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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/64484
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/64484


    Title: Topological pattern discovery and feature extraction for fraudulent financial reporting
    Authors: Huang, Shin-Ying;Tsaih, Rua-Huan;Yu, Fang
    黃馨瑩;蔡瑞煌;郁方
    Contributors: 資管系
    Keywords: Unsupervised learning;Growing Hierarchical Self-Organizing Map;Data mining;Fraudulent financial reporting
    Date: 2014.07
    Issue Date: 2014-03-06 16:30:04 (UTC+8)
    Abstract: Fraudulent financial reporting (FFR) involves conscious efforts to mislead others regarding the financial condition of a business. It usually consists of deliberate actions to deceive regulators, investors or the general public that also hinder systematic approaches from effective detection. The challenge comes from distinguishing dichotomous samples that have their major attributes falling in the same distribution. This study pioneers a novel dual GHSOM (Growing Hierarchical Self-Organizing Map) approach to discover the topological patterns of FFR, achieving effective FFR detection and feature extraction. Specifically, the proposed approach uses fraudulent samples and non-fraudulent samples to train a pair of dual GHSOMs under the same training parameters and examines the hypotheses for counterpart relationships among their subgroups taking advantage of unsupervised learning nature and growing hierarchical structures from GHSOMs. This study further presents (1) an effective classification rule to detect FFR based on the topological patterns and (2) an expert-competitive feature extraction mechanism to capture the salient characteristics of fraud behaviors. The experimental results against 762 annual financial statements from 144 public-traded companies in Taiwan (out of which 72 are fraudulent and 72 are non-fraudulent) reveal that the topological pattern of FFR follows the non-fraud-central spatial relationship, as well as shows the promise of using the topological patterns for FFR detection and feature extraction.
    Relation: Expert System with Applications, 41(9), 4360-4372
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.eswa.2014.01.012
    DOI: 10.1016/j.eswa.2014.01.012
    Appears in Collections:[資訊管理學系] 期刊論文

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