Mining dense structures to uncover anomalous behaviour in financial network data

Authors:
Ursula Redmond;Martin Harrigan;Pádraig Cunningham
Affiliations:
School of Computer Science & Informatics, University College, Dublin, Ireland;School of Computer Science & Informatics, University College, Dublin, Ireland;School of Computer Science & Informatics, University College, Dublin, Ireland
Venue:
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
Year:
2011

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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.