• DocumentCode
    3072264
  • Title

    Analysis on credit card fraud detection methods

  • Author

    Raj, S. Benson Edwin ; Portia, A. Annie

  • Author_Institution
    Dept. of CSE, Karunya Univ., Coimbatore, India
  • fYear
    2011
  • fDate
    18-19 March 2011
  • Firstpage
    152
  • Lastpage
    156
  • Abstract
    Due to the rise and rapid growth of E-Commerce, use of credit cards for online purchases has dramatically increased and it caused an explosion in the credit card fraud. As credit card becomes the most popular mode of payment for both online as well as regular purchase, cases of fraud associated with it are also rising. In real life, fraudulent transactions are scattered with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses. Many modern techniques based on Artificial Intelligence, Data mining, Fuzzy logic, Machine learning, Sequence Alignment, Genetic Programming etc., has evolved in detecting various credit card fraudulent transactions. A clear understanding on all these approaches will certainly lead to an efficient credit card fraud detection system. This paper presents a survey of various techniques used in credit card fraud detection mechanisms and evaluates each methodology based on certain design criteria.
  • Keywords
    credit transactions; data mining; electronic money; fraud; fuzzy logic; genetic algorithms; learning (artificial intelligence); artificial intelligence; credit card fraud detection method; credit card issuing banks; data mining; e-commerce; electronic payment; fraudulent transactions; fuzzy logic; genetic programming; loss minimization; machine learning; online purchase; sequence alignment; Accuracy; Artificial intelligence; Artificial neural networks; Bayesian methods; Credit cards; Data mining; Hidden Markov models; Artificial Intelligence; Artificial Neural Networks; Credit card fraud; Electronic Commerce; Machine Learning; Sequence Alignment;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer, Communication and Electrical Technology (ICCCET), 2011 International Conference on
  • Conference_Location
    Tamilnadu
  • Print_ISBN
    978-1-4244-9393-7
  • Type

    conf

  • DOI
    10.1109/ICCCET.2011.5762457
  • Filename
    5762457