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題名 Topological pattern discovery and feature extraction for fraudulent financial reporting
作者 Huang, Shin-Ying ; Tsaih, Rua-Huan ; Yu, Fang
黃馨瑩;蔡瑞煌;郁方
貢獻者 資管系
關鍵詞 Unsupervised learning; Growing Hierarchical Self-Organizing Map; Data mining; Fraudulent financial reporting
日期 2014.07
上傳時間 6-Mar-2014 16:30:04 (UTC+8)
摘要 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.
關聯 Expert System with Applications, 41(9), 4360-4372
資料類型 article
DOI http://dx.doi.org/10.1016/j.eswa.2014.01.012
dc.contributor 資管系en_US
dc.creator (作者) Huang, Shin-Ying ; Tsaih, Rua-Huan ; Yu, Fangen_US
dc.creator (作者) 黃馨瑩;蔡瑞煌;郁方-
dc.date (日期) 2014.07en_US
dc.date.accessioned 6-Mar-2014 16:30:04 (UTC+8)-
dc.date.available 6-Mar-2014 16:30:04 (UTC+8)-
dc.date.issued (上傳時間) 6-Mar-2014 16:30:04 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/64484-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 108 bytes-
dc.format.mimetype text/html-
dc.language.iso en_US-
dc.relation (關聯) Expert System with Applications, 41(9), 4360-4372en_US
dc.subject (關鍵詞) Unsupervised learning; Growing Hierarchical Self-Organizing Map; Data mining; Fraudulent financial reportingen_US
dc.title (題名) Topological pattern discovery and feature extraction for fraudulent financial reportingen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1016/j.eswa.2014.01.012en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1016/j.eswa.2014.01.012en_US