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A Systematic Review of Financial Translation in the Digital Age: Trends, Challenges, and Human–AI CollaborationAbstract
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.
Keywords:
financial translation
artificial intelligence (AI)
translator roles and skills
human-AI collaboration
translation ethics
translation workflows
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1. Introduction
Over the past two decades, financial translation has shifted from a largely text-bound craft into a complex service embedded in globalised finance. In cross-border banking, international accounting, fintech development, and investment reporting, the translator is expected to deliver not only precise wording but also documents that meet the regulatory and institutional conditions of multiple jurisdictions. A missed nuance in a prospectus or a misread clause in a compliance report can have tangible market consequences, and so accuracy is joined by the less visible work of aligning with policy frameworks, industry norms, and the expectations of stakeholders who may never meet face-to-face.
These demands have emerged alongside the steady arrival of artificial intelligence (AI) into almost every corner of the workflow. Neural machine translation, computer-assisted translation (CAT) tools, post-editing platforms, and, most recently, large language models now appear in the translation process at points where manual work once dominated. A translator may be curating terminology in one moment, revising machine output in the next, and advising on compliance language before the end of the day. Such hybrid practice has altered the pace of work, the shape of professional competence, and the boundaries of translator responsibility.
Studies in the last few years have charted this change from different angles. Ciobanu et al. (2024) describe how speech-enabled post-editing reduces cognitive strain without eroding quality, while Pym and Hao (2024) report gains in speed and accuracy when generative AI tools are folded into task-specific translation. Sector-focused models such as BloombergGPT (Wu et al., 2023) and InvestLM (Liu & Tang, 2023) extend this further, offering domain-trained systems for tasks that range from regulatory summarisation to sentiment analysis. Yet these same tools provoke questions about bias, interpretability, and the diffusion of responsibility when human and machine decisions intertwine (Weber, Carl, & Hinz, 2024).
Not all of the conversation has centred on tools themselves. Human-centred approaches have gained weight, with scholars like Jiménez-Crespo (2025) urging interface designs that preserve translator judgement and agency. In high-risk environments, such as financial risk disclosure or legal compliance, this remains critical. AI systems can be fast and consistent, but they still falter when asked to handle contextual nuance or culturally loaded phrasing (Coeckelbergh, 2023).
Although the literature is growing, coherent and systematically grounded overviews remain rare. Earlier reviews have tended to be narrative rather than systematic, leaving gaps in tracking long-term changes or drawing together research on ethics, competence, and technology under a common frame (Falempin & Ranadireksa, 2024; Mohsen, 2024). Many also predate the surge in generative AI and domain-specific modelling now reshaping how translation is taught, regulated, and practised.
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This study addresses those gaps through a systematic literature review (SLR) of peer-reviewed work on financial translation published between January 2000 and January 2025, drawing on English- and Chinese-language sources indexed in Web of Science, Scopus, and CNKI. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol provides the organising framework, while the Mixed Methods Appraisal Tool (MMAT) and the Critical Appraisal Skills Programme (CASP) guide quality assessment.Three research questions shape the review: First, what main themes in financial translation research (2000–2025) connect with institutional expectations and market conditions? Second, how have AI and digital tools changed workflows, competencies, and quality in financial translation? Third, how do existing conceptual and collaboration models address ethics, cognitive demands, and human–AI workflows?
The analysis draws on Actor–Network Theory (ANT), Augmented Cognition, and Moorkens’s process model. Together, these perspectives make it possible to trace the socio-technical networks in which financial translation now unfolds and to support the Human–AI Collaboration Model developed later in this study. By bringing empirical mapping into conversation with theoretical insight, the review aims to offer a resource for training design, technology development, and policy thinking in a sector where linguistic expertise must continually adjust to evolving institutional and technological realities.
2. Methodology
The review was designed as a SLR of scholarship on financial translation published from 2000 through to the first month of 2025. The framework that underpins this process is PRISMA, which is less a set of bureaucratic steps than a way of thinking about how sources are located, examined, and sifted in view of a defined purpose (Moher et al., 2009). Its stages, while presented here in sequence, overlapped in practice: the outline of the study, the gathering of records, the selection rules, the checks on quality, and finally, the thematic interpretation of what remained.
2.1 Research Questions
The methodological process described in this section was designed to address the three research questions outlined in the Introduction. These questions focus on identifying the main thematic strands in financial translation research, examining the influence of AI and other digital tools on practice and competencies, and analysing the conceptual and collaboration models that shape human-AI workflows in the sector.
