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  2. School of Engineering
  3. Publikationen
  4. Publikationen School of Engineering
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dc.contributor.authorAhelegbey, Daniel Felix-
dc.contributor.authorGiudici, Paolo-
dc.contributor.authorHadji Misheva, Branka-
dc.date.accessioned2021-11-11T11:22:53Z-
dc.date.available2021-11-11T11:22:53Z-
dc.date.issued2019-
dc.identifier.issn0378-4371de_CH
dc.identifier.issn1873-2119de_CH
dc.identifier.urihttps://mpra.ub.uni-muenchen.de/id/eprint/92636de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23448-
dc.description.abstractPeer-to-Peer (P2P) FinTech platforms allow cost reduction and service improvement in credit lending. However, these improvements may come at the price of a worse credit risk measurement, and this can hamper lenders and endanger the stability of a financial system. We approach the problem of credit risk for Peer-to-Peer (P2P) systems by presenting a latent factor-based classification technique to divide the population into major network communities in order to estimate a more efficient logistic model. Given a number of attributes that capture firm performances in a financial system, we adopt a latent position model which allow us to distinguish between communities of connected and not-connected firms based on the spatial position of the latent factors. We show through empirical illustration that incorporating the latent factor-based classification of firms is particularly suitable as it improves the predictive performance of P2P scoring models.de_CH
dc.language.isoende_CH
dc.publisherElsevierde_CH
dc.relation.ispartofPhysica A: Statistical Mechanics and its Applicationsde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectCredit riskde_CH
dc.subjectFactor modelde_CH
dc.subjectFinancial technologyde_CH
dc.subjectScoring modelde_CH
dc.subjectSpatial clusteringde_CH
dc.subjectPeer-to-peerde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleLatent factor models for credit scoring in P2P systemsde_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.doi10.1016/j.physa.2019.01.130de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end121de_CH
zhaw.pages.start112de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume522de_CH
zhaw.publication.reviewPeer review (Publikation)de_CH
zhaw.webfeedFinTechde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitYesde_CH
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Ahelegbey, D. F., Giudici, P., & Hadji Misheva, B. (2019). Latent factor models for credit scoring in P2P systems. Physica A: Statistical Mechanics and Its Applications, 522, 112–121. https://doi.org/10.1016/j.physa.2019.01.130
Ahelegbey, D.F., Giudici, P. and Hadji Misheva, B. (2019) ‘Latent factor models for credit scoring in P2P systems’, Physica A: Statistical Mechanics and its Applications, 522, pp. 112–121. Available at: https://doi.org/10.1016/j.physa.2019.01.130.
D. F. Ahelegbey, P. Giudici, and B. Hadji Misheva, “Latent factor models for credit scoring in P2P systems,” Physica A: Statistical Mechanics and its Applications, vol. 522, pp. 112–121, 2019, doi: 10.1016/j.physa.2019.01.130.
AHELEGBEY, Daniel Felix, Paolo GIUDICI und Branka HADJI MISHEVA, 2019. Latent factor models for credit scoring in P2P systems. Physica A: Statistical Mechanics and its Applications [online]. 2019. Bd. 522, S. 112–121. DOI 10.1016/j.physa.2019.01.130. Verfügbar unter: https://mpra.ub.uni-muenchen.de/id/eprint/92636
Ahelegbey, Daniel Felix, Paolo Giudici, and Branka Hadji Misheva. 2019. “Latent Factor Models for Credit Scoring in P2P Systems.” Physica A: Statistical Mechanics and Its Applications 522: 112–21. https://doi.org/10.1016/j.physa.2019.01.130.
Ahelegbey, Daniel Felix, et al. “Latent Factor Models for Credit Scoring in P2P Systems.” Physica A: Statistical Mechanics and Its Applications, vol. 522, 2019, pp. 112–21, https://doi.org/10.1016/j.physa.2019.01.130.


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