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Defining attributes for scorecard construction in credit scoring


  • David Hand
  • Niall Adams


In many domains, simple forms of classification rules are needed because of requirements such as ease of use. A particularly simple form splits each variable into just a few categories, assigns weights to the categories, sums the weights for a new object to be classified, and produces a classification by comparing the score with a threshold. Such instruments are often called scorecards. We describe a way to find the best partition of each variable using a simulated annealing strategy. We present theoretical and empirical comparisons of two such additive models, one based on weights of evidence and another based on logistic regression.

Suggested Citation

  • David Hand & Niall Adams, 2000. "Defining attributes for scorecard construction in credit scoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(5), pages 527-540.
  • Handle: RePEc:taf:japsta:v:27:y:2000:i:5:p:527-540
    DOI: 10.1080/02664760050076371

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    Cited by:

    1. Robert Till & David Hand, 2003. "Behavioural models of credit card usage," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1201-1220.
    2. Izabela Majer, 2006. "Application scoring: logit model approach and the divergence method compared," Working Papers 17, Department of Applied Econometrics, Warsaw School of Economics.

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