On an ant colony-based approach for business fraud detection

Authors:
Ou Liu;Jian Ma;Pak-Lok Poon;Jun Zhang
Affiliations:
School of Accounting and Finance, The Hong Kong Polytechnic University, Hong Kong, China;Department of Information Systems, City University of Hong Kong, Hong Kong, China;School of Accounting and Finance, The Hong Kong Polytechnic University, Hong Kong, China;Department of Computer Science, Sun Yat-sen University, Guangzhou, China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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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.