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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-19483
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dc.contributor.authorGiudici, Paolo-
dc.contributor.authorHadji Misheva, Branka-
dc.contributor.authorSpelta, Alessandro-
dc.date.accessioned2020-02-19T14:19:21Z-
dc.date.available2020-02-19T14:19:21Z-
dc.date.issued2019-
dc.identifier.issn2624-8212de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/19483-
dc.description.abstractFinancial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models.de_CH
dc.language.isoende_CH
dc.publisherFrontiers Research Foundationde_CH
dc.relation.ispartofFrontiers in Artificial Intelligencede_CH
dc.rightshttp://creativecommons.org/licenses/by/4.0/de_CH
dc.subjectContagionde_CH
dc.subjectCredit riskde_CH
dc.subjectCredit scoringde_CH
dc.subjectNetwork modelde_CH
dc.subjectPeer to peer lendingde_CH
dc.subject.ddc004: Informatikde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleNetwork based scoring models to improve credit risk management in peer to peer lending platformsde_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.doi10.3389/frai.2019.00003de_CH
dc.identifier.doi10.21256/zhaw-19483-
zhaw.funding.euNode_CH
zhaw.issue3de_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.author.additionalNode_CH
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Giudici, P., Hadji Misheva, B., & Spelta, A. (2019). Network based scoring models to improve credit risk management in peer to peer lending platforms. Frontiers in Artificial Intelligence, 2(3). https://doi.org/10.3389/frai.2019.00003
Giudici, P., Hadji Misheva, B. and Spelta, A. (2019) ‘Network based scoring models to improve credit risk management in peer to peer lending platforms’, Frontiers in Artificial Intelligence, 2(3). Available at: https://doi.org/10.3389/frai.2019.00003.
P. Giudici, B. Hadji Misheva, and A. Spelta, “Network based scoring models to improve credit risk management in peer to peer lending platforms,” Frontiers in Artificial Intelligence, vol. 2, no. 3, 2019, doi: 10.3389/frai.2019.00003.
GIUDICI, Paolo, Branka HADJI MISHEVA und Alessandro SPELTA, 2019. Network based scoring models to improve credit risk management in peer to peer lending platforms. Frontiers in Artificial Intelligence. 2019. Bd. 2, Nr. 3. DOI 10.3389/frai.2019.00003
Giudici, Paolo, Branka Hadji Misheva, and Alessandro Spelta. 2019. “Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms.” Frontiers in Artificial Intelligence 2 (3). https://doi.org/10.3389/frai.2019.00003.
Giudici, Paolo, et al. “Network Based Scoring Models to Improve Credit Risk Management in Peer to Peer Lending Platforms.” Frontiers in Artificial Intelligence, vol. 2, no. 3, 2019, https://doi.org/10.3389/frai.2019.00003.


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