Genetic programming for the prediction of insolvency in non-life insurance companies

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

@Article{Salcedo-Sanz:2005:COR,
  author =       "Sancho Salcedo-Sanz and 
                 Jose-Luis Fernandez-Villacanas and Maria Jesus Segovia-Vargas and 
                 Carlos Bousono-Calzon",
  title =        "Genetic programming for the prediction of insolvency
                 in non-life insurance companies",
  journal =      "Computers \& Operations Research",
  year =         "2005",
  volume =       "32",
  pages =        "749--765",
  number =       "4",
  abstract =     "Prediction of non-life insurance companies insolvency
                 has arisen as an important problem in the field of
                 financial research, due to the necessity of protecting
                 the general public whilst minimising the costs
                 associated to this problem, such as the effects on
                 state insurance guaranty funds or the responsibilities
                 for management and auditors. Most methods applied in
                 the past to predict business failure in non-life
                 insurance companies are traditional statistical
                 techniques, which use financial ratios as explicative
                 variables. However, these variables do not usually
                 satisfy statistical assumptions, what complicates the
                 application of the mentioned methods. Emergent
                 statistical learning methods like neural networks or
                 SVMs provide a successful approach in terms of error
                 rate, but their character of black-box methods make the
                 obtained results difficult to be interpreted and
                 discussed. we propose an approach to predict insolvency
                 of non-life insurance companies based on the
                 application of genetic programming (GP). GP is a class
                 of evolutionary algorithms, which operates by codifying
                 the solution of the problem as a population of LISP
                 trees. This type of algorithm provides a diagnosis
                 output in the form of a decision tree with given
                 functions and data. We can treat it like a computer
                 program which returns an answer depending on the input,
                 and, more importantly, the tree can potentially be
                 inspected, interpreted and re-used for different data
                 sets. We have compared the performance of GP with other
                 classifiers approaches, a Support Vector Machine and a
                 Rough Set algorithm. The final purpose is to create an
                 automatic diagnostic system for analysing non-insurance
                 firms using their financial ratios as explicative
                 variables.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6VC5-49PYKV6-3/2/ea8a7b2d639b4cadb419cb9acf2a1352",
  keywords =     "genetic algorithms, genetic programming, Insolvency,
                 Non-life insurance companies, Support vector machines,
                 SVM, Rough set",
  DOI =          "doi:10.1016/j.cor.2003.08.015",
}

Genetic Programming entries for Sancho Salcedo-Sanz Jose-Luis Fernandez-Villacanas Martin Maria Jesus Segovia-Vargas Carlos Bousono-Calzon

Citations