Variable reduction, sample selection bias and bank retail credit scoring
AbstractThis paper investigates the effect of including the customer loan approval process to the estimation of loan performance and explores the influence of sample selection bias in predicting the probability of default. The bootstrap variable reduction technique is applied to reduce the variable dimension for a large data-set drawn from a major UK retail bank. The results show a statistically significant correlation between the loan approval and performance processes. We further demonstrate an economically significant improvement in forecasting performance when taking into account sample selection bias. We conclude that financial institutions can obtain benefits by correcting for sample selection bias in their credit scoring models.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Empirical Finance.
Volume (Year): 17 (2010)
Issue (Month): 3 (June)
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Web page: http://www.elsevier.com/locate/jempfin
Bootstrap variable selection Credit scoring Loan performance forecasting Sample selection bias;
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