2.2 Data Sources and Search Strategy
The search itself was carried out in January 2025. Three large databases—Web of Science, Scopus, and CNKI—were used because together they cover translation studies, linguistics, information science, and the communicative aspects of finance. Search strings were built with Boolean operators, drawing on combinations of “financial translation,” “neural machine translation,” “post-editing,” “AI in translation,” “translator competence,” “workflow,” “terminology,” “translation ethics,” and “collaborative translation.”
The focus was kept on peer-reviewed journal articles, book chapters, and conference proceedings in English or Chinese. By setting the time frame from January 2000 to January 2025, the review was able to take in both formative studies and the most recent contributions. The first sweep yielded 582 records. Once duplicates were removed, the remaining items were brought into Zotero for screening.
2.3 Inclusion and Exclusion Criteria
Eligibility depended on three conditions. A study needed to be concerned directly with financial translation or translators working in financial contexts; it had to present empirical findings or a well-articulated conceptual model; and it needed to engage in a sustained way with one of the core topics—digital tool use, competence, training, or ethics.
Material was excluded if it offered little or no methodological detail, if it dealt with domains outside finance, or if it was an opinion piece, editorial, review, or news item without theoretical grounding. With these filters in place, 342 unique studies went forward for abstract screening.
2.4 Screening and Quality Assessment
Titles and abstracts were examined first, followed by a full-text review where initial checks suggested relevance. Of 191 articles read in full, 73 met all the requirements and entered the final synthesis. The process is summarised in the PRISMA flow diagram (Fig. 1).
Fig. 1
PRISMA flow diagram
For quality appraisal, two established instruments were applied: the Mixed Methods Appraisal Tool (MMAT) and the Critical Appraisal Skills Programme (CASP). They offered a shared basis for evaluating research design, internal coherence, and evidential grounding across qualitative, quantitative, and mixed-methods work. Weaknesses were not uncommon—method descriptions that lacked clarity, very narrow sampling, or conclusions only loosely tied to the data. Only studies meeting the agreed threshold were retained (O’Brien, 2019; Oliver & Álvarez-Vidal, 2023).
2.5 Data Extraction and Thematic Analysis
For each included study, key information was recorded: authorship, date, methodology, geographic scope, technology type, theoretical stance, and main findings. Coding was carried out in NVivo 14, which allowed for consistent categorisation while leaving space for themes to surface through inductive analysis.
This was paired with bibliometric mapping. Co-citation analysis and keyword clustering, undertaken with CiteSpace and VOSviewer, brought to light research concentrations, nascent areas of inquiry, and influential models. The combination of manual coding with these visual tools gave the synthesis both breadth and a finer level of interpretive detail.
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The outcome is not just a catalogue of studies but a textured account of how the field has developed over a quarter of a century, an account that underpins the theoretical discussion to follow.3. Theoretical Framework
The present landscape of financial translation cannot be explained solely through the lens of linguistic transfer. It requires a conceptual approach that engages with the frictions between technological change, human judgement, and the institutional settings in which translation unfolds. Classical models such as Skopos theory and functionalism remain valuable for understanding purpose-driven adaptation, yet they do not capture the hybrid, technology-mediated workflows now taking shape. This review, therefore, anchors its analysis in three strands of theory—ANT, Augmented Cognition, and Moorkens’s process model—which together make it possible to trace the socio-technical dynamics underpinning financial translation and to support the collaborative framework outlined later.
3.1 Actor-Network Theory: Translation as a Distributed Activity
Latour’s (2007) formulation of ANT positions translation within a mesh of interconnected agents, human and non-human alike. In financial contexts, these agents may be translators, clients, regulators, project managers, CAT tools, neural MT engines, or institutional memory systems. Within such a network, agency is shared. Choices about terminology, tone, or equivalence are shaped not just by individual skill but by the affordances and constraints of the surrounding infrastructure.
Empirical work has used ANT to examine how translators negotiate platform-based workflows, conduct post-editing, and manage the authority of automated systems (Freitag et al., 2021; Kenny, 2020). The environment of financial translation, which is dense with compliance rules, client glossaries, and demands for speed, means that the translator functions as one node in a larger decision-making ecology. This lens is well-suited to revealing how human-AI interdependencies evolve and where influence lies.
3.2 Augmented Cognition: Extending and Shaping Human Decision-Making
While ANT describes the structure of relationships, Augmented Cognition focuses on how technology interacts with, and sometimes reshapes, human cognitive processes. Rooted in cognitive engineering and human-computer interaction, it regards translators as agents whose work is mediated through—and subtly steered by—digital tools (O’Brien, 2019; Moorkens, 2019). Features such as predictive typing, adaptive MT engines, and quality estimation modules do more than offer support; they direct attention, shape perception, and influence how meaning is assembled in the target text.
