Affiliations: Data Mining Lab, Department of Computer Engineering, Alzahra University, Tehran, Iran
Correspondence:
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Corresponding author: Mohammadreza Keyvanpour, Data Mining Lab, Department of Computer Engineering, Alzahra University, Tehran, Iran. E-mail:[email protected]
Abstract: Fraud detection is one of the ways to cope with damages
of fraudulent activities that have become common due to the rapid
development of Internet technology and electronic business. In this paper,
first the concept of fraud and the status of fraud detection will be
discussed and then an efficient framework for the classification of various
fraud detection approaches will be presented. Investigation of the proposed
methods in this field indicates the existence of various approaches to fraud
detection and perception of existing concepts. Lack of a comprehensive
classification in this regard and specified evaluation criteria are the main
challenges facing researchers. Therefore, in this paper, in addition to the
identification of four basic approaches to fraud detection, appropriate
criteria for evaluating these methods will be described. Finally, the
proposed approaches will be evaluated based on the criteria presented. Using
the systematic and structured framework proposed in this paper, more
accurate fraud detection methods and their principled application can be
identified and a useful platform for the comparative study of existing
methods in the field will be provided.
Keywords: Fraud, fraud detection, data mining, social network analysis, statistics, formal
