DocumentCode
3302045
Title
Data Mining Applications for Fraud Detection in Securities Market
Author
Golmohammadi, Koosha ; Zaiane, Osmar R.
Author_Institution
Dept. of Comput. Sci., Univ. of Alberta Canada, Edmonton, AB, Canada
fYear
2012
fDate
22-24 Aug. 2012
Firstpage
107
Lastpage
114
Abstract
This paper presents an overview of fraud detection in securities market as well as a comprehensive literature review of data mining methods that are used to address the issue. We identify the best practices that are based on data mining methods for detecting known fraudulent patterns and discovering new predatory strategies. Furthermore, we highlight the challenges faced in the development and implementation of data mining systems for detecting market manipulation in securities market and we provide recommendation for future research works accordingly.
Keywords
data mining; financial data processing; fraud; security of data; data mining applications; fraud detection; fraudulent pattern detection; market manipulation detection; predatory strategy discovery; securities market; Data mining; Data visualization; Databases; Market research; Regulators; Security; Stock markets; data mining; fraud detection; market manipulation; securities market; stocks;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligence and Security Informatics Conference (EISIC), 2012 European
Conference_Location
Odense
Print_ISBN
978-1-4673-2358-1
Type
conf
DOI
10.1109/EISIC.2012.51
Filename
6298820
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