Studies indicate that translators using AI-driven systems work in iterative cycles of anticipation, revision, and validation (Castilho et al., 2020; Kruk & Kałużna, 2025). This pattern shows that success in financial translation depends as much on integrating machine suggestions into compliant, coherent outputs as it does on subject expertise, with professional and ethical judgement acting as a constant counterbalance. The approach also draws attention to competencies that are gaining importance: post-editing agility, digital literacy, and the capacity for strategic oversight.
3.3 Moorkens’s Process Model: Linking Environment, Action, and Quality
Moorkens’s (2020) process model connects ANT’s broad network view with the fine-grained focus of Augmented Cognition. First designed for post-editing research, it sets out translation as a system with clearly defined inputs, environmental factors, operational stages, and evaluation points. It highlights how elements such as deadlines, file formats, or platform compatibility intersect with translator actions, and how quality is upheld through constant monitoring and adjustment.
In finance, where even minor errors may incur legal or reputational costs, this processual approach has particular relevance. It embeds accountability within the workflow rather than treating it as an afterthought. Evidence supports its applicability: Moorkens and O’Brien (2019) link interface design to efficiency and cognitive load, while Sakamoto (2020) finds that workflow optimisation requires repeated calibration between human and technological input.
3.4 Toward a Synthesised Perspective
Together, these three frameworks provide a layered analytical lens. ANT maps the actors and their interrelations; Augmented Cognition examines how translators engage cognitively with the tools at hand; and the process model integrates these insights into a structured, feedback-driven sequence.
This synthesis is the foundation for the collaborative model presented in Section 5. Figure 2 visualises the principal actors, cognitive mechanisms, and systemic constraints within hybrid financial translation. By moving beyond purely descriptive accounts, it explains how the field adapts to technological, institutional, and cognitive pressures, and points towards workflow designs that combine technological efficiency with the interpretive strengths of human expertise.
Fig. 2
Synthesized framework for human–AI financial translation
4. Thematic Findings and Synthesis
The review of seventy-three studies reveals a body of work that, despite its variety, clusters naturally around four broad concerns. These are: how terminology and genre operate in financial translation; the changing nature of workflows and the tools that sustain them; the shifting profiles and skill sets of translators themselves; and the ethical and structural conditions under which the profession functions. The themes emerged through an iterative reading of the literature, supported by co-citation maps, keyword patterning, and inductive coding in NVivo and CiteSpace. Although each strand has its own history, none has remained untouched by the steady advance of intelligent technologies, whose influence now reaches from the minutiae of term choice to the negotiation of professional ethics.
What is striking is the sheer interdisciplinarity that runs through the material. Financial translation sits at a point where linguistics meets economics, where regulatory discourse brushes against rhetorical strategy, and where both are inflected by technological mediation. For ease of reference, the themes are summarised in Table 1, which lists representative studies under each heading, though the discussion that follows often cuts across these categories.
Theme | Key Focus Areas | Representative Studies |
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Terminology & Genre | Multilingual terminology, text typologies, corpus approaches | Biel (2017), Muftah (2024), Fischer et al. (2017) |
Workflow & Tools | CAT, MT, post-editing, automation, platform-based translation | Moorkens & O’Brien (2019), Castilho et al. (2020), Freitag et al. (2021), Kornacki & Pietrzak (2024) |
Translator Roles & Training | Competence frameworks, cognitive effort, training programs | O’Brien (2019), Sakamoto (2020), Kruk & Kałużna (2025) |
Ethics & Market Structures | Professional identity, accountability, transparency, commodification | Calzada Pérez (2018), Mohamed et al. (2024), Oliver & Álvarez-Vidal (2023) |
4.1 Terminology and Genre
In the earlier part of the period under review, roughly from 2000 to the middle of the following decade, scholarship placed heavy emphasis on the specific lexical and structural patterns that give financial discourse its distinctive profile. Research such as Šarčević (2015) and Biel (2017) underscored how precision in terms, especially in risk disclosures and accounting statements, is not merely a matter of stylistic neatness but one with legal and financial weight. Semantic slippage here could alter the interpretation of a liability or obscure the extent of a risk. Corpus-based investigations and genre-analytic frameworks were often used to pin down the recurring forms and pragmatic moves that anchor this discourse in different languages.
Over time, however, interest began to shift from static terminological inventories to a more dynamic view. Muftah’s (2024) diachronic study of Arabic-English financial glossaries is one example, showing how the spread of digital finance and the rise of ESG reporting have opened up new lexical territories, forcing translators and compilers to adjust rapidly. In another vein, Fischer et al. (2017) drew on bilingual corpora to reveal how Chinese IPO prospectuses weave in metaphor and euphemism as a way of framing risk—choices that complicate the translator’s task of maintaining fidelity while satisfying regulatory expectations.
