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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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
Online ISBN: 978-3-540-88653-2
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