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.
This research is supported in part by a departmental general research fund of the Hong Kong Polytechnic University (Project no. G-U442).
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Liu, O., Ma, J., Poon, PL., Zhang, J. (2009). On an Ant Colony-Based Approach for Business Fraud Detection. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2009. Lecture Notes in Computer Science, vol 5754. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04070-2_116
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DOI: https://doi.org/10.1007/978-3-642-04070-2_116
Publisher Name: Springer, Berlin, Heidelberg
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