Non-parametric Statistical Analysis of Machine Learning Methods for Credit Scoring

  • V. García
  • A. I. Marqués
  • J. S. Sánchez
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 171)


Various machine learning techniques have been explored for credit scoring and management, but no consistent conclusions have been drawn on which method shows the best behaviour. This paper presents an experimental analysis involving five real-world databases with several credit scoring models, including logistic regression, neural networks, support vector machines, decision trees, rule induction algorithms, Bayesian models, k nearest neighbours decision rule, and classifier ensembles. Particularly, we analyse the performance of this set of algorithms by means of a non-parametric statistical test and two post-hoc procedures for making pairwise comparisons.


Support Vector Machine Radial Basis Function Random Forest Credit Risk Radial Basis Function Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Institute of New Imaging Technologies, Department of Computer Languages and SystemsUniversitat Jaume ICastelló de la PlanaSpain
  2. 2.Department of Business Administration and MarketingUniversitat Jaume ICastelló de la PlanaSpain

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