Metaphor, in fact, has been a recurring point of difficulty. Some idioms, rooted in local cultural reference, resist direct transfer; others, by contrast, circulate so widely through English-led markets that they have become almost lingua franca in form (Biel, 2017). For translators, this means constantly weighing the pull of global intelligibility against the value of retaining local texture in financial storytelling.
Genre-oriented studies have run in parallel to this terminological work. Analysts of annual reports, audit opinions, and investor briefings have mapped a consistent set of obstacles: the dense legalistic syntax; the embedded references to domestic statutes; and the interplay of textual and visual modes in tables, charts, and annotations. The consensus is that without a clear grasp of genre conventions, pragmatic coherence is easily lost in translation.
More recently, the shape of these genres has itself been shifting. The migration of financial communication to digital platforms, from investor portals to mobile fintech interfaces, has blurred the edges between informative, promotional, and regulatory discourse. Translators now confront hybrid artefacts in which hard data sits alongside persuasive narrative, often in multi-modal form. This evolution calls for strategies that preserve factual accuracy without flattening the rhetorical force that the source text was designed to carry.
The work in this thematic strand makes it clear that financial terminology should be seen less as a closed lexicon than as a living system, subject to market movement, policy change, and technological mediation. The genres through which it is expressed are equally in flux, requiring a translator who can adapt not just to new words, but to new communicative ecologies. The next section turns to how these demands intersect with changing workflows and the tools that enable them.
4.2 Workflow and Tools
Over roughly the past twenty years, the sequence of actions involved in financial translation has changed in ways that would have been hard to anticipate at the turn of the century. A translator who once moved steadily from source to target, often with nothing more than a text editor, now works inside a shifting arrangement of interconnected tools. It is not so much a single leap forward as a gradual accretion: computer-assisted translation (CAT) environments, machine translation (MT) modules, translation management systems (TMS), and automated quality assurance (QA) have been layered together until the older linear model has almost disappeared.
CAT platforms remain the primary workspace. They split documents into segments, track terminology, and draw on translation memories to reuse earlier solutions. Increasingly, these are not stand-alone systems. MT suggestions—sometimes from neural engines tied to an institution’s own database, sometimes linked in through an API—arrive on the screen while drafting is under way (Moorkens, 2020). The process no longer feels like a clear first-then-second step; reviewing, reshaping, and deciding on MT output happens in the same breath as original composition.
Around this sits the managerial layer provided by TMS platforms. These handle file movement, assign work, and track progress in real time. Many now offer dashboards, predictive quality metrics, and automatic prompts when terminology or formatting diverges from agreed norms (Castilho et al., 2020). In the financial sector, such monitoring has a practical edge: meeting filing deadlines or regulatory cut-offs often depends on this constant oversight.
A further twist has been the quiet spread of adaptive MT and predictive typing. These systems adjust their suggestions in response to the translator’s habits, the type of document, or a shared institutional memory. Li et al. (2023) discuss how AI translation tools are aligned with the specific needs of financial discourse, emphasizing the cross-sectoral survey findings that underline the necessity for translation tools to meet the diverse demands of the financial industry (Li et al., 2023). In their recent study, Kruk and Kałużna (2025) observed that predictive input increased the pace of banking report translation by over 20 per cent without harming terminological accuracy. Even so, they noted the temptation to lean too heavily on the offered default phrasing.
QA has also moved into real-time. Many translators now work with background checks that watch for mistyped figures, unit mismatches, date formats, and departures from an agreed style sheet. This can be invaluable when a project involves multiple authors or languages, where small inconsistencies are easily missed (Freitag et al., 2021). But here too there is a limit: without careful configuration and professional judgment, automated QA can give the appearance of precision while overlooking deeper semantic issues.
Reactions to this changed environment are far from uniform. Vieira and Specia (2023) found that while productivity gains were broadly welcomed, some translators spoke of crowded screens and a sense that their decisions were being steered by the interface itself. In high-stakes work, such as major corporate filings, the unease is sharper; the reputational damage of a minor mistranslation can be considerable. Muñoz (2024) points to the cognitive effort involved when post-editing, navigation, and background research all have to be managed at once.
There is no single template. Large investment houses may run a tightly integrated CAT-TMS-MT-QA system with centralised oversight, whereas smaller firms or freelancers often piece together a looser set of tools and rely on manual checks. Table 2 sketches these arrangements, from highly automated corporate models to minimal digital support, along with the automation levels, cognitive load, and typical use cases attached to each.
