A1183
Title: Leveraging network topology for credit risk assessment in P2P lending
Authors: Yiting Liu - Bern University of Applied Science (Switzerland) [presenting]
Lennart John Baals - Bern University of Applied Science (Switzerland)
Branka Hadji Misheva - BFH (Switzerland)
Joerg Osterrieder - Bern Business School and University of Twente (Switzerland)
Abstract: Peer-to-Peer (P2P) lending markets have witnessed remarkable growth, revolutionizing the way borrowers and lenders interact. Despite their increasing popularity, P2P lending poses significant challenges related to credit risk assessment and default prediction with meaningful implications for financial stability. Traditional credit risk models have been widely employed in the field of P2P lending; however, they may not be fully capable to capture the complexity of the loan networks and the nuances of borrower behavior that are specifically evident in P2P lending markets. Thus, an enhanced two-step machine learning (ML) approach is proposed, which first utilizes insights from network analysis and subsequently combines derived network centrality metrics with traditional credit risk factors to improve the prediction accuracy in the credit risk modelling process.
