DocumentCode
2414921
Title
Fuzzy Ranking of Financial Statements for Fraud Detection
Author
Chai, Wei ; Hoogs, Bethany K. ; Verschueren, Benjamin T.
Author_Institution
Gen. Electr. Global Res., Niskayuna
fYear
0
fDate
0-0 0
Firstpage
152
Lastpage
158
Abstract
Automatic detection of anomalies in financial statements can decrease the risk of exposure to fraudulent corporate behavior. This paper proposes a method to convert fraud classification rules learned from a genetic algorithm to a fuzzy score representing the degree to which a company´s financial statements match those rules. Applying the method to financial data in real time can lead to the early detection of potentially fraudulent corporate behavior.
Keywords
financial data processing; fraud; fuzzy reasoning; fuzzy set theory; genetic algorithms; learning (artificial intelligence); pattern classification; security of data; AI learning; automatic anomaly detection; classification rule; financial statement; fraud detection; fraudulent corporate behavior; fuzzy ranking; fuzzy set theory; genetic algorithm; Automation; Computerized monitoring; Fuzzy sets; Genetic algorithms; Inspection; Investments; Logistics; Neural networks; Portfolios; Security;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location
Vancouver, BC
Print_ISBN
0-7803-9488-7
Type
conf
DOI
10.1109/FUZZY.2006.1681708
Filename
1681708
Link To Document