An Overview of Credit Card Fraud Detection Techniques

Dr. Muhammad Zain Ali, Dr. Sohail Masood, Fakhar Ur Rehman, Rahman Rasool and Zainab Sadiq
Additional contact information
Dr. Muhammad Zain Ali: Department of Information Technology, Superior University, Lahore, 54000, Pakistan
Dr. Sohail Masood: Department of Computer Science, Superior University, Lahore, 54000, Pakistan
Fakhar Ur Rehman: Department of Computer Science, Superior University, Lahore, 54000, Pakistan
Rahman Rasool: Department of Computer Science, Superior University, Lahore, 54000, Pakistan
Zainab Sadiq: Department of Computer Science, Superior University, Lahore, 54000, Pakistan

Bulletin of Business and Economics (BBE), 2025, vol. 13, issue 3, 444-449

Abstract: Credit card fraud is the first thing that comes to mind when the word fraud is uttered. The volume of credit card transactions has increased significantly in recent years, along with a corresponding spike in credit card fraud. Monitoring users' and customers' spending patterns helps detect fraud and stop bad behavior. There is a rising rate of credit card fraud as they become the most widely used payment mechanism for both online and offline transactions. The goal of fraud detection is to identify fraudulent conduct as soon as it is possible and to document it. The utilization of charge cards is normal in present day culture. The multimillion-dollar industry of extortion is growing each. Extortion influences the world economy fundamentally. Different contemporary strategies, for example, information mining, AI, fluffy rationale, hereditary programming, and man-made consciousness, have been produced for identifying charge card extortion. This study tells the best way to successfully consolidate information mining methods to keep a low or high misleading problem rate while accomplishing high extortion inclusion.

Keywords: :Fraud detection; Data Mining; Neural Networks; Machine Learning; Clustering approaches; Electronic commerce Credit card fraud; spending patterns; Credit card; fraud detection techniques; and online banking (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://bbejournal.com/index.php/BBE/article/view/985/1139 (application/pdf)
https://bbejournal.com/index.php/BBE/article/view/985 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2025:i:3:p:444-449

Access Statistics for this article

Bulletin of Business and Economics (BBE) is currently edited by Dr. Muhammad Irfan Chani

More articles in Bulletin of Business and Economics (BBE) from Research Foundation for Humanity (RFH) Contact information at EDIRC.
Bibliographic data for series maintained by Dr. Muhammad Irfan Chani ( this e-mail address is bad, please contact ).