The rapid growth of digital banking in the United States has given rise to new and more elaborate financial fraud schemes that target vulnerabilities within cloud-based infrastructure, which require sophisticated risk control strategies to safeguard both financial institutions and consumers. This study investigated the implementation, effectiveness and challenges of cloud-based risk controls for fraud prevention in U.S. digital banking using a systematic literature review method. This study synthesizes peer-reviewed articles, industry reports and regulatory documents to discuss cloud computing architecture, machine learning practices and regulatory compliance frameworks. The research results show that cloud-based fraud detection solutions with capabilities in AI and behavioral analytics cut fraud losses by more than 40%, decrease false positives by 35% and drive annual cost savings for financial institutions ranging from $3.2 million to $18.7 million. Nevertheless, there remain significant regulatory compliance challenges across federal and state levels, third-party vendor management risks, AI model biases, adversarial attacks and institutional readiness issues related to people's skills and systems' interoperability. This study concludes that effective cloud-based fraud prevention necessitates comprehensive strategies combining new and established technologies with strong institutional processes, regular up-skilling of staff members and regulatory anticipatory engagement to develop resilient, lawful and successful security postures amidst the evolving cyber threat environment.