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Cited by 505

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Identifying the signs of fraudulent accounts using data mining techniques

Open access
Li, Shing-Han, Yen, David C., Lu, Wen-Hui, Wang, Chiang·2012·Computers in Human Behavior
DOI 10.1016/j.chb.2012.01.002
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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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Metadata

Authors
Li, S.-H., Yen, D. C., Lu, W.-H., & Wang, C.
Venue
Computers in Human Behavior
Publisher
Elsevier BV
Published
2012-05
Volume
28
Issue
3
Pages
1002-1013
ISSN
0747-5632 (ISSN-L)
DOI
10.1016/j.chb.2012.01.002
OpenAlex
W1636210188
Cited by
505
References
2
FWCI
63.01
Type
journal article
Language
en
Open access
green
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)

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CoChat. (2026). Citation check: Identifying the signs of fraudulent accounts using data mining techniques. https://app.cochat.ai/papers/10.1016/j.chb.2012.01.002
The paper
Li, S.-H., Yen, D. C., Lu, W.-H., & Wang, C. (2012). Identifying the signs of fraudulent accounts using data mining techniques. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2012.01.002

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.