题目：Financial Fraud Detection and Prevention with Data Mining Techniques 报告人：Dr. Hui Xiong
Associate Professor
Rutgers, the State University of New Jersey 时间：6月4日 （星期四） 上午10：30-11：30 地点：蒙民伟楼404会议室 摘要：
Recent years have witnessed increased interests in financial fraud detection a
nd prevention. This is driven by the ever-worsening financial crisis and an in
creased awareness of the importance of financial risk management. Indeed, the
wide availability of fine-grained financial data enables unprecedent opportuni
ties to change the computing paradigm for financial fraud detection and preven
tion. However, as these financial data become more detailed and multi-dimensio
nal, it becomes ever more difficult for analysts to sift through the data even
though it may contain valuable information. Data Mining holds great promise t
o address this challenge by providing efficient techniques to uncover useful i
nformation hidden in the large data repositories. Along this line, in this tal
k, we focus on introducing the unique features that distinguish data mining te
chniques from traditional analytic techniques for fraud detection and preventi
on. Also, as a pilot feasibility study, we will present some real-world case s
tudies to illustrate the applications of data mining techniques for financial
fraud detection and prevention. Finally, an examination of major research need
s in exploiting data mining techniques for fraud detection and prevention reve
als some new opportunities for bio-inspired collaborative fraud detection and
prevention in multi-source and multi-level financial data. 简历：
Dr. Hui Xiong received his Ph.D. from the University of Minnesota (UMN) and th
e 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 Excel
lence (2009), an IBM ESA Innovation Award (2008), the Junior Faculty Teaching
Excellence Award (2007) and the Junior Faculty Research Award (2008) at the R
utgers Business School. His general area of research is data and knowledge eng
ineering, with a focus on developing effective and efficient data analysis tec
hniques for emerging data intensive applications. His research has been suppor
ted in part by the National Science Foundation (NSF), IBM Research, SAP Corpor
ation, Panasonic USA Inc., and Rutgers University. He has published prolifical
ly in refereed journals and conference proceedings (3 books, 20+ journal paper
s, and 40+ conference papers), such as JOC、TKDE、VLDBJ、JDMKD、KDD、CCS. He i
s the co-editor of Clustering and Information Retrieval, the author of Hypercl
ique Pattern Discovery: Algorithms and Applications, and the co-Editor-in-Chie
f of Encyclopedia of GIS. He is an Associate Editor of the Knowledge and Infor
mation Systems journal. He has served regularly in the organization committees
and the program committees of a number of international conferences and works
hops, such as AAAI, KDD, ICDE, ICDM，and ICML. He is a senior member of the IE
EE and a member of the ACM.
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