Abstract
Data mining is one of the most effective methods for fraud detection. This is highlighted by 25 % of organizations that have suffered from economic crimes [1]. This paper presents a case study using real-world data from a large retail company. We identify symptoms of fraud by looking for outliers. To identify the outliers and the context where outliers appear, we learn a regression tree. For a given node, we identify the outliers using the set of examples covered at that node, and the context as the conjunction of the conditions in the path from the root to the node. Surprisingly, at different nodes of the tree, we observe that some outliers disappear and new ones appear. From the business point of view, the outliers that are detected near the leaves of the tree are the most suspicious ones. These are cases of difficult detection, being observed only in a given context, defined by a set of rules associated with the node.
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Acknowledgments
This work was supported by research project TEC4Growth - Pervasive Intelligence, Enhancers and Proofs of Concept with Industrial Impact/NORTE-01-0145-FEDER-000020, financed by the North Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, and through the European Regional Development Fund and by European Commission through the project MAESTRA (ICT-2013-612944).
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Ribeiro, R.P., Oliveira, R., Gama, J. (2016). Detection of Fraud Symptoms in the Retail Industry. In: Montes y Gómez, M., Escalante, H., Segura, A., Murillo, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2016. IBERAMIA 2016. Lecture Notes in Computer Science(), vol 10022. Springer, Cham. https://doi.org/10.1007/978-3-319-47955-2_16
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DOI: https://doi.org/10.1007/978-3-319-47955-2_16
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