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Master's Dissertation
DOI
10.11606/D.45.2008.tde-14102008-204609
Document
Author
Full name
Mauro Correia Alves
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2008
Supervisor
Committee
Barroso, Lúcia Pereira (President)
Aranha Filho, Francisco José Espósito
Lima, Antonio Carlos Pedroso de
Title in Portuguese
Estratégias para o desenvolvimento de modelos de credit score com inferência de rejeitados.
Keywords in Portuguese
credit score
inferência de rejeitados
regresão logística.
risco de crédito
Abstract in Portuguese
Modelos de credit score são usualmente desenvolvidos somente com informações dos proponentes aceitos. Neste trabalho foram consideradas estratégias que podem ser utilizadas para o desenvolvimento de modelos de credit score com a inclusão das informações dos rejeitados. Foram avaliadas as seguintes técnicas de inferência de rejeitados: classificação dos rejeitados como clientes Maus, parcelamento, dados aumentados, uso de informações de mercado e ainda a estratégia de aceitar proponentes rejeitados para acompanhamento e desenvolvimento de novos modelos de risco de crédito. Para a avaliação e comparação dos modelos foram utilizadas as medidas de desempenho: estatística de Kolmogorov-Smirnov (KS), área sob a curva de Lorentz (ROC), área entre as curvas de distribuição acumulada dos escores (AEC), diferença entre as taxas de inadimplência nos intervalos do escore definidos pelos decis e coeficiente de Gini. Concluiu-se que dentre as quatro primeiras técnicas avaliadas, o uso de informaçõoes de mercado foi a que apresentou melhor desempenho. Quanto à estratégia de aceitar proponentes rejeitados, observou-se que há um ganho em relação ao modelo ajustado só com base nos proponentes aceitos.
Title in English
Strategies for the development of credit score with the inference rejected
Keywords in English
credit risk
credit score
logistic regression.
reject inference
Abstract in English
Credit scoring models are usually built using only information of accepted applicants. This text considered strategies that can be used to develop credit score models with inclusion of the information of the rejects. We evaluated the techniques of reject inference: classification of rejected customers as bad, parceling, augmentation, use of market information and the strategy of accepting rejected proponents for monitoring and developing new models of credit risk. For the evaluation and comparison between models were used performance measures: Kolmogorov-Smirnov statistics (KS), the area under the Lorentz Curve (ROC), area between cumulative distribution curves of the scores (AEC), difference among the delinquency rate in the score buckets based on deciles (DTI) and the Gini coefficient. We concluded that among the first four techniques evaluated, the fourth (use of market information) had the best performance. For the strategy to accept rejected bidders, it was observed that there is a gain in relation to the model that uses only information of accepted applicants.
 
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DissertacaoMauro.pdf (996.95 Kbytes)
Publishing Date
2008-12-11
 
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