Influencing factor analysis of credit risk in P2P lending based on interpretative structural modeling

Abstract

Managing and preventing the credit risk in Peer-to-Peer lending has become a crucial problem in China’s internet finance market. The influencing factors of the credit risk in P2P lending are identified from three aspects including P2P lending platform, borrowers and environment. The internal relation between these influencing factors is also explored by using the method of Interpretative Structural Modeling. Results show that factors such as the audit mechanism of P2P lending platform could affect the credit risk in P2P lending directly. In addition, borrowers’ moral level, their job stability, and the policy environment will affect the credit risk in P2P lending comprehensively through influencing other factors.

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