Mining Dense Structures to Uncover Anomalous Behaviour in Financial Network Data

  • Ursula Redmond
  • Martin Harrigan
  • Pádraig Cunningham
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7472)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ursula Redmond
    • 1
  • Martin Harrigan
    • 1
  • Pádraig Cunningham
    • 1
  1. 1.School of Computer Science & InformaticsUniversity CollegeDublinIreland

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