Credit card fraud detection using AdaBoost and majority voting

Kuldeep Randhawa, Chu Kiong Loo, Manjeevan Seera, Chee Peng Lim, Asoke K. Nandi

Research output: Contribution to journalArticleResearchpeer-review

316 Citations (Scopus)

Abstract

Credit card fraud is a serious problem in financial services. Billions of dollars are lost due to credit card fraud every year. There is a lack of research studies on analyzing real-world credit card data owing to confidentiality issues. In this paper, machine learning algorithms are used to detect credit card fraud. Standard models are first used. Then, hybrid methods which use AdaBoost and majority voting methods are applied. To evaluate the model efficacy, a publicly available credit card data set is used. Then, a real-world credit card data set from a financial institution is analyzed. In addition, noise is added to the data samples to further assess the robustness of the algorithms. The experimental results positively indicate that the majority voting method achieves good accuracy rates in detecting fraud cases in credit cards.

Original languageEnglish
Pages (from-to)14277-14284
Number of pages8
JournalIEEE Access
Volume6
DOIs
Publication statusPublished - 2018
Externally publishedYes

Keywords

  • AdaBoost
  • classification
  • credit card
  • fraud detection
  • predictive modelling
  • voting

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