Workflow Type | Tool Configuration | Degree of Automation | Cognitive Load | Common Use Cases |
|---|---|---|---|---|
High-Integration | CAT + MT + TMS + QA suite | High | Moderate–High | Corporate reporting, banking M&A |
Mid-Level Integration | CAT + MT + standalone QA | Medium | Moderate | Insurance documents, investor decks |
Freelance Hybrid | CAT + third-party MT + manual QA | Low–Medium | High | SME reports, compliance notices |
Minimal Digital Support | CAT only (manual QA, no MT) | Low | Moderate | Specialist commentary, audits |
What emerges from this strand of findings is that tools are not simply neutral aids to speed. They have begun to reshape the mental routines, the pace, and even the sense of authorship in financial translation. Designing workflows that use automation effectively, yet leave room for professional agency, will be as much a part of future research as tracking the next round of technical innovations. The following section turns from systems and processes to the human actors within them, including their roles, their skills, and how both are changing.
4.3 Translator Roles and Training
In recent years, the speed of change in financial translation has been difficult to overstate. It has not simply altered the tools in use or the order in which tasks are done; it has also redefined the very outline of the translator’s role. What was once imagined almost exclusively in linguistic terms is now stretched to cover the mediation of specialist knowledge, the management of digital processes, and the willingness to make ethical calls in complex, hybrid settings. These shifts follow the broader movement in the profession towards multi-competence and technology-integrated work.
The familiar core remains. Precision with terminology, control of syntax, sensitivity to tone—these are still non-negotiable, particularly when a text will be used in mergers and acquisitions, high-risk financial reporting, or cross-border compliance. Yet the list is longer now. The normalised presence of MT, post-editing platforms, and expansive terminology databases has produced workflows where human and machine are in constant exchange. The translator must be able to handle post-editing at speed, navigate different software environments without friction, and, crucially, decide within seconds whether an AI-generated proposal should be trusted. This is technical work, but not only that; it draws on digital literacy, contextual reasoning, and the steady alertness required to spot where algorithmic bias may creep in (Moorkens & O’Brien, 2019; Massey & Ehrensberger-Dow, 2021).
Competence models have adjusted in response. Both the European Master’s in Translation (EMT, 2022) and the Process of Acquisition of Translation Competence and its Evaluation (PACTE) group’s framework depict translation as an activity that draws on linguistic, intercultural, technological, ethical, and institutional resources. Within the context of financial translation research, these frameworks can be read not only as training blueprints but also as conceptual models that articulate how translators’ roles evolve under technological and institutional pressures. In the EMT outline, for example, “technological competence” and “service provision competence” appear alongside linguistic expertise—an explicit signal that the translator must be capable of managing projects, monitoring version histories, and shaping outputs that satisfy client expectations for quality, security, and turnaround (González-Davies, 2022).
Still, there is a persistent mismatch between the frameworks on paper and the training found in many institutions. Reports point out that numerous university programmes still privilege theoretical study or literary translation, leaving graduates with limited experience of financial subject matter or digital workflows. This shortfall can slow their entry into the profession and, in some cases, lead to uneven quality in the first years of practice (O’Brien et al., 2023; Valdez & Liu, 2024).
Attempts to close the gap have taken different forms. Some programmes have moved toward collaborative designs, with universities working alongside tool developers, financial organisations, and professional associations. Trials in both Europe and Asia have included virtual internships and sandbox platforms where students can practise post-editing anonymised financial texts while receiving immediate feedback. These spaces allow for immersion in workplace conditions, but they also leave room for observation—how the tool behaves, how risk is assessed, how revisions evolve (Sakamoto, 2020; Shih, 2023).
Ethics is no longer treated as peripheral. As AI systems take over more decision points in the production chain, the translator’s capacity to intervene, to question, and to reframe becomes vital. Training modules now often include confidentiality, transparency, and accountability in the handling of sensitive or regulated data. They also raise the issue of shared authorship in post-edited texts, asking where responsibility rests when human and machine contributions are woven together (Taibi & Valdez, 2023).
The role of the financial translator now sits at the intersection of technology, subject expertise, and professional judgement. Whether the task is interpreting central bank statements, producing market commentary under tight deadlines, or aligning a text with shifting compliance demands, human expertise is central. Training that equips practitioners with both technical agility and ethical grounding will be essential.
As shown in Table 3, these are the competencies that now anchor the financial translator’s evolving role.
