Genetic programming for credit scoring: The case of Egyptian public sector banks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Abdou200911402,
  author =       "Hussein A. Abdou",
  title =        "Genetic programming for credit scoring: The case of
                 Egyptian public sector banks",
  journal =      "Expert Systems with Applications",
  volume =       "36",
  number =       "9",
  pages =        "11402--11417",
  year =         "2009",
  ISSN =         "0957-4174",
  DOI =          "doi:10.1016/j.eswa.2009.01.076",
  URL =          "http://www.sciencedirect.com/science/article/B6V03-4VJSRWK-1/2/a3b8516f289c76c474c6a1eb9d26d7ec",
  URL =          "http://results.ref.ac.uk/Submissions/Output/2691591",
  keywords =     "genetic algorithms, genetic programming, Credit
                 scoring, Weight of evidence, Egyptian public sector
                 banks",
  abstract =     "Credit scoring has been widely investigated in the
                 area of finance, in general, and banking sectors, in
                 particular. Recently, genetic programming (GP) has
                 attracted attention in both academic and empirical
                 fields, especially for credit problems. The primary aim
                 of this paper is to investigate the ability of GP,
                 which was proposed as an extension of genetic
                 algorithms and was inspired by the Darwinian evolution
                 theory, in the analysis of credit scoring models in
                 Egyptian public sector banks. The secondary aim is to
                 compare GP with probit analysis (PA), a successful
                 alternative to logistic regression, and weight of
                 evidence (WOE) measure, the later a neglected technique
                 in published research. Two evaluation criteria are used
                 in this paper, namely, average correct classification
                 (ACC) rate criterion and estimated misclassification
                 cost (EMC) criterion with different misclassification
                 cost (MC) ratios, in order to evaluate the capabilities
                 of the credit scoring models. Results so far revealed
                 that GP has the highest ACC rate and the lowest EMC.
                 However, surprisingly, there is a clear rule for the
                 WOE measure under EMC with higher MC ratios. In
                 addition, an analysis of the dataset using Kohonen maps
                 is undertaken to provide additional visual insights
                 into cluster groupings.",
  uk_research_excellence_2014 = "D - Journal article",
}

Genetic Programming entries for Hussein A Abdou

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