A genetic programming approach for fraud detection in electronic transactions

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Carlos A. S. Assis and Adriano C. M. Pereira and 
                 Marconi A. Pereira and Eduardo G. Carrano",
  booktitle =    "IEEE Symposium on Computational Intelligence in Cyber
                 Security (CICS 2014)",
  title =        "A genetic programming approach for fraud detection in
                 electronic transactions",
  year =         "2014",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CICYBS.2014.7013373",
  size =         "9 pages",
  abstract =     "The volume of on line transactions has increased
                 considerably in the recent years. Consequently, the
                 number of fraud cases has also increased, causing
                 billion dollar losses each year worldwide. Therefore,
                 it is mandatory to employ mechanisms that are able to
                 assist in fraud detection. In this work, it is proposed
                 the use of Genetic Programming (GP) to identify frauds
                 (charge back) in electronic transactions, more
                 specifically in online credit card operations. A case
                 study, using a real dataset from one of the largest
                 Latin America electronic payment systems, has been
                 conducted in order to evaluate the proposed algorithm.
                 The presented algorithm achieves good performance in
                 fraud detection, obtaining gains up to 17percent with
                 regard to the actual company baseline. Moreover,
                 several classification problems, with considerably
                 different datasets and domains, have been used to
                 evaluate the performance of the algorithm. The
                 effectiveness of the algorithm has been compared with
                 other methods, widely employed for classification. The
                 results show that the proposed algorithm achieved good
                 classification effectiveness in all tested instances.",
  notes =        "Centro Fed. de Educ., Tecnol. de Minas Gerais, Belo
                 Horizonte, Brazil Also known as \cite{7013373}",

Genetic Programming entries for Carlos A S de Assis Adriano C Machado Pereira Marconi de Arruda Pereira Eduardo Gontijo Carrano