Skip to content
Journal article

Detecting credit card fraud by genetic algorithm and scatter search

Duman E, Ozcelik M ...see all

Expert Systems with Applications, vol. 38, issue 10 (2011) pp. 13057-13063

  • 78

    Readers

    Mendeley users who have this article in their library.
  • 40

    Citations

    Citations of this article.
  • 4.0k

    Views

    ScienceDirect users who have downloaded this article.
Sign in to save reference

Abstract

In this study we develop a method which improves a credit card fraud detection solution currently being used in a bank. With this solution each transaction is scored and based on these scores the transactions are classified as fraudulent or legitimate. In fraud detection solutions the typical objective is to minimize the wrongly classified number of transactions. However, in reality, wrong classification of each transaction do not have the same effect in that if a card is in the hand of fraudsters its whole available limit is used up. Thus, the misclassification cost should be taken as the available limit of the card. This is what we aim at minimizing in this study. As for the solution method, we suggest a novel combination of the two well known meta-heuristic approaches, namely the genetic algorithms and the scatter search. The method is applied to real data and very successful results are obtained compared to current practice. ?? 2010 Elsevier Ltd. All rights reserved.

Author-supplied keywords

  • Credit cards
  • Fraud
  • Genetic algorithms
  • Optimization
  • Scatter search

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Get full text

Authors

  • Ekrem Duman

  • M. Hamdi Ozcelik

Cite this document

Choose a citation style from the tabs below