Detection of Credit Card Fraud Through Machine Learning In Banking Industry


  • Mohsin Asad Gill School of Business, University of Southern Queensland, Australia.
  • Muneera Quresh Department of Management Sciences, Qurtaba University, Peshawar, Pakistan.
  • Awais Rasool Department of Computer Science, University of Agriculture Faisalabad, 38000, Pakistan.
  • Muhammad Mubashir Hassan Department of Computer Science, Riphah International University, Lahore, Pakistan.


Machine Learning, Banking Industry, CC, Fraud Detection


Facial expression-based automatic emotion identification is an intriguing research subject that has found applications in areas as diverse as security, healthcare, and the human-computer interface. Researchers in this area seek to improve computer prediction by creating methods for reading and encoding facial emotions. As deep learning has shown to be so effective, many designs have been used to maximize its potential. This paper's goal is to examine recent efforts towards fully autonomous FER with the use of deep learning. The researcher emphasizes these processing contributions, architectures and databases, and then illustrate the progress accomplished by comparing the offered approaches and acquired outcomes. This paper’s goal is to aid and direct scholars by surveying current efforts and offering suggestions for how to further the area.




How to Cite

Mohsin Asad Gill, Muneera Quresh, Awais Rasool, & Muhammad Mubashir Hassan. (2023). Detection of Credit Card Fraud Through Machine Learning In Banking Industry. Journal of Computing & Biomedical Informatics, 5(01), 273–282. Retrieved from