Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (34): 226-230.DOI: 10.3778/j.issn.1002-8331.2008.34.069

• 工程与应用 • Previous Articles     Next Articles

Application of integrated classification method in identifying risk of fraudulent financial report

SONG Xin-ping1,4,DING Yong-sheng2,3,4,ZHANG Ge-fu4   

  1. 1.School of Bussiness and Management,Jiangsu University,Zhenjiang,Jiangsu 212013,China
    2.College of Information Sciences and Technology,Donghua University,Shanghai 201620,China
    3.Engineering Resenrch Center of Digitized Textile & Fashion Technology,Ministry of Education,Donghua University,Shanghai 201620,China
    4.Glorious Sun School of Business and Management,Donghua University,Shanghai 200051,China
  • Received:2007-12-13 Revised:2008-05-30 Online:2008-12-01 Published:2008-12-01
  • Contact: SONG Xin-ping



  1. 1.江苏大学 工商管理学院,江苏 镇江 212013
    2.东华大学 信息科学与技术学院,上海 201620
    3.东华大学 数字化纺织服装技术教育部工程研究中心,上海 201620
    4.东华大学 旭日工商管理学院,上海 200051

  • 通讯作者: 宋新平

Abstract: Based on 36 A-shared manufacturing listed companies with fraud financial report and the same number of matched sample with normal financial report in 2005,financial fraut identification models are built using 23 financial indicators and four including multiple discriminative analysis,support vector machine and an integrated classifier.The result indicates that the four models are all effective to some extent and the integrated classifier outperforms other methods in terms of prediction accuracy.

Key words: financial fraud identification, support vector machine, decision tree, integrated classifier

摘要: 以2005年A股制造业的36个财务欺诈公司及相应的配对公司为样本,采用23个财务指标,通过数据挖掘途径,运用多元判别分析、支持向量机、决策树和自己设计的集成分类方法构建了财务欺诈识别模型。实证结果表明:4 种模型都具有一定有效性,集成分类方法的识别准确度最高且识别效果最好。

关键词: 财务欺诈识别, 支持向量机, 决策树, 集成分类方法