Abstract
Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural network to aid the detection of credit card fraud. In this paper, a method to improve this extraction algorithm is presented along with results from a large real-world European credit card data set. Through this application it is shown that neural networks can assist in mission-critical areas of business and are an important tool in the transparent detection of fraud.
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References
Transnational Financial Crime Program, The International Centre for Criminal Law Reform & Criminal Justice Policy, Vancouver, Canada: Drawing conclusions about financial fraud: crime, development, and international co-operative strategies in China and the West (2008)
IdentityTheft.com Inc., http://www.identitytheft.com/index.php/article/stolen_credit_terroist_attacks
Unisys-Corporation: Research shows economic crisis increases Americans’ fears about fraud and ID theft (2009)
Button, M., Johnston, L., Frimpong, K.: The fraud review and the policing of fraud: Laying the foundations for a centralized fraud police or counter fraud executive? Policing, 241–250 (2008)
Everett, C.: Credit card fraud funds terrorism. Computer Fraud & Security (5) (2003)
Association of Certified Fraud Examiners: Report to the Nation on Occupational Fraud and Abuse (2008)
European Healthcare Fraud & Corruption Network: The Human Cost of Fraud (2010)
Aleskerov, E., Freisleben, B., Rao, B.: CARDWATCH: a neural network based database mining system for credit card fraud detection. In: Computational Intelligence for Financial Engineering (CIFEr), pp. 220–226. IEEE Press, Los Alamitos (1997)
Rong-Chang, C., Shu-Ting, L., Xun, L.: Personalized Approach Based on SVM and ANN for detecting credit card fraud. In: International Conference on Neural Networks and Brain, pp. 810–815. IEEE Press, Los Alamitos (2005)
Crosman, P.: Card fraud costs U.S. payment providers $8.6 billion per year. Bank Systems & Technology (2010)
UK Payments Administration: Card fraud facts and figures (2009)
Cybersource: Seventh annual UK online fraud report: trends, key metrics, informed decisions (2011)
Brause, R., Langsdorf, T., Hepp, M.: Neural data mining for credit card fraud detection. In: 11th International Conference on Tools with Artificial Intelligence, p. 103. IEEE Press, Los Alamitos (1999)
Ghosh, S., Reilly, D.L.: Credit card fraud detection with a neural network. In: International Conference on System Sciences, Hawaii, pp. 621–630. IEEE Press, Los Alamitos (1994)
Quinlan, J.R.: Induction of decision trees. Machine Learning 1, 81–106 (1986)
Towell, G.G., Shavlik, J.W.: Extracting refined rules from knowledge-based neural networks. Machine Learning 13(1), 71–101 (1993)
Setiono, R.: Extracting rules from neural networks by pruning and hidden-unit splitting. Neural Computation 9(1), 205–225 (1997)
Craven, M., Shavlik, J.W.: Using sampling and queries to extract rules from trained neural networks. In: International Conference on Machine Learning, pp. 37–45. Morgan Kaufmann, San Francisco (1994)
Thrun, S.: Advances in Neural Information Processing Systems-Extracting rules from artificial neural networks with distributed representations. MIT Press, Cambridge (1995)
Ryman-Tubb, N., d’Avila Garcez, A.S.: SOAR - Sparse Oracle-based Adaptive Rule extraction: knowledge extraction from large-scale datasets to detect credit card fraud. In: World Congress on Computational Intelligence, Barcelona, Spain, pp. 1–9. IEEE Press, Los Alamitos (2010)
Barakat, N., Diederich, J.: Learning-based rule-extraction from support vector machines. In: 12th International Conference on Computer Theory and Applications, IEEE Press, Los Alamitos (2004)
Barakat, N., Diederich, J.: Eclectic rule-extraction from support vector machines. International Journal Computational Intelligence 2(1), 59–62 (2005)
Engelbrecht, A.P., Viktor, H.L.: Engineering applications of bio-inspired artificial neural networks-Rule improvement through decision boundary detection using sensitivity analysis. Springer, Heidelberg (1999)
Carpenter, G.A., Grossberg, S.: ART2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics 26, 4919–4930 (1987)
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: Synthetic Minority Over-sampling TEchnique. Journal of Artificial Intelligence Research 16, 341–378 (2002)
Fletcher, R., Powell, M.J.D.: A rapidly convergent descent method for minimization. Computing Journal 6, 163–168 (1963)
Hirose, Y., Yamashita, K., Hijiya, S.: Backpropagation algorithm which varies the number of hidden units. In: International Joint Conference on Neural Networks, p. 625. Elsevier, Amsterdam (1989)
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Ryman-Tubb, N.F., Krause, P. (2011). Neural Network Rule Extraction to Detect Credit Card Fraud. In: Iliadis, L., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN AIAI 2011 2011. IFIP Advances in Information and Communication Technology, vol 363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23957-1_12
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DOI: https://doi.org/10.1007/978-3-642-23957-1_12
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