A machine learning based credit card fraud detection using the GA algorithm for feature selection

Emmanuel Ileberi, Yanxia Sun, Zenghui Wang

Research output: Contribution to journalArticlepeer-review

108 Citations (Scopus)

Abstract

The recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.

Original languageEnglish
Article number24
JournalJournal of Big Data
Volume9
Issue number1
DOIs
Publication statusPublished - Dec 2022

Keywords

  • Cybersecurity
  • Fraud detection
  • Genetic algorithm
  • Machine learning

ASJC Scopus subject areas

  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Information Systems and Management

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