CCFD: Efficient Credit Card Fraud Detection Using Meta-Heuristic Techniques and Machine Learning Algorithms

Diana T. Mosa, Shaymaa E. Sorour (), Amr A. Abohany and Fahima A. Maghraby
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Diana T. Mosa: Department of Cyber Security, College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia
Shaymaa E. Sorour: Department of Management Information Systems, School of Business, King Faisal University, Alhufof 31982, Saudi Arabia
Amr A. Abohany: Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh 33516, Egypt
Fahima A. Maghraby: College of Computing and Information Technology, Arab Academy for Science, Technology, and Maritime Transport, Cairo 2033, Egypt

Mathematics, 2024, vol. 12, issue 14, 1-27

Abstract: This study addresses the critical challenge of data imbalance in credit card fraud detection (CCFD), a significant impediment to accurate and reliable fraud prediction models. Fraud detection (FD) is a complex problem due to the constantly evolving tactics of fraudsters and the rarity of fraudulent transactions compared to legitimate ones. Efficiently detecting fraud is crucial to minimize financial losses and ensure secure transactions. By developing a framework that transitions from imbalanced to balanced data, the research enhances the performance and reliability of FD mechanisms. The strategic application of Meta-heuristic optimization (MHO) techniques was accomplished by analyzing a dataset from Kaggle’s CCF benchmark datasets, which included data from European credit-cardholders. They evaluated their capability to pinpoint the smallest, most relevant set of features, analyzing their impact on prediction accuracy, fitness values, number of selected features, and computational time. The study evaluates the effectiveness of 15 MHO techniques, utilizing 9 transfer functions (TFs) that identify the most relevant subset of features for fraud prediction. Two machine learning (ML) classifiers, random forest (RF) and support vector machine (SVM), are used to evaluate the impact of the chosen features on predictive accuracy. The result indicated a substantial improvement in model efficiency, achieving a classification accuracy of up to 97% and reducing the feature size by up to 90%. In addition, it underscored the critical role of feature selection in optimizing fraud detection systems (FDSs) and adapting to the challenges posed by data imbalance. Additionally, this research highlights how machine learning continues to evolve, revolutionizing FDSs with innovative solutions that deliver significantly enhanced capabilities.

Keywords: Credit Card Fraud (CCF); Feature Selection (FS); Meta-heuristic Optimization (MHO); Transfer Functions (TFs) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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