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How to deal with extreme cases for credit risk monitoring: a case study in a credit risk data science company

datacite.subject.fosCiências Sociais::Economia e Gestãopt_PT
dc.contributor.advisorEça, Afonso Fuzeta
dc.contributor.authorFernandes, Sebastião Cardoso
dc.date.accessioned2018-04-27T14:18:25Z
dc.date.available2021-01-20T01:30:26Z
dc.date.issued2018-01-20
dc.description.abstractThe Global Financial Crisis triggered a severe hold on credit lending due to the financial institutions’ inability to assess credit applicants risk levels properly. Based on U.S. data from Lending Club, we conducted a study to evaluate the consequences of including macroeconomic risk factors in individual credit application observations. Through historical scenario stress testing, we find that this approach results in an increase in performance for credit scoring models developed in a stable economic cycle and applied to a recession. The inclusion of macroeconomic indicators reveals potential for credit institutions to better absorb shocks derived from economic downturns.pt_PT
dc.identifier.tid201861623pt_PT
dc.identifier.urihttp://hdl.handle.net/10362/35455
dc.language.isoengpt_PT
dc.subjectCredit scoringpt_PT
dc.subjectClassificationpt_PT
dc.subjectConsumer loanspt_PT
dc.subjectFinancial stabilitypt_PT
dc.subjectStress testpt_PT
dc.subjectMacroeconomic scenariospt_PT
dc.titleHow to deal with extreme cases for credit risk monitoring: a case study in a credit risk data science companypt_PT
dc.typemaster thesis
dspace.entity.typePublication
rcaap.rightsopenAccesspt_PT
rcaap.typemasterThesispt_PT
thesis.degree.nameA Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economicspt_PT

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