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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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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DOI: https://doi.org/10.1007/3-540-36159-6_23
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