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.

中文翻译：

---

用于检测 P2P 网络中债务违约的多层和并行连接图卷积网络

摘要

本文提出了一种多层并行连接的图卷积网络（MPGCNs）方法来探索债务人-债权人关系网络是否有助于检测点对点（P2P）借贷中的违约风险。结果表明：（1）债权债务关系网络反映了贷款人的风险偏好和借款人的成功贷款信息。(2) 提出的 MPGCNs 方法可以准确地检测违约风险。因此，考虑债权债务关系网络的结构有助于P2P借贷监管机构和政府监管机构控制风险。