<p>Information security is becoming an important part of everyone’s life, and its job is to protect end users’ private information. These hypersensitive data can be categorized into various domains, including finance, insurance, banking, investment, and foreign exchange services. Banking fraud refers to the act of committing fraud using credit and debit cards. Credit cards offer an effective and user-friendly means of doing online transactions, so that people can use these cards for financial activity. The probability of credit card misuse and the rise in usage have increased. Both credit card providers and cardholders experience significant economic losses due to falsification. The principal objective of this paper’s investigational study is to review the quick rundown performance analysis of the most popular machine learning (ML) and deep learning (DL)approaches for fraud identification. An effort is made to go over appropriate performance indicators, including typical problems that could arise when training credit card fraud models and their possible fixes that have not been covered in prior research. In the meantime, the research and experimental findings using a real-world dataset show how reliable ML and DL architectures are for detecting credit card fraud.</p>