Strategies for the development of credit score with the inference rejected / Estratégias para o desenvolvimento de modelos de credit score com inferência de rejeitados.

AUTOR(ES)

Mauro Correia Alves

DATA DE PUBLICAÇÃO

2008

RESUMO

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.

ASSUNTO(S)

inferência de rejeitados regresão logística. reject inference credit score credit score risco de crédito logistic regression. credit risk

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