Modelling consumer credit risk

82 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Data mining for discrimination discovery (2010, background): “For instance, a large body of literature [Baesens et al. 2003; Hand 2001; Hand and Henley 1997; Thomas 2000; Viaene et al. 2001; Vojtek and Kočenda 2006] refers to classification models as the basis of scoring systems to predict the reliability of a mortgage/credit card debtor or the risk of taking ”
Classification methods applied to credit scoring: A systematic review and overall comparison (2016, methodology): “Datasets which contain confidential information on applicants cannot be released to third parties without careful safeguards (Hand, 2001b).”
Classifier Technology and the Illusion of Progress (2006, unknown): “Changes to the distributions that describe the customers explain why, in the credit scoring and banking industries [16, 20, 39, 42], the classification rules used to predict which applicants are likely to default on loans are updated every few months: their performance degrades, not because the rule”
A Two-step Method to Construct Credit Scoring Models with Data Mining Techniques (2006, background): “One of the major problems that can arise when constructing a credit scoring model is that the model may be built using a biased sample of consumers and customers who have been granted credit [Hand, 2001].”