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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References
Bihina Bella, M.A., Eloff, J.H.P., Olivier, M.S.: A Fraud Management System Architecture for Next-Generation Networks. Forensic Science International 185, 51–58 (2009)
Burge, P., Shawe-Taylor, J.: An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection. Journal of Parallel and Distributed Computing 61, 915–925 (2001)
Cerullo, M.J., Cerullo, V.: Using Neural Networks to Predict Financial Reporting Fraud: Part 1. Computer Fraud and Security 1999, 14–17 (1999)
Chen, H.J., Huang, S.Y., Kuo, C.L.: Using the Artificial Neural Network to Predict Fraud Litigation: Some Empirical Evidence from Emerging Markets. Expert Systems with Applications 36, 1478–1484 (2009)
Dorigo, M., Stutzle, T.: Ant Colony Optimization. MIT Press, Cambridge (2004)
Estévez, P.A., Held, C.M., Perez, C.A.: Subscription Fraud Prevention in Tele-communications Using Fuzzy Rules and Neural Networks. Expert Systems with Applications 31, 337–344 (2006)
Huang, S., Tsai, C., Yen, C., Cheng, Y.: A Hybrid Financial Analysis Model for Business Failure Prediction. Expert Systems with Applications 35, 1034–1040 (2008)
Khashei, M., Hejazi, S.R., Bijari, M.: A New Hybrid Artificial Neural Networks and Fuzzy Regression Model for Time Series Forecasting. Fuzzy Sets and Systems 159, 769–786 (2008)
Kim, G.: Sarbanes-Oxley, Fraud Prevention, and IMCA: A Framework for Effective Controls Assurance. Computer Fraud and Security, 12–16 (2003)
Mercer, C.J.: Fraud Detection via Regression Analysis. Computers and Security 9, 331–338 (1990)
Pollard, C.: Telecom Fraud: The Cost of Doing Nothing Just Went Up. Computers and Security 24, 437–439 (2005)
Quah, T.S., Sriganesh, M.: Real-Time Credit Card Fraud Detection Using Computational Intelligence. Expert Systems with Applications 35, 1721–1732 (2008)
Sexton, R.S., Dorsey, R.E.: Reliable Classification Using Neural Networks: A Genetic Algorithm and Backpropagation Comparison. Decision Support Systems 30, 11–22 (2000)
Shawe-Taylor, J., Howker, K., Burge, P.: Detection of Fraud in Mobile Tele-communications. Information Security Technical Report 4, 16–28 (1999)
Viaene, S., Ayuso, M., Guillen, M., Gheel, D.V., Dedene, G.: Strategies for Detecting Fraudulent Claims in the Automobile Insurance Industry. European Journal of Operational Research 176, 565–583 (2007)
Yen, C.: Warning Signals for Potential Accounting Frauds in Blue Chip Companies: An Application of Adaptive Resonance Theory. Information Sciences 177, 4515–4525 (2007)
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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
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