Toward an Efficient Credit Card Fraud Detection

Aryam Alneyadi, Hanane Lamaazi, Mahra Alshamsi, Mariam Albaloushi, Mariam Alneyadi, Nasreddine Megrez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Credit card fraud is a type of financial crime involving the unlawful use of a credit card for financial advantages. It can be obtained through unethical behaviors such as phishing, skimming devices, and data breaches. Because credit card fraud is becoming a major threat to cardholders, issuing banks, and online stores, developing an efficient detection system becomes crucial. This paper proposes a three-stage fraudulent card detection system. It relies on a) Detection of invalid and fake credit from legitimate ones by using the Luhn algorithm for card number validation, b) dynamic verification of card expiry date, and c) a script code validation for Card Verification Value (CVV) or Card Verification Code (CVC) that compute the total number of digits which should be within the specified range. In addition, this work provides a user-friendly GUI that integrates the proposed solution, making it very easy and simple to use. As a result of extensive tests of several fraudulent and valid cards, our proposed solution showed a high efficiency compared to the distance sum model. It also detected serial numbers that passed the applications where only the luhn algorithm is used.
Original languageEnglish
Title of host publication2024 Arab ICT Conference (AICTC)
PublisherIEEE
ISBN (Electronic)9798350342086
ISBN (Print)9798350342093
DOIs
Publication statusPublished - 31 Oct 2024
EventArab ICT Conference 2024 - , Bahrain
Duration: 27 Feb 202428 Feb 2024
https://www.arabict.info/

Conference

ConferenceArab ICT Conference 2024
Abbreviated titleAICTC
Country/TerritoryBahrain
Period27/02/2428/02/24
Internet address

Keywords

  • Codes
  • machine learning algorithms
  • Heuristic algorithms
  • Prevention and mitigation
  • Phishing
  • Machine learning
  • Data breach
  • Credit cards
  • Fraud
  • Graphical user interfaces

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