Network based credit risk models

Giudici, Paolo; Hadji Misheva, Branka; Spelta, Alessandro (2020). Network based credit risk models Quality Engineering, 32(2), pp. 199-211. Taylor & Francis 10.1080/08982112.2019.1655159

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Peer-to-Peer lending platforms may lead to cost reduction, and to an improved user experience. These improvements may come at the price of inaccurate credit risk measurements, which can hamper lenders and endanger the stability of a financial system. In the article, we propose how to improve credit risk accuracy of peer to peer platforms and, specifically, of those who lend to small and medium enterprises. To achieve this goal, we propose to augment traditional credit scoring methods with “alternative data” that consist of centrality measures derived from similarity networks among borrowers, deduced from their financial ratios. Our empirical findings suggest that the proposed approach improves predictive accuracy as well as model explainability.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for Applied Data Science & Finance Business School
Name:	Giudici, Paolo; Hadji Misheva, Branka and Spelta, Alessandro
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD61 Risk Management H Social Sciences > HG Finance
ISSN:	0898-2112
Publisher:	Taylor & Francis
Language:	English
Submitter:	Branka Hadji Misheva
Date Deposited:	17 Aug 2022 11:02
Last Modified:	17 Aug 2022 11:02
Publisher DOI:	10.1080/08982112.2019.1655159
Uncontrolled Keywords:	credit scoring models, network model, speer-to-peer lending
ARBOR DOI:	10.24451/arbor.17297
URI:	https://arbor.bfh.ch/id/eprint/17297