• DocumentCode
    2288699
  • Title

    Research on detecting technique of financial statement fraud based on Fuzzy Genetic Algorithms BPN

  • Author

    Liang, Jie ; Lv, Wei

  • Author_Institution
    Sch. of Manage., Shenyang Univ. of Technol., Shenyang, China
  • fYear
    2009
  • fDate
    14-16 Sept. 2009
  • Firstpage
    1462
  • Lastpage
    1468
  • Abstract
    In recent years, the phenomenon of financial statement fraud what happened in listed companies becomes a global focus, which seriously affects economic development. To avoid the huge harm brought by financial statement fraud, to reduce the heavy work of the auditors, to increase the efficiency and precision of auditing and detecting,it is extremely urgent to research detecting technique, which is efficient, convenient and practical. This paper studies on the financial statements of fraud companies and paired companies. It explores the two aspects characteristic signals both from finance and corporate governance, and establishes a set of more perfect feature indicators for detecting the fraud. Then it designs the Fuzzy Genetic Algorithms BPN (FGABPN) model to detecting fraudulent financial reporting for the first time. It is found by test that discrimination of the model is higher.
  • Keywords
    auditing; backpropagation; financial data processing; fraud; fuzzy neural nets; genetic algorithms; backpropagation neural net; corporate governance; economic development; financial statement fraud; fuzzy genetic algorithm BPN; Conference management; Engineering management; Environmental economics; Finance; Financial management; Genetic algorithms; Manufacturing industries; Research and development management; Stock markets; Technology management; detecting technology; financial statement fraud; fuzzy genetic algorithm BPN;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering, 2009. ICMSE 2009. International Conference on
  • Conference_Location
    Moscow
  • Print_ISBN
    978-1-4244-3970-6
  • Electronic_ISBN
    978-1-4244-3971-3
  • Type

    conf

  • DOI
    10.1109/ICMSE.2009.5317990
  • Filename
    5317990