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Abstract
In many cases synthetic data is more suitable than authentic data for the testing and training of fraud detection systems. At the same time synthetic data suffers from some drawbacks originating from the fact that it is indeed synthetic and may not have the realism of authentic data. In order to counter this disadvantage, we have developed a method for generating synthetic data that is derived from authentic data. We identify the important characteristics of authentic data and the frauds we want to detect and generate synthetic data with these properties.
The author is also with Telia Research AB, SE-123 86 Farsta, Sweden
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Authors and Affiliations
Department of Computer Engineering, Chalmers University of Technology, 412 96, Göteborg, Sweden
Emilie Lundin, Håkan Kvarnström & Erland Jonsson
- Emilie Lundin
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- Håkan Kvarnström
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- Erland Jonsson
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Editors and Affiliations
Labs for Information Technology, 21 Heng Mui Keng Terrace, Singapore, 119613
Robert Deng , Feng Bao & Jianying Zhou , &
Engineering Research Center for Information Security Technology, Chinese Academy of Sciences, P.O. Box 8718, Beijing, 100080, China
Sihan Qing
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Lundin, E., Kvarnström, H., Jonsson, E. (2002). A Synthetic Fraud Data Generation Methodology. In: Deng, R., Bao, F., Zhou, J., Qing, S. (eds) Information and Communications Security. ICICS 2002. Lecture Notes in Computer Science, vol 2513. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36159-6_23
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