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Seminar abstract


Financial Fraud Detection and Prevention with Data Mining Techniques

Hui Xiong
Associate Professor
Management Science and Information Systems Department, Rutgers, the State University of New Jersey

Abstract :

Recent years have witnessed increased interests in financial fraud detection and prevention. This is driven by the ever-worsening financial crisis and an increased awareness of the importance of financial risk management. Indeed, the wide availability of fine-grained financial data enables unprecedent opportunities to change the computing paradigm for financial fraud detection and prevention. However, as these financial data become more detailed and multi-dimensional, it becomes ever more difficult for analysts to sift through the data even though it may contain valuable information. Data Mining holds great promise to address this challenge by providing efficient techniques to uncover useful information hidden in the large data repositories. Along this line, in this talk, we focus on introducing the unique features that distinguish data mining techniques from traditional analytic techniques for fraud detection and prevention. Also, as a pilot feasibility study, we will present some real-world case studies to illustrate the applications of data mining techniques for financial fraud detection and prevention. Finally, an examination of major research needs in exploiting data mining techniques for fraud detection and prevention reveals some new opportunities for bio-inspired collaborative fraud detection and prevention in multi-source and multi-level financial data.


Dr. Hui Xiong received his Ph.D. from the University of Minnesota (UMN) and the B.E. degree from the University of Science and Technology of China (USTC). He is currently an Associate Professor at Rutgers, the State University of New Jersey, USA, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009), an IBM ESA Innovation Award (2008), the Junior Faculty Teaching Excellence Award (2007) and the Junior Faculty Research Award (2008) at the Rutgers Business School. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. His research has been supported in part by the National Science Foundation (NSF), IBM Research, SAP Corporation, Panasonic USA Inc., and Rutgers University. He has published prolifically in refereed journals and conference proceedings (3 books, 20+ journal papers, and 40+ conference papers), such as JOC、TKDE、VLDBJ、JDMKD、KDD、CCS. He is the co-editor of Clustering and Information Retrieval, the author of Hyperclique Pattern Discovery: Algorithms and Applications, and the co-Editor-in-Chief of Encyclopedia of GIS. He is an Associate Editor of the Knowledge and Information Systems journal. He has served regularly in the organization committees and the program committees of a number of international conferences and workshops, such as AAAI, KDD, ICDE, ICDM,and ICML. He is a senior member of the IEEE and a member of the ACM.
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