A social network analysis approach to detecting suspicious online financial activities

Authors:
Lei Tang;Geoffrey Barbier;Huan Liu;Jianping Zhang
Affiliations:
Data Mining and Machine Learning Laboratory, Arizona State University, Tempe, AZ;Data Mining and Machine Learning Laboratory, Arizona State University, Tempe, AZ;Data Mining and Machine Learning Laboratory, Arizona State University, Tempe, AZ;The MITRE Corporation, McLean, VA
Venue:
SBP'10 Proceedings of the Third international conference on Social Computing, Behavioral Modeling, and Prediction
Year:
2010

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Abstract

Social network analysis techniques can be applied to help detect financial crimes. We discuss the relationship between detecting financial crimes and the social web, and use select case studies to illustrate the potential for applying social network analysis techniques. With the increasing use of online financing services and online financial activities, it becomes more challenging to find suspicious activities among massive numbers of normal and legal activities.