Artikel

Systemic acquired critique of credit card deception exposure through machine learning

A wide range of recent studies are focusing on current issues of financial fraud, especially concerning cybercrimes. The reason behind this is even with improved security, a great amount of money loss occurs every year due to credit card fraud. In recent days, ATM fraud has decreased, while credit card fraud has increased. This study examines articles from five foremost databases. The literature review is designed using extraction by database, keywords, year, articles, authors, and performance measures based on data used in previous research, future research directions and purpose of the article. This study identifies the crucial gaps which ultimately allow research opportunities in this fraud detection process by utilizing knowledge from the machine learning domain. Our findings prove that this research area has become most dominant in the last ten years. We accessed both supervised and unsupervised machine learning techniques to detect cybercrime and management techniques which provide evidence for the effectiveness of machine learning techniques to control cybercrime in the credit card industry. Results indicated that there is room for further research to obtain better results than existing ones on the basis of both quantitative and qualitative research analysis.

Sprache
Englisch

Erschienen in
Journal: Journal of Open Innovation: Technology, Market, and Complexity ; ISSN: 2199-8531 ; Volume: 8 ; Year: 2022 ; Issue: 4 ; Pages: 1-20

Klassifikation
Management
Thema
algorithms
credit card
database
dataset
financial organization
fraud
machine learning

Ereignis
Geistige Schöpfung
(wer)
Dantas, Rui Miguel
Firdaus, Raheela
Jaleel, Farrokh
Mata, Pedro Neves
Mata, Mário Nuno
Li, Gang
Ereignis
Veröffentlichung
(wer)
MDPI
(wo)
Basel
(wann)
2022

DOI
doi:10.3390/joitmc8040192
Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Artikel

Beteiligte

  • Dantas, Rui Miguel
  • Firdaus, Raheela
  • Jaleel, Farrokh
  • Mata, Pedro Neves
  • Mata, Mário Nuno
  • Li, Gang
  • MDPI

Entstanden

  • 2022

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