Competency Area | Description | Key References |
|---|---|---|
Linguistic Competence | Mastery of financial terminology, syntax, and stylistic conventions across language pairs | EMT (2022), Massey & Ehrensberger-Dow (2021) |
Technological Proficiency | Familiarity with CAT tools, TMS platforms, and post-editing techniques | Moorkens & O’Brien (2019), Sakamoto (2020) |
Ethical Judgment | Awareness of AI bias, data privacy, and shared authorship responsibility | Taibi & Valdez (2023), Shih (2023) |
Institutional Knowledge | Understanding of financial systems, regulatory frameworks, and client expectations | González-Davies (2022), Valdez & Liu (2024) |
Strategic Adaptability | Ability to navigate hybrid workflows, shifting standards, and evolving professional roles | O’Brien et al. (2023), Sakamoto (2020) |
Equipping translators with this full spectrum of competence is one way to secure a profession that is both technologically confident and ethically aware.
4.4 Ethics and Market Structures
As intelligent systems and large-scale digital infrastructures have moved from peripheral to central in financial translation, the ethical and structural questions have only multiplied. AI tools and data-driven platforms are now embedded in everyday practice, and with them comes a shifting balance between opportunity and risk. In a sector where the smallest interpretive slip can carry outsized legal or reputational consequences, these are not abstract debates.
Confidentiality is the concern most easily named. Financial documents carry details that may be commercially sensitive—earnings projections, investment strategies, proprietary models. Passing such material through a cloud-hosted MT engine or remote TMS can expose it to unauthorised access, data leakage, or disputes over jurisdiction. While secure, on-premise systems are standard in many high-stakes institutions, freelance translators and small agencies often lack these protections. As Bowker (2021) and Moorkens (2022) note, the divide in security infrastructure reinforces the unevenness of ethical safeguards across the profession. As seen in Table 4, ethical tensions in human–AI financial translation scenarios include issues related to data confidentiality, algorithmic bias, accountability diffusion, regulatory lag, and economic precarity.
Bias in algorithms is a quieter but equally significant problem. MT trained on narrow, unbalanced, or outdated datasets may introduce subtle shifts in meaning; Terminological distortion, culturally skewed readings, or evaluative language that was never intended. In the financial sphere, such distortions can influence investor mood, sway market reactions, or enter into regulatory records. Taibi and Valdez (2023) point out that these are not isolated glitches but structural effects of how data is chosen, models are built, and oversight is applied.
Responsibility in post-editing remains an unsettled area. In human-machine collaborations, where errors occur, the chain of liability is often unclear: was it the post-editor, the project lead, or the MT system’s developers? Biel (2023) argues that this diffusion complicates established ideas of translator agency and highlights the need for explicit attribution models.
Regulatory measures lag. Standards bodies such as ISO (2015) and ASTM International (2014) have revised ethical guidelines for translation, but harmonised global rules on MT in official financial documentation are still lacking (Kaindl et al., 2020). The absence of consistent policies leaves space for discrepancies between jurisdictions.
Market shifts exacerbate the situation. The growth of platform-based service models and consolidation among large vendors has concentrated control, driving down rates for post-editing work. Freelancers are often left to manage these conditions without adequate training or technical support. Algorithmic pricing and the persistent belief that MT invariably speeds delivery add to the precariousness (Moorkens, 2020).
There are, however, signs of organised response: shared authorship agreements and human-in-the-loop workflow design, both of which function as emerging collaboration models linking human expertise with technological processes, alongside translator advocacy initiatives. Yet without stronger institutional support and more coherent policy frameworks, these remain partial answers to complex problems.
Ethical Issue | Affected Stakeholders | Potential Mitigation Strategies |
|---|---|---|
Data confidentiality | Translators, clients, regulators | Secure platforms, on-premise solutions, NDAs |
Algorithmic bias | Translators, investors, compliance teams | Balanced corpora, bias detection protocols, human oversight |
Accountability diffusion | Translators, managers, AI developers | Clear attribution models, contractual clauses |
Regulatory lag | Regulators, corporations | International harmonisation, regular policy updates |
Economic precarity | Freelancers, small agencies | Fair rate guidelines, training provision, transparency in pricing algorithms |
Ethics and market structures are not separate from the technology story; they are woven through it. Taken together, the four thematic strands explored in this section address the three research questions set out in the Introduction, linking thematic mapping to the influence of technology and to the frameworks for ethical and collaborative practice that are shaping financial translation in the digital age.
5. Human–AI Collaboration Model in Financial Translation
The deepening integration of artificial intelligence into financial translation has reshaped not only the technical configuration of workflows but also the interaction between human expertise and machine systems. Earlier sections examined shifts in practice, competence, and ethics; here, these strands are synthesised into a conceptual framework that maps the interplay of actors, processes, and feedback in hybrid environments. Drawing on the theoretical lenses outlined in Section 3 and the thematic findings of Section 4, the model proposes a structured view of how translation is now produced in a complex network of people, tools, and institutional constraints.
