A systematic approach to construct credit risk forecast models

Detalhes bibliográficos
Autor(a) principal: Selau, Lisiane Priscila Roldão
Data de Publicação: 2011
Outros Autores: Ribeiro, Jose Luis Duarte
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Institucional da UFRGS
Texto Completo: http://hdl.handle.net/10183/129137
Resumo: Due to the recent growth in the consumer credit market and the consequent increase in default indices, companies are seeking to improve their credit analysis by incorporating objective procedures. Multivariate techniques have been used as an alternative to construct quantitative models for credit forecast. These techniques are based on consumer profile data and allow the identification of standards concerning default behavior. This paper presents a methodology for forecasting credit risk by using three multivariate techniques: discriminant analysis, logistic regression and neural networks. The proposed method (deemed the CRF Model) consists of six steps and is illustrated by means of a real application. An important contribution of this paper is the organization of the methodological procedures and the discussion of the decisions that should be made during the application of the model. The feasibility of the approach proposed was tested in a program for granting credit offered by a network of pharmacies. The use of the models for forecasting credit risk greatly reduces the subjectivity of the analysis, by establishing a standardized procedure that speeds up and qualifies credit analysis.
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spelling Selau, Lisiane Priscila RoldãoRibeiro, Jose Luis Duarte2015-11-09T16:26:51Z20110101-7438http://hdl.handle.net/10183/129137000775019Due to the recent growth in the consumer credit market and the consequent increase in default indices, companies are seeking to improve their credit analysis by incorporating objective procedures. Multivariate techniques have been used as an alternative to construct quantitative models for credit forecast. These techniques are based on consumer profile data and allow the identification of standards concerning default behavior. This paper presents a methodology for forecasting credit risk by using three multivariate techniques: discriminant analysis, logistic regression and neural networks. The proposed method (deemed the CRF Model) consists of six steps and is illustrated by means of a real application. An important contribution of this paper is the organization of the methodological procedures and the discussion of the decisions that should be made during the application of the model. The feasibility of the approach proposed was tested in a program for granting credit offered by a network of pharmacies. The use of the models for forecasting credit risk greatly reduces the subjectivity of the analysis, by establishing a standardized procedure that speeds up and qualifies credit analysis.application/pdfengPesquisa Operacional. Rio de Janeiro, RJ. Vol. 31, n. 1 (Jan./Apr. 2011), p. 41-56Modelos estatísticosEngenharia econômicaModelos de previsãoCredit riskRorecast modelCredit analysisA systematic approach to construct credit risk forecast modelsinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/otherinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSORIGINAL000775019.pdf000775019.pdfTexto completo (inglês)application/pdf494099http://www.lume.ufrgs.br/bitstream/10183/129137/1/000775019.pdf493ea30c30f0bd4a067d7053cf32766bMD51TEXT000775019.pdf.txt000775019.pdf.txtExtracted Texttext/plain46047http://www.lume.ufrgs.br/bitstream/10183/129137/2/000775019.pdf.txtb6db673a50f2ad44ebdf987268865c5eMD52THUMBNAIL000775019.pdf.jpg000775019.pdf.jpgGenerated Thumbnailimage/jpeg1671http://www.lume.ufrgs.br/bitstream/10183/129137/3/000775019.pdf.jpg3b22d924c01752c708ad94935491006cMD5310183/1291372023-07-08 03:35:26.597618oai:www.lume.ufrgs.br:10183/129137Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2023-07-08T06:35:26Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false
dc.title.pt_BR.fl_str_mv A systematic approach to construct credit risk forecast models
title A systematic approach to construct credit risk forecast models
spellingShingle A systematic approach to construct credit risk forecast models
Selau, Lisiane Priscila Roldão
Modelos estatísticos
Engenharia econômica
Modelos de previsão
Credit risk
Rorecast model
Credit analysis
title_short A systematic approach to construct credit risk forecast models
title_full A systematic approach to construct credit risk forecast models
title_fullStr A systematic approach to construct credit risk forecast models
title_full_unstemmed A systematic approach to construct credit risk forecast models
title_sort A systematic approach to construct credit risk forecast models
author Selau, Lisiane Priscila Roldão
author_facet Selau, Lisiane Priscila Roldão
Ribeiro, Jose Luis Duarte
author_role author
author2 Ribeiro, Jose Luis Duarte
author2_role author
dc.contributor.author.fl_str_mv Selau, Lisiane Priscila Roldão
Ribeiro, Jose Luis Duarte
dc.subject.por.fl_str_mv Modelos estatísticos
Engenharia econômica
Modelos de previsão
topic Modelos estatísticos
Engenharia econômica
Modelos de previsão
Credit risk
Rorecast model
Credit analysis
dc.subject.eng.fl_str_mv Credit risk
Rorecast model
Credit analysis
description Due to the recent growth in the consumer credit market and the consequent increase in default indices, companies are seeking to improve their credit analysis by incorporating objective procedures. Multivariate techniques have been used as an alternative to construct quantitative models for credit forecast. These techniques are based on consumer profile data and allow the identification of standards concerning default behavior. This paper presents a methodology for forecasting credit risk by using three multivariate techniques: discriminant analysis, logistic regression and neural networks. The proposed method (deemed the CRF Model) consists of six steps and is illustrated by means of a real application. An important contribution of this paper is the organization of the methodological procedures and the discussion of the decisions that should be made during the application of the model. The feasibility of the approach proposed was tested in a program for granting credit offered by a network of pharmacies. The use of the models for forecasting credit risk greatly reduces the subjectivity of the analysis, by establishing a standardized procedure that speeds up and qualifies credit analysis.
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dc.relation.ispartof.pt_BR.fl_str_mv Pesquisa Operacional. Rio de Janeiro, RJ. Vol. 31, n. 1 (Jan./Apr. 2011), p. 41-56
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