Identifying the signs of fraudulent accounts using data mining techniques

Authors:
Shing-Han Li;David C. Yen;Wen-Hui Lu;Chiang Wang
Affiliations:
Department of Information Management, Tatung University, 40 ChungShan North Road, 3rd Section, Taipei 104, Taiwan;Department of Decision Sciences and Management Information Systems, Miami University, Oxford, OH 45056, United States;Department of Computer Science and Engineering, Tatung University, 40 ChungShan North Road, 3rd Section, Taipei 104, Taiwan;Department of Information Management, Tatung University, 40 ChungShan North Road, 3rd Section, Taipei 104, Taiwan
Venue:
Computers in Human Behavior
Year:
2012

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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.