A Survey on Credit Card Fraud Detection Techniques in Banking Industry for Cyber Security

Btoush, Eyad and Zhou, Xujuan and Gururajan, Raj ORCID: https://orcid.org/0000-0002-5919-0174 and Chan, KC ORCID: https://orcid.org/0000-0002-8756-2991 and Tao, XiaoHui ORCID: https://orcid.org/0000-0002-0020-077X (2021) A Survey on Credit Card Fraud Detection Techniques in Banking Industry for Cyber Security. In: 8th IEEE International Conference on Behavioural and Social Computing (BESC 2021), 29 Oct - 31 Oct 2021, Doha, Qatar.


Abstract

The technological revolution is accelerating due to a number of key enabling technologies, such as Artificial Intelligence (AI)/Machine Learning (ML), big data, blockchain, cloud computing, Internet of Thing (IoT). With the broad adoption of ever-improving internet technology, cyber security is of great importance in the banking industry due to the rising number of cyber attacks and crimes. Credit card fraud is one of the most serious threats facing the banking industry worldwide. Credit card fraud is expanding at an alarming rate and has developed into a significant problem, particularly as the volume of financial transactions involving credit cards continues to expand. In this paper, we have reviewed various credit card fraud detection techniques that can strengthen the defense against a range of frauds. Additionally, we analysed the findings and reported the research challenges. Finally, we compared various techniques and highlighted their advantages and disadvantages. This will help provide guidance for determining the most appropriate techniques for credit card fraud detection.


Statistics for USQ ePrint 48715
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 - 31 Dec 2021)
Date Deposited: 06 Jun 2022 04:47
Last Modified: 24 Jun 2022 02:59
Uncontrolled Keywords: Artificial intelligence; Banking; Credit card; Credit card fraud detection; Fraud; Machine learning
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460407 System and network security
46 INFORMATION AND COMPUTING SCIENCES > 4604 Cybersecurity and privacy > 460403 Data security and protection
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461103 Deep learning
Identification Number or DOI: https://doi.org/10.1109/BESC53957.2021.9635559
URI: http://eprints.usq.edu.au/id/eprint/48715

Actions (login required)

View Item Archive Repository Staff Only