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Review of machine learning approach on credit card fraud detection

Research output: Contribution to journalReview articlepeer-review

172 Citations (Scopus)
4 Downloads (Pure)

Abstract

Massive usage of credit cards has caused an escalation of fraud. Usage of credit cards has resulted in the growth of online business advancement and ease of the e-payment system. The use of machine learning (methods) are adapted on a larger scale to detect and prevent fraud. ML algorithms play an essential role in analysing customer data. In this research article, we have conducted a comparative analysis of the literature review considering the ML techniques for credit card fraud detection (CCFD) and data confidentiality. In the end, we have proposed a hybrid solution, using the neural network (ANN) in a federated learning framework. It has been observed as an effective solution for achieving higher accuracy in CCFD while ensuring privacy.

Original languageEnglish
Pages (from-to)55-68
Number of pages14
JournalHuman-Centric Intelligent Systems
Volume2
Issue number1
DOIs
Publication statusPublished - 5 May 2022

Keywords

  • Artificial neural network (ANN)
  • Blockchain
  • Credit card fraud
  • Federated learning
  • Privacy-preserving
  • Random forest (RF) method
  • Support vector machine (SVM)

ASJC Scopus subject areas

  • Information Systems
  • Artificial Intelligence
  • Computer Science (miscellaneous)

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