Gangwani, Akansha (60168694900); Patni, Jagdish Chandra (46161508100); Panjwani, Tisha (60169099700); Thakur, Akansha (60169507200); Vaishnavi (60169507300); Lalwani, Ash (60169507400); Bahadure, Nilesh Bhaskarrao (57192103540); Mishra, Pawan Kumar (57192097291); Sharma, Gaurav (59504491100)Akansha (60168694900); Patni, Jagdish Chandra (46161508100); Panjwani, Tisha (60169099700); Thakur, Akansha (60169507200); Vaishnavi (60169507300); Lalwani, Ash (60169507400); Bahadure, Nilesh Bhaskarrao (57192103540); Mishra, Pawan Kumar (57192097291); Sharma, Gaurav (59504491100)Gangwani2026-02-052026-02-0520259780735446496978073545279497807354518899780735450301978073544108897807354035989780735444577978073544359497807354480019780735416437https://dx.doi.org/10.1063/5.0295549https://www.scopus.com/pages/publications/105021026387https://gnanaganga.alliance.edu.in/handle/123456789/8976In the banking or financial sector, the major problem is credit card fraud and the financial institutions find it challenging. This fundamental study will examine how machine learning combats this issue and suggest possible ways to detect and avoid credit card fraud detection. This article highlights the methods and resources for identifying fraudulent credit card purchases. The study examines the prevalence of credit card fraud and the reasons it must be quickly detected, preparing the data is a crucial step in identifying credit card fraud by examining the various datasets to identify fraudulent activities. The study also describes the importance of cleaning, modifying, and creating characteristics for various kinds of data related to credit card fraud detection. Highlighting the importance of having a different proportion of actual and fraudulent transactions is also emphasized. © 2025 Author(s)enANN; Credit Card fraud; Logistic Regression; Principal Component Analysis; SMOTE; Support Vector MachineConference paper