Assessing Consumer Credit Applications by a Genetic Programming Approach

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

@InCollection{series/sci/RamponeFL13,
  author =       "Salvatore Rampone and Franco Frattolillo and 
                 Federica Landolfi",
  title =        "Assessing Consumer Credit Applications by a Genetic
                 Programming Approach",
  bibdate =      "2013-01-18",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/series/sci/sci448.html#RamponeFL13",
  booktitle =    "Advanced Dynamic Modeling of Economic and Social
                 Systems",
  publisher =    "Springer",
  year =         "2013",
  volume =       "448",
  editor =       "Araceli N. Proto and Massimo Squillante and 
                 Janusz Kacprzyk",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-32902-9",
  series =       "Studies in Computational Intelligence",
  pages =        "79--89",
  URL =          "http://dx.doi.org/10.1007/978-3-642-32903-6_7",
  DOI =          "doi:10.1007/978-3-642-32903-6_7",
  abstract =     "Credit scoring is the assessment of the risk
                 associated with lending to an organisation or an
                 individual. Genetic Programming is an evolutionary
                 computational technique that enables computers to solve
                 problems without being explicitly programmed. This
                 paper proposes a genetic programming approach for risk
                 assessment. In particular, the study is set in order to
                 predict, on a collection of real loan data, whether a
                 credit request has to be approved or rejected. The task
                 is to use existing data to develop rules for placing
                 new observations into one of a set of discrete groups.
                 The automation of such decision-making processes can
                 lead to savings in time and money by relieving the load
                 of work on an expert who would otherwise consider each
                 new case individually. The proposed model provides good
                 performance in terms of accuracy and error rate.",
}

Genetic Programming entries for Salvatore Rampone Franco Frattolillo Federica Landolfi

Citations