Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

• Published in: Information systems frontiers Vol. 25; no. 5; pp. 1985 - 2003
• Main Authors: Hajek, Petr, Abedin, Mohammad Zoynul, Sivarajah, Uthayasankar
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-10-2023; Springer Nature B.V
• Subjects: Algorithms; Business and Management; Control; Data analysis; Fraud; Fraud prevention; Information systems; IT in Business; Machine learning; Management of Computing and Information Systems; Mobile commerce; Operations Research/Decision Theory; Outliers (statistics); Payment systems; Supervised learning; System effectiveness; Systems Theory; Imbalanced data; Mobile payment; Outlier detection; Fraud detection; Machine learning
• ISSN: 1387-3326; 1572-9419
• DOI: 10.1007/s10796-022-10346-6

Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems.