Using Genetic Programming to Detect Fraud in Electronic Transactions

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@InProceedings{deAssis:2013:WebMedia,
  author =       "Carlos A. S. {de Assis} and Adriano C. M. Pereira and 
                 Marconi {de A. Pereira} and Eduardo G. Carrano",
  title =        "Using Genetic Programming to Detect Fraud in
                 Electronic Transactions",
  booktitle =    "Proceedings of the 19th Brazilian Symposium on
                 Multimedia and the Web (WebMedia '13)",
  year =         "2013",
  pages =        "337--340",
  address =      "Salvador, Brazil",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, fraud, web
                 transactions",
  isbn13 =       "978-1-4503-2559-2",
  URL =          "http://doi.acm.org/10.1145/2526188.2526221",
  DOI =          "doi:10.1145/2526188.2526221",
  acmid =        "2526221",
  size =         "4 pages",
  abstract =     "The volume of online transactions has raised a lot in
                 last years, mainly due to the popularity of E-commerce,
                 such as Web retailers. We also observe a significant
                 increase in the number of fraud cases, resulting in
                 billions of dollars losses each year worldwide.
                 Therefore it is important and necessary to developed
                 and apply techniques that can assist in fraud
                 detection, which motivates our research. This work
                 proposes the use of Genetic Programming (GP), an
                 Evolutionary Computation approach, to model and detect
                 fraud (charge back) in electronic transactions, more
                 specifically in credit card operations. In order to
                 evaluate the technique, we perform a case study using
                 an actual dataset of the most popular Brazilian
                 electronic payment service, called UOL PagSeguro. Our
                 results show good performance in fraud detection,
                 presenting gains up to 17.72percent percent compared to
                 the baseline, which is the actual scenario of the
                 corporation.",
  notes =        "In Portuguese

                 Also known as \cite{Assis:2013:UGP:2526188.2526221}",
}

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

Citations