Identifying the signs of fraudulent accounts using data mining techniques

By Shing-Han Li, David Chi-Chung Yen, Wen-Hui Lu, Chi-Jer Wang. 2010

453 citations recorded by the source corpus.

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.

Subfields: Accounting, Artificial Intelligence, Information Systems

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Citations in the corpus

Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020, background): “Financial institutions can also detect frauds by finding patterns that deviates greatly from normal behavior by classification and/or anomaly detection [96], [97].”