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Novel Techniques for Profiling and Fraud Detection in Mobile Telecommunications

In: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications

Author

Listed:
  • John Shawe-Taylor

    (Royal Holloway, University of London, UK)

  • Keith Howker

    (Royal Holloway, University of London, UK)

  • Phil Gosset

    (Vodafone Ltd, UK)

  • Mark Hyland

    (Katholieke Universiteit Leuven, Belgium)

  • Herman Verrelst

    (Katholieke Universiteit Leuven, Belgium)

  • Yves Moreau

    (Katholieke Universiteit Leuven, Belgium)

  • Christof Stoermann

    (Siemens AG, U.S.A.)

  • Peter Burge

    (Logica UK Ltd, U.K.)

Abstract

The following sections are included:IntroductionFraud scenariosThe fraud detection environmentSystem requirementsASPeCT choice of technologyUser ProfilingThe FDT componentsThe ASPeCT trialLegal considerationsLegal backgroundTelecommunications privacyPersonal data protectionAdmissibility of electronic evidenceConclusionsReferences

Suggested Citation

  • John Shawe-Taylor & Keith Howker & Phil Gosset & Mark Hyland & Herman Verrelst & Yves Moreau & Christof Stoermann & Peter Burge, 2000. "Novel Techniques for Profiling and Fraud Detection in Mobile Telecommunications," World Scientific Book Chapters, in: Business Applications Of Neural Networks The State-of-the-Art of Real-World Applications, chapter 8, pages 113-139, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9789812813312_0008
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    Cited by:

    1. Marron, J.S., 2017. "Big Data in context and robustness against heterogeneity," Econometrics and Statistics, Elsevier, vol. 2(C), pages 73-80.

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