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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23432
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dc.contributor.authorGiudici, Paolo-
dc.contributor.authorHadji Misheva, Branka-
dc.contributor.authorSpelta, Alessandro-
dc.date.accessioned2021-11-10T08:08:20Z-
dc.date.available2021-11-10T08:08:20Z-
dc.date.issued2019-
dc.identifier.issn0898-2112de_CH
dc.identifier.issn1532-4222de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23432-
dc.description.abstractPeer-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 toaugment traditional credit scoring methods with “alternative data” that consist of centralitymeasures derived from similarity networks among borrowers, deduced from their financialratios. Our empirical findings suggest that the proposed approach improves predictiveaccuracy as well as model explainability.de_CH
dc.language.isoende_CH
dc.publisherTaylor & Francisde_CH
dc.relation.ispartofQuality Engineeringde_CH
dc.rightshttp://creativecommons.org/licenses/by-nc-nd/4.0/de_CH
dc.subjectCredit scoring modelde_CH
dc.subjectNetwork modelde_CH
dc.subjectPeer-to-peer lendingde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleNetwork based credit risk modelsde_CH
dc.typeBeitrag in wissenschaftlicher Zeitschriftde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
dc.identifier.doi10.1080/08982112.2019.1655159de_CH
dc.identifier.doi10.21256/zhaw-23432-
zhaw.funding.euNode_CH
zhaw.issue2de_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end211de_CH
zhaw.pages.start199de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume32de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedFinTechde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Giudici, P., Hadji Misheva, B., & Spelta, A. (2019). Network based credit risk models. Quality Engineering, 32(2), 199–211. https://doi.org/10.1080/08982112.2019.1655159
Giudici, P., Hadji Misheva, B. and Spelta, A. (2019) ‘Network based credit risk models’, Quality Engineering, 32(2), pp. 199–211. Available at: https://doi.org/10.1080/08982112.2019.1655159.
P. Giudici, B. Hadji Misheva, and A. Spelta, “Network based credit risk models,” Quality Engineering, vol. 32, no. 2, pp. 199–211, 2019, doi: 10.1080/08982112.2019.1655159.
GIUDICI, Paolo, Branka HADJI MISHEVA und Alessandro SPELTA, 2019. Network based credit risk models. Quality Engineering. 2019. Bd. 32, Nr. 2, S. 199–211. DOI 10.1080/08982112.2019.1655159
Giudici, Paolo, Branka Hadji Misheva, and Alessandro Spelta. 2019. “Network Based Credit Risk Models.” Quality Engineering 32 (2): 199–211. https://doi.org/10.1080/08982112.2019.1655159.
Giudici, Paolo, et al. “Network Based Credit Risk Models.” Quality Engineering, vol. 32, no. 2, 2019, pp. 199–211, https://doi.org/10.1080/08982112.2019.1655159.


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