Towards a Comprehensible and Accurate Credit Management Model: Application of Four Computational Intelligence Methodologies

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  author =       "Athanasios Tsakonas and Nikolaos Ampazis and 
                 Georgios Dounias",
  title =        "Towards a Comprehensible and Accurate Credit
                 Management Model: Application of Four Computational
                 Intelligence Methodologies",
  booktitle =    "2006 International Symposium on Evolving Fuzzy
  year =         "2006",
  month =        sep,
  pages =        "295--299",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, applicant
                 classification, banking, computational intelligence,
                 credit management model, credit risk, feedforward
                 neural networks, fuzzy rule based systems,
                 grammar-guided genetic programming, hierarchical
                 decision trees, inductive machine learning, rule-based
                 categorization, second order methods, bank data
                 processing, decision trees, feed forward neural nets,
                 fuzzy systems, grammars, learning by example, risk
  URL =          "",
  DOI =          "doi:10.1109/ISEFS.2006.251142",
  bibsource =    "OAI-PMH server at",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:",
  abstract =     "The paper presents methods for classification of
                 applicants into different categories of credit risk
                 using four different computational intelligence
                 techniques. The selected methodologies involved in the
                 rule-based categorization task are (1) feed forward
                 neural networks trained with second order methods (2)
                 inductive machine learning, (3) hierarchical decision
                 trees produced by grammar-guided genetic programming
                 and (4) fuzzy rule based systems produced by
                 grammar-guided genetic programming. The data used are
                 both numerical and linguistic in nature and they
                 represent a real-world problem, that of deciding
                 whether a loan should be granted or not, in respect to
                 financial details of customers applying for that loan,
                 to a specific private EU bank. We examine the proposed
                 classification models with a sample of enterprises that
                 applied for a loan, each of which is described by
                 financial decision variables (ratios), and classified
                 to one of the four predetermined classes. Attention is
                 given to the comprehensibility and the ease of use for
                 the acquired decision models. Results show that the
                 application of the proposed methods can make the
                 classification task easier and - in some cases - may
                 minimize significantly the amount of required credit
                 data. We consider that these methodologies may also
                 give the chance for the extraction of a comprehensible
                 credit management model or even the incorporation of a
                 related decision support system in banking",
  notes =        "also known as \cite{4016706}",

Genetic Programming entries for Athanasios D Tsakonas Nikolaos Ampazis Georgios Dounias