Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/54699
Title: On an ant colony-based approach for business fraud detection
Authors: Liu, O 
Ma, J
Poon, PL 
Zhang , J
Keywords: Ant colony optimization
Artificial neural network
Fraud detection
Issue Date: 2009
Publisher: Springer-Verlag
Source: In DS Huang, KH Jo, HH Lee, HJ Kang & V Bevilacqua (Eds.), Emerging intelligent computing technology and applications : 5th International Conference on Intelligent Computing, ICIC 2009, Ulsan, South Korea, September 16-19, 2009 : proceedings, p. 1104-1111. Berlin: Springer-Verlag, 2009 How to cite?
Series/Report no.: Lecture notes in computer science ; v. 5754
Abstract: Nowadays we witness an increasing number of business frauds. To protect investors’ interest, a financial firm should possess an effective means to detect such frauds. In this regard, artificial neural networks (ANNs) are widely used for fraud detection. Traditional back-propagation-based algorithms used for training an ANN, however, exhibit the local optima problem, thus reducing the effectiveness of an ANN in detecting frauds. To alleviate the problem, this paper proposes an approach to training an ANN using an ant colony optimization technique, through which the local optima problem can be solved and the effectiveness of an ANN in fraud detection can be improved. Based on our approach, an associated prototype system is designed and implemented, and an exploratory study is performed. The results of the study are encouraging, showing the viability of our proposed approach.
URI: http://hdl.handle.net/10397/54699
ISBN: 9783642040702 (electronic bk.)
9783642040696 (print)
DOI: 10.1007/978-3-642-04070-2_116
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