Abstract
The paper discusses ranking methods for outliers in trade data based on statistical information with the objective to prioritize anti-fraud investigation activities. The paper presents a ranking method based on risk analysis framework and discusses a comprehensive trade fraud indicator that aggregates a number of individual numerical criteria.
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© 2012 Springer-Verlag Berlin Heidelberg
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Kopustinskas, V., Arsenis, S. (2012). Risk Analysis Approaches to Rank Outliers in Trade Data. In: Di Ciaccio, A., Coli, M., Angulo Ibanez, J. (eds) Advanced Statistical Methods for the Analysis of Large Data-Sets. Studies in Theoretical and Applied Statistics(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21037-2_13
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DOI: https://doi.org/10.1007/978-3-642-21037-2_13
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21036-5
Online ISBN: 978-3-642-21037-2
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