Liu, Yiting; Baals, Lennart John; Osterrieder, Jörg Robert; Hadji Misheva, Branka (2024). Network centrality and credit risk: A comprehensive analysis of peer-to-peer lending dynamics Finance Research Letters, 63, p. 105308. Elsevier 10.1016/j.frl.2024.105308
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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.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
Business School > Institute for Applied Data Science & Finance Business School > Institute for Applied Data Science & Finance > Finance, Accounting and Tax Business School |
Name: |
Liu, Yiting![]() Baals, Lennart John; Osterrieder, Jörg Robert ![]() Hadji Misheva, Branka |
Subjects: |
H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HG Finance |
ISSN: |
15446123 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Yiting Liu |
Date Deposited: |
03 May 2024 11:42 |
Last Modified: |
07 May 2024 16:21 |
Publisher DOI: |
10.1016/j.frl.2024.105308 |
Uncontrolled Keywords: |
Peer-to-Peer lending Credit-default prediction Machine Learning Network centrality |
ARBOR DOI: |
10.24451/arbor.21851 |
URI: |
https://arbor.bfh.ch/id/eprint/21851 |