International Research Journal of Education and Technology

Digital transactions and online banking are widely used, but fraud activities have increased. This project focuses on using Artificial Intelligence (AI) and Machine Learning (ML) techniques to detect and prevent fraud in financial transactions. By analyzing transaction data and identifying unusual patterns, the system provides real-time alerts and reduces fraudulent activities. The report covers techniques such as supervised learning, unsupervised learning, deep learning, and natural language processing (NLP), along with their applications, benefits, and challenges.. Python is employed for analysis, emphasizing the ability of deep learning to manage and prevent fraud in real-time on dynamic datasets. In the end, this study concludes that by using deep learning algorithms, we can control online credit card fraud detection in banks, in  this  project  efficency  to improve the efficiency of the banking system. We can manage fraudulent activity in real-time and on dynamic datasets by utilizing deep learning algorithms, which allows for ongoing improvement of the fraud detection and prevention system.