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
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;
Conference_Titel :
Intelligence and Security Informatics Conference (EISIC), 2012 European
Conference_Location :
Odense
Print_ISBN :
978-1-4673-2358-1
DOI :
10.1109/EISIC.2012.51
