article

Identifying the signs of fraudulent accounts using data mining techniques

Authors:

Chiang WangAuthors Info & Claims

Computers in Human Behavior, Volume 28, Issue 3

Pages 1002 - 1013

https://doi.org/10.1016/j.chb.2012.01.002

Published: 01 May 2012 Publication History

Abstract

In today's technological society there are various new means to commit fraud due to the advancement of media and communication networks. One typical fraud is the ATM phone scams. The commonality of ATM phone scams is basically to attract victims to use financial institutions or ATMs to transfer their money into fraudulent accounts. Regardless of the types of fraud used, fraudsters can only collect victims' money through fraudulent accounts. Therefore, it is very important to identify the signs of such fraudulent accounts and to detect fraudulent accounts based on these signs, in order to reduce victims' losses. This study applied Bayesian Classification and Association Rule to identify the signs of fraudulent accounts and the patterns of fraudulent transactions. Detection rules were developed based on the identified signs and applied to the design of a fraudulent account detection system. Empirical verification supported that this fraudulent account detection system can successfully identify fraudulent accounts in early stages and is able to provide reference for financial institutions.

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Cited By

Bockel-Rickermann CVerdonck TVerbeke W(2023)Fraud analyticsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120605232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120605
Eshghi AKargari M(2019)Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertaintyExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.11.039121:C(382-392)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1016/j.eswa.2018.11.039
Chang YLai KChou SChen M(2017)Mining the Networks of Telecommunication Fraud Groups using Social Network AnalysisProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3119396(1128-1131)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3119396
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Identifying the signs of fraudulent accounts using data mining techniques

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cover image Computers in Human Behavior

Computers in Human Behavior Volume 28, Issue 3

May, 2012

312 pages

ISSN:0747-5632

Issue’s Table of Contents

Copyright © Elsevier Ltd © 2012.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 May 2012

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Cited By

Bockel-Rickermann CVerdonck TVerbeke W(2023)Fraud analyticsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120605232:COnline publication date: 1-Dec-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120605
Eshghi AKargari M(2019)Introducing a new method for the fusion of fraud evidence in banking transactions with regards to uncertaintyExpert Systems with Applications: An International Journal10.1016/j.eswa.2018.11.039121:C(382-392)Online publication date: 1-May-2019
https://dl.acm.org/doi/10.1016/j.eswa.2018.11.039
Chang YLai KChou SChen M(2017)Mining the Networks of Telecommunication Fraud Groups using Social Network AnalysisProceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 201710.1145/3110025.3119396(1128-1131)Online publication date: 31-Jul-2017
https://dl.acm.org/doi/10.1145/3110025.3119396
Wang HXu ZFujita HLiu S(2016)Towards felicitous decision makingInformation Sciences: an International Journal10.1016/j.ins.2016.07.007367:C(747-765)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.07.007
Bello-Orgaz GJung JCamacho D(2016)Social big dataInformation Fusion10.1016/j.inffus.2015.08.00528:C(45-59)Online publication date: 1-Mar-2016
https://dl.acm.org/doi/10.1016/j.inffus.2015.08.005

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