This study focuses on how automation and human expertise interactively shape financial translation workflows. Specifically, it aims to examine the effectiveness of automation with and without human judgment, identify the influence of financial technology (FinTech) on the cost-efficiency of financial translation, and study the role of regional factors in shaping the implementation of FinTech in translation practices. Taking a mixed-methods approach, the study reviews twenty financial translation projects from 2021 to 2024. It includes press releases, product brochures, project reports, and contracts, each involving a blend of automated tooling with human oversight. For data collection, we reviewed the academic literature and industry reports, and interviewed translators and project managers. Drawing on the technology acceptance model (TAM) and transaction cost theory (TCT), regression analysis and paired-sample t-tests were used to analyze the data, showing significant differences in automation effectiveness. Automation managed approximately 63% of the translation workflows for routine documents such as press releases. However, when it came to more complicated content types like financial reports, the contribution of automation declined to about 32%, highlighting the importance of human expertise in these types of translation tasks. The cost analysis revealed a significant decrease in labor costs, falling from 61.85% to 41.38%. This reduction was, however, partially balanced out by higher spending on training and security measures. Furthermore, regional variation in FinTech penetration was closely tied to data privacy, an environment of regulatory statements, and industry-specific demand for services. To conclude, the study highlights the need for tailored strategies balancing efficiency and accuracy, considering regional contexts in global financial communication.