Industry Paper: A Prototype for Credit Card Fraud Management

Alexander Artikis, Nikos Katzouris, Ivo Correia, Chris Baber, Natan Morar, Inna Skarbovsky, Fabiana Fournier, Georgios Paliouras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


To prevent problems and capitalise on opportunities before they even occur, the research project SPEEDD proposed a methodology, and developed a prototype for proactive event-driven decisionmaking. We present the application of this methodology to credit card fraud management. The machine learning component of the SPEEDD prototype supports the online construction of fraud patterns, allowing it to efficiently adapt to the continuously changing fraud types. Moreover, the user interface of the prototype enables fraud analysts to make the most out of the results of automation (complex event processing) and thus reach informed decisions. Unlike most academic research on credit card fraud management, the assessment of the prototype (components) is based on representative transaction datasets, allowing for a realistic evaluation.

Original languageEnglish
Title of host publicationDEBS 2017
Subtitle of host publicationProceedings of the 11th ACM International Conference on Distributed Event-Based Systems
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Electronic)9781450350655
Publication statusPublished - 8 Jun 2017
Event11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017 - Barcelona, Spain
Duration: 19 Jun 201723 Jun 2017


Conference11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017


  • Event pattern construction
  • Event pattern matching
  • Human factors analysis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Systems Engineering
  • Software


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