Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning

Liu, Yiting; Baals, Lennart John; Osterrieder, Jörg Robert; Hadji Misheva, Branka (2024). Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning Expert Systems with Applications, 252, p. 124100. Elsevier 10.1016/j.eswa.2024.124100

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Peer-to-Peer (P2P) lending markets have witnessed remarkable growth, revolutionizing the way borrowers and lenders interact. Despite the increasing popularity of P2P lending, it 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 capable to capture latent factor information inherent to a loan network based on similarity distances. Thus, in this study we propose an enhanced two-step modeling approach for Machine Learning (ML) that 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 default prediction process. Through a comparative analysis of three classical ML models with varying degrees of complexity, namely Elastic Net (EN), Random Forest (RF), and Multi-Layer Perceptron (MLP), we showcase novel evidence that the systematic inclusion of network topology features in the credit scoring process can significantly improve the prediction accuracy of the scoring models. Additional robustness tests via the inclusion of randomly shuffled centrality metrics in the analysis, and a further comparison of the graph-based models against a pertinent state-of-the-art credit scoring model in form of XGBoost, further confirm our results. The insights from this study bear valuable conclusions for P2P lending platforms to further improve their scoring systems with graph-enhanced metrics, thereby reducing default risk and facilitating greater access to credit.

Item Type:	Journal Article (Original Article)
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, Yiting0009-0006-9554-8205; Baals, Lennart John; Osterrieder, Jörg Robert0000-0003-0189-8636 and Hadji Misheva, Branka
Subjects:	H Social Sciences > HG Finance
ISSN:	0957-4174
Publisher:	Elsevier
Funders:	[UNSPECIFIED] European Cooperation in Science and Technology (COST) ; [7] Swiss National Science Foundation ; [UNSPECIFIED] Marie Skodowska-Curie Actions
Language:	English
Submitter:	Yiting Liu
Date Deposited:	04 Jun 2024 16:37
Last Modified:	09 Jun 2024 01:40
Publisher DOI:	10.1016/j.eswa.2024.124100
ARBOR DOI:	10.24451/arbor.21961
URI:	https://arbor.bfh.ch/id/eprint/21961