Financial fraud across cross-border payment systems and blockchain-based transaction networks has grown in scale, sophistication, and velocity, driven by increased digitization, regulatory fragmentation, and the pseudonymous nature of decentralized infrastructures. This study presents a comprehensive examination of AI-driven anomaly detection techniques designed to address these evolving threats. From a broad perspective, the paper reviews the global financial ecosystem, highlighting vulnerabilities in traditional correspondent banking frameworks and emerging decentralized finance (DeFi) architectures. It then narrows to advanced machine learning and deep learning approaches, including supervised, unsupervised, and hybrid models such as autoencoders, graph neural networks, and reinforcement learning systems for real-time fraud detection. Particular emphasis is placed on transaction pattern analysis, behavioral profiling, and network topology modeling to uncover hidden relationships and detect anomalous activities across distributed ledgers and cross-border payment rails. The study further evaluates challenges such as data sparsity, class imbalance, adversarial manipulation, privacy constraints, and regulatory compliance, including AML and KYC requirements. By integrating AI with blockchain analytics and financial monitoring systems, the paper demonstrates how adaptive, scalable, and explainable detection frameworks can significantly enhance fraud prevention capabilities. The findings provide strategic insights for financial institutions, regulators, and fintech developers aiming to strengthen global financial security.