Influencing factor analysis of credit risk in P2P lending based on interpretative structural modeling. (3rd May 2016)

• Record Type: Journal Article
• Authors: Ma, Hui-Zi; Wang, Xiang-Rong
• Is Part Of: Journal of discrete mathematical sciences & cryptography. Volume 19:Number 3(2016)
• Page Start: 777
• Page End: 786
• Publication Date: 2016-05-03
• Subjects: Influencing factors -- P2P -- Credit risk -- Interpretative structural modeling; Computer science -- Mathematics -- Periodicals; Cryptography -- Periodicals; Computer science -- Mathematics; Cryptography; Periodicals; 004.0151
• DOI: 10.1080/09720529.2016.1178935
• Languages: English
• ISSNs: 0972-0529

Abstract

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.