTs. A risk evaluation model named MULTIPLE NLP Integrated Learning Text Classifier (MUN-LETCLA) based on five NLP techniques and meta-learning is proposed. Then risk classification power of the MUN-LETCLA and the single NLP models is assessed. The results show that the proposed model is effective in classifying low-risk and high-risk P2P-INTs. The NLP models can automatically detect the P2P-INTs risk from Top Management Team (TMT) members' working experience, educational background, and TMT composition with a precision level of more than 75%. INDEX TERMS Machine learning, natural language processing (NLP), peer-to-peer (P2P) network lending, risk evaluation, text analysis.

Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions

“…(2019) complements demographic and financial information with narrative data and include soft information related to loan description, the character of borrowers, and variables obtained from a clustering procedure on soft information. Li et al (2019), with advanced natural language processing (NLP) techniques, evaluates the risk of fraud associated with platforms in China. They use variables derived from management team members' working experience, educational background, and its composition.…”

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