Variable reduction, sample selection bias and bank retail credit scoring

A.P. Marshall, L. Tang, Alistair Milne

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


This 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 dataset 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.
Original languageEnglish
Pages (from-to)501–512
Number of pages12
JournalJournal of Empirical Finance
Issue number3
Publication statusPublished - Jun 2010


  • bootstrap variable selection
  • credit scoring
  • loan performance forecasting
  • sample selection bias


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