Big data analytics has emerged as a transformative tool in the financial services industry, particularly in the United States, where institutions manage trillions of dollars in daily transactions. This study explores how financial institutions leverage big data analytics for risk management, with a specific focus on fraud detection and prevention. By integrating advanced technologies such as machine learning and artificial intelligence, big data analytics enables the real-time processing of vast datasets to uncover hidden patterns, identify anomalies, and predict potential threats. Traditional fraud detection methods often fail to address the growing complexity and sophistication of financial crimes. In contrast, machine learning models like Logistic Regression, Decision Trees, and Random Forests provide robust solutions by offering enhanced predictive accuracy and adaptability to evolving fraud tactics. This study examines a dataset comprising demographic, transactional, and geographical features, which are analyzed using machine learning algorithms. In order to guarantee fair and reliable fraud detection systems, the report emphasizes the need to strike a balance between regulatory compliance and technical improvements. The results highlight how crucial it is to include big data analytics into financial risk management plans in order to improve operational security and client confidence. To further increase the effectiveness of fraud detection, future researc...