A Systematic Review of Financial Translation in the Digital Age: Trends, Challenges, and Human–AI Collaboration

In recent years, financial translators have found their work unfolding at a point where intelligent systems meet shifting industry norms and evolving professional identities. The present review follows these changes from 2000 onwards, focusing on the rise of artificial intelligence (AI), the reshaping of translator roles, and the redefinition of institutional expectations. Studies in English and Chinese were drawn from Web of Science, Scopus, and CNKI; from these, 73 were chosen through the PRISMA process and assessed with the Mixed Methods Appraisal Tool (MMAT) and the Critical Appraisal Skills Programme (CASP). The analysis brought to light four recurring areas: approaches to financial language and genre, ways tools are woven into workflows, adaptations in competencies and training, and the ethical or market conditions framing the work. Actor-Network Theory, Augmented Cognition, and Moorkens’s process model together informed a Human-AI Collaboration Model, in which translation is understood as an iterative exchange between human expertise and technological capacity. While automation has increased both speed and consistency, it also prompts concerns over responsibility, cognitive demand, and autonomy—questions that remain unevenly addressed and point to the need for wider geographical coverage and more diverse professional perspectives.