At its centre lies the recognition that financial translation is no longer a simple transfer of meaning between languages. It is instead iterative, collaborative, and highly mediated. Human translators, post-editors, algorithmic engines, terminology databases, and institutional stakeholders each play a role, their contributions embedded within interdependent relationships. The framework organises this activity into five dimensions: task inputs, actor configuration, processes, outputs, and feedback loops (Fig. 3).
Fig. 3
Synthesized human–AI collaboration model
Inputs refer to the factors that shape the initial conditions of a task—source texts, client specifications, regulatory constraints, deadlines, and reference materials. Each introduces its pressures. For instance, differing jurisdictional standards in multilingual financial reporting influence terminology, tone, and formatting. The clarity of inputs sets the baseline for downstream efficiency and cognitive demands on translators.
Actor configuration identifies the range of participants, human and non-human. Translators, editors, and project managers operate alongside CAT tools, MT engines, and quality estimation modules. Following Actor-Network Theory (Latour, 2007), the model views these participants as nodes in a network, their influence determined by expertise, interpretive capacity, and the ability to recalibrate outputs. Machine actors can accelerate decision-making, but their parameters are ultimately shaped by human agency and organisational rules.
Processes describe the workflow stages, from pre-processing and machine translation through human revision, quality control, and validation. Adapted from Moorkens’s process model, the sequence may vary according to text type and risk level. Annual disclosures, for example, often require multi-tier review, while internal memos may follow a leaner path. The model accommodates both linear and iterative approaches, recognising that in agile settings revision and evaluation often loop back into earlier stages.
Outputs encompass the deliverables, from translated investment reports to multilingual fintech platforms. Quality is measured not only in linguistic terms but also in compliance, usability, and adherence to institutional voice. Automated QA tools contribute quantitative checks, yet final evaluation typically rests with human reviewers who weigh measurable indicators against qualitative judgment.
Feedback loops close the system, returning information from completed tasks to earlier stages. Updates to translation memories, post-editing data feeding adaptive MT engines, and user feedback shaping interface design are all examples. Feedback also operates at a learning level: translators reflecting on errors, inconsistencies, or system limitations improve their future performance. This reflects the Augmented Cognition perspective (Stanney & Schmorrow, 2008), which treats adaptation as essential to human-technology collaboration.
The model is deliberately flexible, allowing for variation across institutional contexts, language pairs, and tool ecosystems. A multinational accounting firm might privilege traceability and consistency, while a fintech start-up may prioritise speed and customisation. It also makes visible the points where ethical issues can emerge: over-reliance on automation, lack of transparency in MT suggestions, or ambiguity in authorship.
Three theoretical strands converge here: Actor-Network Theory clarifies how agency is distributed; Augmented Cognition illuminates the cognitive dimension of decision-making; and Moorkens’s systems perspective structures the procedural flow. Together, they provide an explanatory account of hybrid financial translation.
Beyond scholarship, the framework has practical utility. It can inform curriculum design, guide platform development, and support policy-making in multilingual financial communication. Identifying weak links—unclear task delegation, ineffective feedback systems, or poor integration of ethical safeguards—can lead to targeted interventions. For example, refining feedback loops could enhance translator engagement and system learning, while explicit actor mapping could reduce role ambiguity.
While the model offers a coherent picture of collaborative translation, its value depends on how it is applied and adapted in real contexts. The next section turns to the systemic challenges that will shape its implementation and the methodological robustness of the evidence base underpinning it.
6. Challenges and Quality Appraisal
The model of human-AI collaboration outlined earlier offers a useful frame for understanding how financial translation is changing. Yet, when the detail of practice is examined, persistent difficulties become apparent. Some are rooted in technology itself, others in the institutional settings where translation takes place, and still others in the way the field’s scholarship has been built. None of these are easily resolved, but they are central to ensuring that financial translation remains both effective and credible.
6.1 Systemic Challenges in Human-AI Financial Translation
Opacity is perhaps the most widely acknowledged concern. In many organisations, neural MT systems and large language models are embedded in secure platforms, but the decision pathways that shape their output remain hidden. Post-editors may therefore correct sentences without knowing why an error arose or how to prevent similar issues later, a problem that is magnified in high-stakes contexts such as multilingual regulatory disclosures or investment reporting.
