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Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-23433
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dc.contributor.authorAhelegbey, Daniel Felix-
dc.contributor.authorGiudici, Paolo-
dc.contributor.authorHadji Misheva, Branka-
dc.date.accessioned2021-11-10T08:09:11Z-
dc.date.available2021-11-10T08:09:11Z-
dc.date.issued2019-
dc.identifier.issn2624-8212de_CH
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/23433-
dc.description.abstractThis paper investigates how to improve statistical-based credit scoring of SMEs involved in P2P lending. The methodology discussed in the paper is a factor network-based segmentation for credit score modeling. The approach first constructs a network of SMEs where links emerge from comovement of latent factors, which allows us to segment the heterogeneous population into clusters. We then build a credit score model for each cluster via lasso-type regularization logistic regression. We compare our approach with the conventional logistic model by analyzing the credit score of over 1,5000 SMEs engaged in P2P lending services across Europe. The result reveals that credit risk modeling using our network-based segmentation achieves higher predictive performance than the conventional model.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.subjectFinTechde_CH
dc.subjectCredit riskde_CH
dc.subjectCredit scoringde_CH
dc.subjectFactor modelsde_CH
dc.subjectLassode_CH
dc.subjectPeer-to-peer lendingde_CH
dc.subjectSegmentationde_CH
dc.subject.ddc332: Finanzwirtschaftde_CH
dc.titleFactorial network models to improve P2P credit risk managementde_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.2019.00008de_CH
dc.identifier.doi10.21256/zhaw-23433-
dc.identifier.pmid33733097de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.start8de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.volume2de_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). Factorial network models to improve P2P credit risk management. Frontiers in Artificial Intelligence, 2, 8. https://doi.org/10.3389/frai.2019.00008
Ahelegbey, D.F., Giudici, P. and Hadji Misheva, B. (2019) ‘Factorial network models to improve P2P credit risk management’, Frontiers in Artificial Intelligence, 2, p. 8. Available at: https://doi.org/10.3389/frai.2019.00008.
D. F. Ahelegbey, P. Giudici, and B. Hadji Misheva, “Factorial network models to improve P2P credit risk management,” Frontiers in Artificial Intelligence, vol. 2, p. 8, 2019, doi: 10.3389/frai.2019.00008.
AHELEGBEY, Daniel Felix, Paolo GIUDICI und Branka HADJI MISHEVA, 2019. Factorial network models to improve P2P credit risk management. Frontiers in Artificial Intelligence. 2019. Bd. 2, S. 8. DOI 10.3389/frai.2019.00008
Ahelegbey, Daniel Felix, Paolo Giudici, and Branka Hadji Misheva. 2019. “Factorial Network Models to Improve P2P Credit Risk Management.” Frontiers in Artificial Intelligence 2: 8. https://doi.org/10.3389/frai.2019.00008.
Ahelegbey, Daniel Felix, et al. “Factorial Network Models to Improve P2P Credit Risk Management.” Frontiers in Artificial Intelligence, vol. 2, 2019, p. 8, https://doi.org/10.3389/frai.2019.00008.


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