The rapid growth of digital payment systems and online banking has significantly increased the risk of credit card fraud across global financial networks. Traditional fraud detection systems often struggle to identify sophisticated fraudulent activities due to the large volume of transactions and evolving attack patterns. Cloud-integrated machine learning techniques provide a scalable, efficient, and intelligent solution for detecting fraudulent transactions in real time. This study explores the application of machine learning algorithms combined with cloud computing technologies for intelligent credit card fraud detection. The proposed framework utilizes cloud infrastructure to process large-scale transactional data while employing machine learning models such as Decision Trees, Random Forest, Support Vector Machines, Neural Networks, and Deep Learning techniques to identify suspicious behavior patterns. Cloud integration enhances storage capacity, computational efficiency, data accessibility, and real-time monitoring capabilities. The study also discusses data preprocessing, feature engineering, anomaly detection, and model evaluation methods used in fraud detection systems. Furthermore, the research highlights the advantages and challenges associated with cloud-based fraud detection applications, including scalability, security concerns, latency, and privacy issues. The findings indicate that cloud-supported machine learning systems significantly improve fraud detection accuracy, reduce false positives, and strengthen financial transaction security in modern banking environments.
