One of the major fraud risks that financial institutions and consumers around the world face is credit card Fraud. This now requires major advanced methods to identify and prevent this from reoccurring. This study compares three main approaches to fraud detection: machine learning, traditional statistical methods, and a combination of both. This research will show how each method works, the cost implications of each method, and the threats and problems that are faced when setting these methods up. We will be highlighting real examples from banks and fin tech companies in the United States, Europe, and West Africa, the research will take us through the day to day standing operating processes. It also points out how results and adoption differ across regions. The findings go to show that machine learning and hybrid approaches generally do a better job at spotting fraud and adapting to new patterns than traditional statistical techniques. However, putting these advanced systems in place is not always easy, as there are issues such as poor data quality, strict regulations, and limited resources, especially in developing regions. This often slows down implementation. The study also finds that using more advanced fraud detection systems can reduce financial losses and increase customer trust, which supports overall economic stability. At the same time, challenges remain, including constantly changing fraud tactics, difficulties integrating new systems with older technology, and a shortage of skilled professionals. Overall, this research provides practical guidance for financial institutions look ing to improve their fraud detection systems and highlights the need to tailor solutions to specific regional and operational conditions.