Multi-layer and Parallel-connected Graph Convolutional Networks for Detecting Debt Default in P2P Networks

Shan Lu, Yu Wang, Xueyong Liu and Cheng Jiang

Emerging Markets Finance and Trade, 2022, vol. 58, issue 6, 1688-1701

Abstract: This paper presents a multilayer and parallel-connected graph convolutional networks (MPGCNs) method to explore whether a debtor–creditor relationship network helps to detect the default risk in peer-to-peer (P2P) lending. Results show that: (1) The debtor–creditor relationship network reflects lenders’ risk preference and borrowers’ successful loan information. (2) The proposed MPGCNs method can detect default risk accurately. Therefore, considering the structure of the debtor–creditor relationship network is helpful for P2P lending regulators and government supervisors to control risk.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/1540496X.2021.1921730 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:mes:emfitr:v:58:y:2022:i:6:p:1688-1701

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/MREE20

DOI: 10.1080/1540496X.2021.1921730

Access Statistics for this article

More articles in Emerging Markets Finance and Trade from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().