Abstract
Fraud detection and prevention systems are based on various technological paradigms but the most prevailing one is rule-based reasoning. However, most of the existing rule-based fraud detection systems consist of fixed and inflexible decision-making rules which limit significantly the effectiveness of such systems. In this paper we present a fraud detection approach which combines the technologies of knowledge-based systems and adaptive systems in order to overcome the limitations of traditional rule-based reasoning. Our approach is supported by an integrated generic methodology for addressing fraud in various e-government domains and organizations through a number of well defined steps that ensure the efficient application of the approach. It is supported also by a generic ontological framework based on which different domain specific fraud knowledge models can be built and through which the generic character and adaptability of our approach is ensured.
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References
Belhadji, B., Dionne, G.: Development of an Expert System for Automatic Detection of Automobile Insurance Fraud, Ecole des Hautes Etudes Commerciales de Montreal- 97-06, Ecole des Hautes Etudes Commerciales de Montreal-Chaire de gestion des risques (1997)
Patrick, B., de Rijke, M., Venema, Y.: Modal Logic. Cambridge Univ. Press, Cambridge (2001)
Crockford, N.: An Introduction to Risk Management, 2nd edn. Woodhead-Faulkner (1986); 0-85941-332-2
Asuncion, G.-P., Corcho, O., Fernandez-Lopez, M.: Ontological Engineering. Springer, Heidelberg (2004)
Gruber, T.R.: A translation approach to portable ontology specification. Knowledge Acquisition 5(2), 1220–1999 (1993)
Hand, D., Mannila, H., Smyth, P.: Principles of Data Mining. MIT Press, Cambridge (2001)
Hodges, W.: Classical Logic I: First Order Logic. In: Goble, L. (ed.) The Blackwell Guide to Philosophical Logic. Blackwell, Malden (2001)
Kou, Y., Lu, C.T., Sirwongwattana, S., Huang, Y.P.: Survey of Fraud Detection Techniques. In: Proceedings of the 2004 International Conference on Networking, Sensing, and Control, Taipei, Taiwan, March 21-23, 2004, pp. 749–754 (2004)
Lam, J.: Enterprise Risk Management: From Incentives to Controls. John Wiley, Chichester (2003); ISBN-13 978-0471430001
Morris, H., Blumstein, R., Hendrick, S.: Searchspace: Operational Business Analytics that Automate the Decision-Making Process, IDC White Paper (2002)
Nute, D.: Defeasible logic. In: Handbook of logic in artificial intelligence and logic programming. Nonmonotonic reasoning and uncertain reasoning, vol. 3, pp. 353–395. Oxford University Press, Oxford (1994)
Tadepalli, S., Sinha, A.K., Ramakrishnnan, N.: Ontology driven data mining for geosciences. In: Proceedings of 2004 AAG Annual Meeting, Denver, USA (2004)
Zukerman, I., Albrecht, D.W.: Predictive statistical user models for user modeling. User Modeling and User-Adapted Interaction 11(1-2), 5–18 (2000)
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Alexopoulos, P. et al. (2008). An Adaptive Knowledge-Based Approach for Detecting Fraud across Different e-Government Domains. In: Filipe, J., Obaidat, M.S. (eds) E-business and Telecommunications. ICETE 2007. Communications in Computer and Information Science, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88653-2_8
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DOI: https://doi.org/10.1007/978-3-540-88653-2_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88652-5
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