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

doi:10.1145/3093742.3093912

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 proceeding › Conference contribution

10 Citations (Scopus)

Abstract

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 language	English
Title of host publication	DEBS 2017
Subtitle of host publication	Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems
Publisher	Association for Computing Machinery
Pages	249-260
Number of pages	12
ISBN (Electronic)	9781450350655
DOIs	https://doi.org/10.1145/3093742.3093912
Publication status	Published - 8 Jun 2017
Event	11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017 - Barcelona, Spain Duration: 19 Jun 2017 → 23 Jun 2017

Conference

Conference	11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017
Country/Territory	Spain
City	Barcelona
Period	19/06/17 → 23/06/17

Keywords

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

Access to Document

10.1145/3093742.3093912

Cite this

Artikis, A., Katzouris, N., Correia, I., Baber, C., Morar, N., Skarbovsky, I., Fournier, F., & Paliouras, G. (2017). Industry Paper: A Prototype for Credit Card Fraud Management. In DEBS 2017: Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems (pp. 249-260). Association for Computing Machinery . https://doi.org/10.1145/3093742.3093912

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title = "Industry Paper: A Prototype for Credit Card Fraud Management",

abstract = "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.",

keywords = "Event pattern construction, Event pattern matching, Human factors analysis",

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

year = "2017",

month = jun,

day = "8",

doi = "10.1145/3093742.3093912",

language = "English",

pages = "249--260",

booktitle = "DEBS 2017",

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note = "11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017 ; Conference date: 19-06-2017 Through 23-06-2017",

}

Artikis, A, Katzouris, N, Correia, I, Baber, C, Morar, N, Skarbovsky, I, Fournier, F & Paliouras, G 2017, Industry Paper: A Prototype for Credit Card Fraud Management. in DEBS 2017: Proceedings of the 11th ACM International Conference on Distributed Event-Based Systems. Association for Computing Machinery , pp. 249-260, 11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017, Barcelona, Spain, 19/06/17. https://doi.org/10.1145/3093742.3093912

TY - GEN

T1 - Industry Paper: A Prototype for Credit Card Fraud Management

AU - Artikis, Alexander

AU - Katzouris, Nikos

AU - Correia, Ivo

AU - Baber, Chris

AU - Morar, Natan

AU - Skarbovsky, Inna

AU - Fournier, Fabiana

AU - Paliouras, Georgios

PY - 2017/6/8

Y1 - 2017/6/8

N2 - 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.

AB - 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.

KW - Event pattern construction

KW - Event pattern matching

KW - Human factors analysis

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DO - 10.1145/3093742.3093912

M3 - Conference contribution

AN - SCOPUS:85023201971

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EP - 260

BT - DEBS 2017

PB - Association for Computing Machinery

T2 - 11th ACM International Conference on Distributed Event-Based Systems, DEBS 2017

Y2 - 19 June 2017 through 23 June 2017

ER -

Industry Paper: A Prototype for Credit Card Fraud Management

Abstract

Conference

Keywords

ASJC Scopus subject areas

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Cite this