A review of data mining-based financial fraud detection research

Dianmin Yue, Xiaodan Wu, Yunfeng Wang, Yue Li, Chao Hsien Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Citations (Scopus)

Abstract

Nationwide, financial losses due to financial statement frauds (FSF) are mounting. The industry recognizes the problem and is just now starting to act. Although prevention is the best way to reduce frauds, fraudsters are adaptive and will usually find ways to circumvent such measures. Detecting fraud is essential once prevention mechanism has failed. Several data mining algorithms have been developed that allow one to extract relevant knowledge from a large amount of data like fraudulent financial statements to detect FSF. Detecting FSF is a new attempt; thus, several research questions have often being asked: (1) Can FSF be detected? How likely and how to do it? (2) What data features can be used to predict FSF? (3) What kinds of algorithm can be used to detect FSF? (4) How to measure the performance of the detection? And (5) How effective of these algorithms in terms of fraud detection? To help answer these questions, we conduct an extensive review on literatures. We present a generic framework to guide our analysis. Critical issues for FSF detection are identified and discussed. Finally, we share directions for future research.

Original languageEnglish (US)
Title of host publication2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007
Pages5514-5517
Number of pages4
DOIs
StatePublished - Dec 1 2007
Event2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007 - Shanghai, China
Duration: Sep 21 2007Sep 25 2007

Publication series

Name2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007

Other

Other2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007
CountryChina
CityShanghai
Period9/21/079/25/07

Fingerprint

fraud
Data mining
Mountings
Industry
industry
performance

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Communication

Cite this

Yue, D., Wu, X., Wang, Y., Li, Y., & Chu, C. H. (2007). A review of data mining-based financial fraud detection research. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007 (pp. 5514-5517). [4341127] (2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007). https://doi.org/10.1109/WICOM.2007.1352
Yue, Dianmin ; Wu, Xiaodan ; Wang, Yunfeng ; Li, Yue ; Chu, Chao Hsien. / A review of data mining-based financial fraud detection research. 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007. 2007. pp. 5514-5517 (2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007).
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abstract = "Nationwide, financial losses due to financial statement frauds (FSF) are mounting. The industry recognizes the problem and is just now starting to act. Although prevention is the best way to reduce frauds, fraudsters are adaptive and will usually find ways to circumvent such measures. Detecting fraud is essential once prevention mechanism has failed. Several data mining algorithms have been developed that allow one to extract relevant knowledge from a large amount of data like fraudulent financial statements to detect FSF. Detecting FSF is a new attempt; thus, several research questions have often being asked: (1) Can FSF be detected? How likely and how to do it? (2) What data features can be used to predict FSF? (3) What kinds of algorithm can be used to detect FSF? (4) How to measure the performance of the detection? And (5) How effective of these algorithms in terms of fraud detection? To help answer these questions, we conduct an extensive review on literatures. We present a generic framework to guide our analysis. Critical issues for FSF detection are identified and discussed. Finally, we share directions for future research.",
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Yue, D, Wu, X, Wang, Y, Li, Y & Chu, CH 2007, A review of data mining-based financial fraud detection research. in 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007., 4341127, 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007, pp. 5514-5517, 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007, Shanghai, China, 9/21/07. https://doi.org/10.1109/WICOM.2007.1352

A review of data mining-based financial fraud detection research. / Yue, Dianmin; Wu, Xiaodan; Wang, Yunfeng; Li, Yue; Chu, Chao Hsien.

2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007. 2007. p. 5514-5517 4341127 (2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Yue D, Wu X, Wang Y, Li Y, Chu CH. A review of data mining-based financial fraud detection research. In 2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007. 2007. p. 5514-5517. 4341127. (2007 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2007). https://doi.org/10.1109/WICOM.2007.1352