Article: Defending against digital thievery: a machine learning approach to predict e-payment fraud Journal: International Journal of Management Practice (IJMP) 2024 Vol.17 No.5 pp.522 - 538 Abstract: The increased usage of credit cards has facilitated the development of e-commerce and electronic payment systems. However, this trend has also led to a surge in fraudulent activities. As a result, websites and e-commerce platforms that handle customer data have been required to establish efficient fraud prevention systems capable of detecting and preventing fraudulent electronic payment operations. Machine learning has emerged as a highly effective fraud detection and prevention approach in this context. This study focused on implementing a machine-learning system to identify fraudulent electronic payments. To achieve this objective, an AdaBoost supervised machine learning model was utilised. The effectiveness of the model in accurately detecting and preventing online fraud, thus minimising losses resulting from fraudulent transactions, was evaluated. Different performance measures, including precision, recall, accuracy, F1 score, and latency, were employed and compared with those of other machine learning models, namely CatBoost and XGBoost. A comprehensive assessment of its effectiveness in fraud detection was conducted by comparing the performance metrics of the AdaBoost model to those of other machine learning models. This analysis provided insights into the model's capabilities, strengths, and areas for improvement in accurately identifying and preventing fraudulent e-payments. Inderscience Publishers - linking academia, business and industry through research

Title: Defending against digital thievery: a machine learning approach to predict e-payment fraud

Authors: Manal Loukili; Fayçal Messaoudi; Mohammed El Ghazi

Addresses: Sidi Mohamed Ben Abdellah University, National School of Applied Sciences, Fez, Morocco ' Sidi Mohamed Ben Abdellah University, National School of Applied Sciences, Fez, Morocco ' Sidi Mohamed Ben Abdellah University, Superior School of Technology, Fez, Morocco

Abstract: The increased usage of credit cards has facilitated the development of e-commerce and electronic payment systems. However, this trend has also led to a surge in fraudulent activities. As a result, websites and e-commerce platforms that handle customer data have been required to establish efficient fraud prevention systems capable of detecting and preventing fraudulent electronic payment operations. Machine learning has emerged as a highly effective fraud detection and prevention approach in this context. This study focused on implementing a machine-learning system to identify fraudulent electronic payments. To achieve this objective, an AdaBoost supervised machine learning model was utilised. The effectiveness of the model in accurately detecting and preventing online fraud, thus minimising losses resulting from fraudulent transactions, was evaluated. Different performance measures, including precision, recall, accuracy, F1 score, and latency, were employed and compared with those of other machine learning models, namely CatBoost and XGBoost. A comprehensive assessment of its effectiveness in fraud detection was conducted by comparing the performance metrics of the AdaBoost model to those of other machine learning models. This analysis provided insights into the model's capabilities, strengths, and areas for improvement in accurately identifying and preventing fraudulent e-payments.

Keywords: e-payment; fraud detection; e-commerce; ensemble learning; supervised machine learning.

DOI: 10.1504/IJMP.2024.140861

International Journal of Management Practice, 2024 Vol.17 No.5, pp.522 - 538

Received: 13 Dec 2022
Accepted: 08 Jun 2023
Published online: 03 Sep 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Defending against digital thievery: a machine learning approach to predict e-payment fraud

Keep up-to-date