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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Alhabshy, Abdallah A.
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An optimized architecture for real-time fraud detection in big data systems, ecosystems, and environments Abutaleb, Gaber Elsayed; Alhabshy, Abdallah A.; Elemary, Berihan R.; Ali, Ebeid; Eldahshan, Kamal Abdelraouf
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1221-1235

Abstract

The exponential growth of data in recent years has created significant challenges in fraud detection. Fraudulent activities are increasingly widespread across sectors, such as banking, web networks, health insurance, and telecommunications. This trend highlights a growing need for big data technologies such as Hadoop, Spark, Storm, and HBase to enable real-time detection and analysis of data fraud. This study aims to enhance understanding of the fraud classifications and their spread in various sectors. Fraud detection involves analyzing data and developing machine learning (ML) models or traditional rule-based systems to identify abnormal activities as they occur. The analysis in this paper examines both the advantages and limitations of these solutions, particularly regarding scalability and performance. This paper evaluates the methods and big data tools used in fraud detection and prevention through a comprehensive literature review, emphasizing the implementation challenges. This review discusses existing solutions, operational environments, and the ML algorithms and traditional rules employed. The main objective of this study is to address these challenges by proposing an innovative architecture that equips organizations with the latest knowledge and methodologies in big data technologies for real-time fraud detection and prevention.
Co-Authors Abutaleb, Gaber Elsayed Ali, Ebeid Eldahshan, Kamal Abdelraouf Elemary, Berihan R.