DocumentCode :
2288699
Title :
Research on detecting technique of financial statement fraud based on Fuzzy Genetic Algorithms BPN
Author :
Liang, Jie ; Lv, Wei
Author_Institution :
Sch. of Manage., Shenyang Univ. of Technol., Shenyang, China
fYear :
2009
fDate :
14-16 Sept. 2009
Firstpage :
1462
Lastpage :
1468
Abstract :
In recent years, the phenomenon of financial statement fraud what happened in listed companies becomes a global focus, which seriously affects economic development. To avoid the huge harm brought by financial statement fraud, to reduce the heavy work of the auditors, to increase the efficiency and precision of auditing and detecting,it is extremely urgent to research detecting technique, which is efficient, convenient and practical. This paper studies on the financial statements of fraud companies and paired companies. It explores the two aspects characteristic signals both from finance and corporate governance, and establishes a set of more perfect feature indicators for detecting the fraud. Then it designs the Fuzzy Genetic Algorithms BPN (FGABPN) model to detecting fraudulent financial reporting for the first time. It is found by test that discrimination of the model is higher.
Keywords :
auditing; backpropagation; financial data processing; fraud; fuzzy neural nets; genetic algorithms; backpropagation neural net; corporate governance; economic development; financial statement fraud; fuzzy genetic algorithm BPN; Conference management; Engineering management; Environmental economics; Finance; Financial management; Genetic algorithms; Manufacturing industries; Research and development management; Stock markets; Technology management; detecting technology; financial statement fraud; fuzzy genetic algorithm BPN;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Management Science and Engineering, 2009. ICMSE 2009. International Conference on
Conference_Location :
Moscow
Print_ISBN :
978-1-4244-3970-6
Electronic_ISBN :
978-1-4244-3971-3
Type :
conf
DOI :
10.1109/ICMSE.2009.5317990
Filename :
5317990
Link To Document :
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