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

Abstract

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.