An intelligent payment card fraud detection system

Manjeevan Seera, Chee Peng Lim (), Ajay Kumar (), Lalitha Dhamotharan () and Kim Hua Tan ()
Additional contact information
Chee Peng Lim: Deakin University
Ajay Kumar: EMLYON Business School
Lalitha Dhamotharan: University of Exeter
Kim Hua Tan: Nottingham University Business School

Annals of Operations Research, 2024, vol. 334, issue 1, No 17, 445-467

Abstract: Abstract Payment cards offer a simple and convenient method for making purchases. Owing to the increase in the usage of payment cards, especially in online purchases, fraud cases are on the rise. The rise creates financial risk and uncertainty, as in the commercial sector, it incurs billions of losses each year. However, real transaction records that can facilitate the development of effective predictive models for fraud detection are difficult to obtain, mainly because of issues related to confidentially of customer information. In this paper, we apply a total of 13 statistical and machine learning models for payment card fraud detection using both publicly available and real transaction records. The results from both original features and aggregated features are analyzed and compared. A statistical hypothesis test is conducted to evaluate whether the aggregated features identified by a genetic algorithm can offer a better discriminative power, as compared with the original features, in fraud detection. The outcomes positively ascertain the effectiveness of using aggregated features for undertaking real-world payment card fraud detection problems.

Keywords: Classification; Feature aggregation; Fraud detection; Payment card; Predictive modeling (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-021-04149-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
Working Paper: An intelligent payment card fraud detection system (2024)
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:spr:annopr:v:334:y:2024:i:1:d:10.1007_s10479-021-04149-2

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-021-04149-2

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().