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

Petr Hajek, Abedin Abedin, Uthayasankar Sivarajah

Information Systems Frontiers

Swansea University Author: Abedin Abedin

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DOI (Published version): 10.1007/s10796-022-10346-6

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 rare...

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Published in: Information Systems Frontiers
ISSN: 1387-3326 1572-9419
Published: Springer Science and Business Media LLC 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa64228
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first_indexed 2023-09-20T14:32:09Z
last_indexed 2023-09-20T14:32:09Z
id cronfa64228
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spelling v2 64228 2023-08-31 Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework 4ed8c020eae0c9bec4f5d9495d86d415 Abedin Abedin Abedin Abedin true false 2023-08-31 BAF 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. Journal Article Information Systems Frontiers Springer Science and Business Media LLC 1387-3326 1572-9419 Mobile payment, Fraud detection, Machine learning, Imbalanced data, Outlier detection 14 10 2022 2022-10-14 10.1007/s10796-022-10346-6 http://dx.doi.org/10.1007/s10796-022-10346-6 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2023-09-22T15:18:23.2879280 2023-08-31T17:30:04.9417540 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Petr Hajek 1 Abedin Abedin 2 Uthayasankar Sivarajah 3
title Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
spellingShingle Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
Abedin Abedin
title_short Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
title_full Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
title_fullStr Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
title_full_unstemmed Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
title_sort Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework
author_id_str_mv 4ed8c020eae0c9bec4f5d9495d86d415
author_id_fullname_str_mv 4ed8c020eae0c9bec4f5d9495d86d415_***_Abedin Abedin
author Abedin Abedin
author2 Petr Hajek
Abedin Abedin
Uthayasankar Sivarajah
format Journal article
container_title Information Systems Frontiers
publishDate 2022
institution Swansea University
issn 1387-3326
1572-9419
doi_str_mv 10.1007/s10796-022-10346-6
publisher Springer Science and Business Media LLC
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
url http://dx.doi.org/10.1007/s10796-022-10346-6
document_store_str 0
active_str 0
description 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.
published_date 2022-10-14T15:18:22Z
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score 11.012678

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