A Survey on the Identification of Credit Card Fraud Using Machine Learning with Precision, Performance, and Challenges

Credit card usage is essential in the current economic climate. It has become a necessary component of domestic, commercial, and international operations. Even though there are many advantages to using credit cards when done properly and sensibly, fraudulent activity can result in serious credit and financial harm. Credit card fraud is becoming more of an issue in the financial services industry because more unauthorized payments lead to significant losses. Because of the high number of transactions and changing fraud patterns, traditional rule-based fraud detection techniques are no longer adequate. Machine learning (ML) techniques provide viable ways to analyze trends and anomalies in order to detect fraudulent transactions. This research looks at a number of machine learning methods, including both supervised and unsupervised training strategies, emphasizing their accuracy, effectiveness, and drawbacks. In order to increase detection rates, the study also looks at assessment metrics, data imbalance problems, and new hybrid models. Lastly, important issues including privacy issues, limitations on real-time detection, and changing fraud tactics are covered, highlighting the necessity of flexible and expandable fraud detection systems.