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New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection
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Author(s): Addisson Salazar (Universitat Politècnica de València, Spain), Gonzalo Safont (Universitat Politècnica de València, Spain), Alberto Rodriguez (Universidad Miguel Hernández de Elche, Spain)and Luis Vergara (Universitat Politècnica de València, Spain)
Copyright: 2019
Pages: 16
Source title:
Advanced Methodologies and Technologies in System Security, Information Privacy, and Forensics
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-5225-7492-7.ch022
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Abstract
Automatic credit card fraud detection (ACCFD) is a challenge issue that has been increasingly studied considering the expanded potential of new technologies to emulate legitimate operations. Solution has to handle changing fraud behavior, detection in data with very small fraud/legitimate operations ratio, and accomplish operation requirements of very low false alarm in real-time processing. In this chapter, main issues related with the problem of ACCFD and proposed solutions are discussed from theoretical and practical standpoints. The perspective of detection analyses from receiving operating characteristic curves and business key performance indicators are jointly analyzed. A new conceptual framework for ACCFD considering decision fusion and surrogate data is outlined including a case of study with different proportions of real and surrogate data. In addition, the sensitivity of the methods to different proportions of fraud/legitimate ratios is tested. Finally, theoretical and practical conclusions are provided, and several open lines of research are proposed.
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