English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88866/118573 (75%)
Visitors : 23548717      Online Users : 133
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 商學院 > 會計學系 > 期刊論文 >  Item 140.119/63967
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/63967


    Title: Unsupervised Neural Networks Approach for Understanding Fraudulent Financial Reporting
    Authors: Huang, Shin-Ying;Tsaih, Rua-Huan;Lin, Wan-Ying
    黃馨瑩;蔡瑞煌;林宛瑩
    Contributors: 會計系
    Keywords: Financial reporting;Knowledge management;Neural nets;Financial statements;Fraudulent financial reporting;Growing hierarchical self-organizing map;Knowledge extraction
    Date: 2011-09
    Issue Date: 2014-02-18 16:35:46 (UTC+8)
    Abstract: Purpose - Creditor reliance on accounting-based numbers as a persistent and traditional standard to assess a firm's financial soundness and viability suggests that the integrity of financial statements is essential to credit decisions. The purpose of this paper is to provide an approach to explore fraudulent financial reporting (FFR) via growing hierarchical self-organizing map (GHSOM), an unsupervised neural network tool, to help capital providers evaluate the integrity of financial statements, and to facilitate analysis further to reach prudent credit decisions. Design/methodology/approach - This paper develops a two-stage approach: a classification stage that well trains the GHSOM to cluster the sample into subgroups with hierarchical relationship, and a pattern-disclosure stage that uncovers patterns of the common FFR techniques and relevant risk indicators of each subgroup. Findings - An application is conducted and its results show that the proposed two-stage approach can help capital providers evaluate the reliability of financial statements and accounting numbers-based decisions. Practical implications - Following the SOM theories, it seems that common FFR techniques and relevant risk indicators extracted from the GHSOM clustering result are applicable to all samples clustered in the same leaf node (subgroup). This principle and any pre-warning signal derived from the identified indicators can be applied to assessing the reliability of financial statements and forming a basis for further analysis in order to reach prudent decisions. The limitation of this paper is the subjective parameter setting of GHSOM. Originality/value - This is the first application of GHSOM to financial data and demonstrates an alternative way to help capital providers such as lenders to evaluate the integrity of financial statements, a basis for further analysis to reach prudent decisions. The proposed approach could be applied to other scenarios that rely on accounting numbers as a basis for decisions.
    Relation: Industrial Management and Data Systems, 112(2), 224 - 244
    Source URI: http://dx.doi.org/10.1108/02635571211204272
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1108/02635571211204272
    DOI: 10.1108/02635571211204272
    Appears in Collections:[會計學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    224244.pdf148KbAdobe PDF929View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback