Network Centrality and Credit Risk: A Comprehensive Analysis of Peer-to-Peer Lending Dynamics

Abstract

This letter delves into credit risk assessment within the Peer-to-Peer (P2P) lending domain by leveraging a com- prehensive dataset from Bondora, a leading European P2P platform. Through combining traditional credit features with network topological features, namely degree centrality, we showcase the crucial role of a borrower’s position and connectivity within the P2P network in determining loan default probabilities. Our findings are bolstered by robustness checks using shuffled centrality features, which further underscore the significance of integrating both financial and network attributes in credit risk evaluation. Our results shed new light on credit risk determinants in P2P lending and benefit investors in capturing inherent information from P2P loan networks.

Keywords: P2P-lending, Credit-Default Prediction, Machine Learning (ML), Network Centrality

JEL Classification: G00, G1, G12, G14, G02, G4