Abstract
The identification of anomalous user behaviour is important in a number of application areas, since it may be indicative of fraudulent activity. In the work presented here, the focus is on the identification and subsequent investigation of suspicious interactions in a network of financial transactions. A network is constructed from data from a peer-to-peer lending system, with links between members representing the initiation of loans. The network is time-sliced to facilitate temporal analysis. Anomalous network structure is sought in the time-sliced network, illustrating the occurrences of unusual behaviour among members. In order to assess the significance of the dense structures returned the enrichment of member attributes within these structures is examined. It seems that dense structures are associated with geographic regions.
This work is supported by Science Foundation Ireland under Grant No. 08/SRC/I1407.
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Redmond, U., Harrigan, M., Cunningham, P. (2012). Mining Dense Structures to Uncover Anomalous Behaviour in Financial Network Data. In: Atzmueller, M., Chin, A., Helic, D., Hotho, A. (eds) Modeling and Mining Ubiquitous Social Media. MUSE MSM 2011 2011. Lecture Notes in Computer Science(), vol 7472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33684-3_4
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