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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F23%3A39920840" target="_blank" >RIV/00216275:25410/23:39920840 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s10796-022-10346-6" target="_blank" >https://link.springer.com/article/10.1007/s10796-022-10346-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10796-022-10346-6" target="_blank" >10.1007/s10796-022-10346-6</a>

Result language

angličtina

Original language name

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

Original language 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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Information Systems Frontiers

ISSN

1387-3326

e-ISSN

1572-9419

Volume of the periodical

25

Issue of the periodical within the volume

5

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

19

Pages from-to

1985-2003

UT code for WoS article

000867540400001

EID of the result in the Scopus database

2-s2.0-85139775639