Hybrid Big Bang-Big crunch with cuckoo search for feature selection in credit card fraud detection.

Journal: Scientific reports
Published Date: Jul 4, 2025

Abstract

The technological advancements of financial applications and the expansion of e-commerce platforms have increased the daily volume of credit card transactions. Consequently, there has been a substantial rise in instances of credit card fraud that leads to monetary losses for both individuals and financial institutions. The fraudsters continuously develop new technologies to breach security and acquire the credit card credentials of users through fraudulent activities such as scamming, phishing, or exploiting data breaches. There are numerous machine learning and deep learning techniques for detecting credit card frauds. However, due to the higher dimensionality and the imbalance between fraud and legitimate transactions, it becomes challenging to determine credit frauds with effective performance. To address the aforementioned issues, the current work has presented a novel hybrid Big Bang-Big crunch with cuckoo search (HBCS) method for feature selection prior to performing the classification process. Here, both the Big Bang-Big crunch (BB-BC) and cuckoo search (CS) are metaheuristic algorithms, with BB-BC being a physics-inspired algorithm derived from the theory of universe evolution and CS being an inspiration from the cuckoo bird's brood parasitism behavior. In the HBCS method, the BB-BC algorithm is utilized for exploiting the solution space locally and CS to explore the solutions globally. Here, the CS algorithm uses the Levy flight attribute to help the BB-BC agents escape from stagnation and premature convergence. After feature selection, classification is performed using Deep Convolutional Neural Networks (DCNN) and Enhanced DCNN (EDCNN) to improve detection accuracy. The efficacy of the proposed framework is accessed through experiments conducted on the ECC (European Credit Cardholders) dataset. The HBCS-based system achieves 94.59% accuracy with DCNN and 95.61% with EDCNN, outperforming individual BB-BC and CS feature selection techniques. The experimental evaluations also confirm the efficacy of the proposed framework to detect credit card frauds, surpassing state-of-the-art approaches.

Authors

  • Mohd Shukri Ab Yajid
    Management and Science University, Shah Alam, Malaysia.
  • Nilesh Bhosle
    Department of Computing Science and Artificial Intelligence, NIMS Institute of Engineering & Technology, NIMS University Rajasthan, Jaipur, India.
  • Gadug Sudhamsu
    Department of Computer Science and Engineering, School of Engineering and Technology, JAIN University, Bangalore, Karnataka, India.
  • Ali Khatibi
    McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
  • Sahil Sharma
    Department of Biotechnology, Indian Institute of Technology Roorkee, Roorkee 247667, India.
  • Rubal Jeet
    Department of Computer Science Engineering, Chandigarh Engineering College, Chandigarh Group of Colleges-Jhanjeri, 140307, Mohali, Punjab, India.
  • R Sivaranjani
    Department of Mathematics, Bharathiar University, Coimbatore - 641 046, Tamil Nadu, India. Electronic address: sivaranjanisurya@gmail.com.
  • A Bhowmik
    Centre for Research Impact & Outcome, Institute of Engineering and Technology, Chitkara University, 140401, Rajpura, Punjab, India.
  • A Johnson Santhosh
    Faculty of Mechanical Engineering, Jimma Institute of Technology, Jimma University, Jimma, Ethiopia. johnson.antony@ju.edu.et.

Keywords

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