Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics

Yiting Liu, Lennart John Baals*, Jörg Osterrieder, Branka Hadji-Misheva

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
11 Downloads (Pure)

Abstract

This letter analyzes credit risk assessment in the Peer-to-Peer (P2P) lending domain by leveraging a comprehensive dataset from Bondora, a leading European P2P platform. Through combining traditional credit features with network topological features, namely the 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.

Original languageEnglish
Article number105308
Number of pages11
JournalFinance Research Letters
Volume63
Early online date26 Mar 2024
DOIs
Publication statusPublished - May 2024

Keywords

  • Credit-default prediction
  • Machine Learning
  • Network centrality
  • Peer-to-Peer lending

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