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

Author

Listed:
  • Petr Hajek

    (University of Pardubice)

  • Mohammad Zoynul Abedin

    (Teesside University International Business School, Teesside University)

  • Uthayasankar Sivarajah

    (Bradford University School of Management)

Abstract

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.

Suggested Citation

  • Petr Hajek & Mohammad Zoynul Abedin & Uthayasankar Sivarajah, 2023. "Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework," Information Systems Frontiers, Springer, vol. 25(5), pages 1985-2003, October.
    • Handle: RePEc:spr:infosf:v:25:y:2023:i:5:d:10.1007_s10796-022-10346-6
    DOI: 10.1007/s10796-022-10346-6
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    References listed on IDEAS

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    1. Olayinka David-West & Oluwasola Oni & Folajimi Ashiru, 2022. "Diffusion of Innovations: Mobile Money Utility and Financial Inclusion in Nigeria. Insights from Agents and Unbanked Poor End Users," Information Systems Frontiers, Springer, vol. 24(6), pages 1753-1773, December.
    2. Lin Jia & Xiuwei Song & Dianne Hall, 2022. "Influence of Habits on Mobile Payment Acceptance: An Ecosystem Perspective," Information Systems Frontiers, Springer, vol. 24(1), pages 247-266, February.
    3. Amita Goyal Chin & Mark A. Harris & Robert Brookshire, 2022. "An Empirical Investigation of Intent to Adopt Mobile Payment Systems Using a Trust-based Extended Valence Framework," Information Systems Frontiers, Springer, vol. 24(1), pages 329-347, February.
    4. Delphine Prady & Hervé Tourpe & Sonja Davidovic & Soheib Nunhuck, 2020. "Beyond the COVID-19 Crisis: A Framework for Sustainable Government-To-Person Mobile Money Transfers," IMF Working Papers 2020/198, International Monetary Fund.
    5. Arpan Kumar Kar, 2021. "What Affects Usage Satisfaction in Mobile Payments? Modelling User Generated Content to Develop the “Digital Service Usage Satisfaction Model”," Information Systems Frontiers, Springer, vol. 23(5), pages 1341-1361, September.
    6. Shaio Yan Huang & Chi-Chen Lin & An-An Chiu & David C. Yen, 2017. "Fraud detection using fraud triangle risk factors," Information Systems Frontiers, Springer, vol. 19(6), pages 1343-1356, December.
    7. Abhipsa Pal & Rahul De’ & Tejaswini Herath, 2020. "The Role of Mobile Payment Technology in Sustainable and Human-Centric Development: Evidence from the Post-Demonetization Period in India," Information Systems Frontiers, Springer, vol. 22(3), pages 607-631, June.
    8. Abhipsa Pal & Tejaswini Herath & Rahul De’ & H. Raghav Rao, 2021. "Is the Convenience Worth the Risk? An Investigation of Mobile Payment Usage," Information Systems Frontiers, Springer, vol. 23(4), pages 941-961, August.
    9. Hardin, Johanna & Rocke, David M., 2004. "Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 625-638, January.
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