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Data representativeness problem in credit scoring

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  • Josef Ditrich

Abstract

When building models, it is common to split the whole dataset into a development and a validation sample. In some cases, using random sampling instead of stratified sampling can lead to loss of representativeness of final samples. In such cases, a model built on these data gives different or unexpected results when its performance is measured on the validation sample. In the business area, a lack of representativeness can cause interpretative problems and can have a huge financial impact when a biased model is involved in the credit granting process. The aim of this paper is to examine and understand why representativeness should be checked before the start of modelling. The paper deals with methods of identification of selection bias in time. It recommends using three tests as a common part of the data preparation process.

Suggested Citation

  • Josef Ditrich, 2015. "Data representativeness problem in credit scoring," Acta Oeconomica Pragensia, Prague University of Economics and Business, vol. 2015(3), pages 3-17.
    • Handle: RePEc:prg:jnlaop:v:2015:y:2015:i:3:id:472:p:3-17
    DOI: 10.18267/j.aop.472
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    References listed on IDEAS

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    1. Donald W. K. Andrews, 2000. "Inconsistency of the Bootstrap when a Parameter Is on the Boundary of the Parameter Space," Econometrica, Econometric Society, vol. 68(2), pages 399-406, March.
    2. Xiao-Li Meng & Xianchao Xie, 2014. "I Got More Data, My Model is More Refined, but My Estimator is Getting Worse! Am I Just Dumb?," Econometric Reviews, Taylor & Francis Journals, vol. 33(1-4), pages 218-250, June.
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    More about this item

    Keywords

    credit scoring; credit risk models; selection bias; random sampling; stratified sampling; data splitting;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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