Abstract
In the rapidly evolving digital economy, Financial Transaction Management (FTM) systems have become the backbone of secure, efficient, and compliant financial operations. As financial institutions face increasing demands for real-time processing, cross-border interoperability, and intelligent automation, legacy FTM systems are proving inadequate. This review provides a comprehensive theoretical exploration of the modernization and optimization of FTM systems, focusing on emerging technologies, data integration architectures, and predictive optimization frameworks. Drawing from over thirty authoritative academic, regulatory, and industry sources, the paper critically examines existing research on transaction data structures, technological enablers such as AI, blockchain, and cloud computing, and presents a novel model—the Integrated Data Fusion Optimization Model (IDFOM)—that unifies predictive analytics, dynamic resource allocation, and adaptive compliance mechanisms. Through real-world case studies and comparative performance analysis, the IDFOM is demonstrated to significantly enhance fraud detection, compliance accuracy, and operational efficiency. The review concludes with practical and policy recommendations, outlining future research directions in explainable AI, federated data governance, and global regulatory harmonization. This paper contributes to the theoretical foundations of next-generation FTM systems and offers a strategic roadmap for practitioners, policymakers, and researchers.