• 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