Mining Financial Statement Fraud

被引：0
|
作者：
West, Jarrod [1 ]
Bhattacharya, Maumita [1 ]
机构：
[1] Charles Sturt Univ, Sch Comp & Math, Albury, NSW 2640, Australia
来源：
PROCEEDINGS OF THE 2015 10TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS | 2015年
关键词：
financial statement fraud; data mining; computational intelligence; problem representation; feature selection; performance metric;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Financial statement fraud detection is an important problem with a number of design aspects to consider. Issues such as (i) problem representation, (ii) feature selection, and (iii) choice of performance metrics all influence the perceived performance of detection algorithms. Efficient implementation of financial fraud detection methods relies on a clear understanding of these issues. In this paper we present an analysis of the three key experimental issues associated with financial statement fraud detection, critiquing the prevailing ideas and providing new understandings.
引用
收藏
页码：461 / 466
页数：6
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