World Wide Web

, Volume 16, Issue 4, pp 449–475 | Cite as

Effective detection of sophisticated online banking fraud on extremely imbalanced data

  • Wei Wei
  • Jinjiu Li
  • Longbing Cao
  • Yuming Ou
  • Jiahang Chen


Sophisticated online banking fraud reflects the integrative abuse of resources in social, cyber and physical worlds. Its detection is a typical use case of the broad-based Wisdom Web of Things (W2T) methodology. However, there is very limited information available to distinguish dynamic fraud from genuine customer behavior in such an extremely sparse and imbalanced data environment, which makes the instant and effective detection become more and more important and challenging. In this paper, we propose an effective online banking fraud detection framework that synthesizes relevant resources and incorporates several advanced data mining techniques. By building a contrast vector for each transaction based on its customer’s historical behavior sequence, we profile the differentiating rate of each current transaction against the customer’s behavior preference. A novel algorithm, ContrastMiner, is introduced to efficiently mine contrast patterns and distinguish fraudulent from genuine behavior, followed by an effective pattern selection and risk scoring that combines predictions from different models. Results from experiments on large-scale real online banking data demonstrate that our system can achieve substantially higher accuracy and lower alert volume than the latest benchmarking fraud detection system incorporating domain knowledge and traditional fraud detection methods.


fraud detection online banking contrast pattern neural network data mining 


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Wei Wei
    • 1
  • Jinjiu Li
    • 1
  • Longbing Cao
    • 1
  • Yuming Ou
    • 1
  • Jiahang Chen
    • 1
  1. 1.Advanced Analytics InstituteUniversity of Technology SydneySydneyAustralia

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