Questions of quality assurance are closely related. Automated checks now flag inconsistencies in terminology, syntax, or formatting almost instantly. Yet they can miss pragmatic nuance, semantic drift, or regulatory misalignment across jurisdictions. As noted in Section 4.2, such tools can produce texts that look correct while falling short in communicative terms.
Other concerns are more human in origin. As machine suggestions dominate the drafting space, translators risk becoming passive correctors rather than active interpreters. Over time, this can weaken autonomy and erode the skills that underpin the profession (Moorkens & O’Brien, 2019; Massey & Ehrensberger-Dow, 2021). Ethical tensions also persist: proprietary systems may restrict how far a translator can intervene, or embed biases inherited from skewed training data, a risk that is acute in value-laden domains such as ESG reporting.
Institutions vary considerably in how they deploy AI tools. Some multinationals have built integrated platforms with training programmes and feedback channels, while others assemble a patchwork of tools with minimal oversight. The result is uneven quality and inconsistent expectations, leaving individual translators to bridge the gap without clear guidance.
6.2 Quality Appraisal of Reviewed Studies
The studies included in this review, 73 in total, were chosen through the PRISMA process and appraised with the Mixed Methods Appraisal Tool (MMAT) (Hong et al., 2018) and the Critical Appraisal Skills Programme (CASP, 2018). Around two-thirds were methodologically strong, offering clear questions, solid data, and coherent reasoning.
In earlier work, especially studies on initial MT adoption in financial contexts, theoretical framing was sometimes absent, and descriptive reporting took the place of analysis. More recent studies show a different limitation: a narrow focus on speed, error rates, or edit distance, often at the expense of cognitive load, user experience, or organisational complexity.
Geographic and linguistic scope remains narrow. Most research comes from Europe or China, with little representation from Latin America, Africa, or Southeast Asia. This leaves important questions unanswered about how financial translation is shaped by different legal regimes, market pressures, and institutional structures. Cross-linguistic or bilingual comparisons are still rare despite the multilingual nature of the field.
Interdisciplinary engagement is growing but remains patchy. Some studies draw on human-computer interaction, cognitive science, or sociolinguistics, yet many remain within the boundaries of translation studies. Without stronger links to AI ethics, digital regulation, or cognitive ergonomics, the field risks addressing technological change with a limited toolkit.
There are signs of progress: more recent research is methodologically varied, empirically grounded, and more willing to consider the socio-technical dimensions of AI-assisted work. Building on these trends will require greater transparency, broader representation, and ethical alignment if both theory and practice are to move forward together.
7. Conclusion and Future Directions
This review set out to follow the changing shape of financial translation as technology and institutional practices have advanced. From 73 peer-reviewed studies published between 2000 and early 2025, the analysis traced a steady movement away from purely text-equivalence toward a multi-layered, technology-mediated craft. Themes such as terminology management, workflow design, the scope of translator competence, and the weight of ethical pressures have shifted alongside broader changes in digital infrastructure and professional norms.
The work today is rarely confined to the page. Translators operate within environments where intelligent systems, institutional rules, and human judgment converge. Interfaces must be navigated, regulatory obligations met, and decisions negotiated with others—particularly in high-stakes domains such as multilingual disclosure, fintech localisation, and investment communication. The model outlined in Section 5 draws together Actor-Network Theory, Augmented Cognition, and process-based perspectives to show how translation happens through a web of actors, linked tasks, and feedback loops.
Technological tools, such as neural MT engines, CAT platforms, QA modules, do bring gains in speed and consistency. Yet they leave untouched the translator’s ability to reason through ambiguity, reconcile cultural and regulatory demands, and adapt outputs to context. The review also revealed where the field is thinner: the cognitive and emotional strain of hybrid work remains under-examined; many training programmes still lag behind industry needs; and questions of authorship, bias, and transparency are addressed only sporadically in tool or workflow design.
The way forward will demand a more human-centred research agenda, one that examines not only capability but also the lived experience of use. Collaboration across linguistics, finance, technology, and policy will be essential for systems that are efficient, accountable, and responsive to practice.
Table 5 summarises six areas for targeted work: translator cognition and wellbeing; ehical frameworks for AI integration; quality in agile workflows; updated competence models; tools customised for financial subdomains; and the influence of institutional logics and market pressures. Each links theoretical insight to practical change.
Future Research Area | Key Questions | Theoretical Contribution | Practical Implication |
|---|---|---|---|
Translator Cognition and Wellbeing | How do translators experience cognitive and emotional strain in hybrid workflows? | Deepens understanding of human–machine cognitive dynamics | Supports ergonomic tool design and mental health awareness |
Ethical Frameworks for AI Integration | Who is responsible when automated systems introduce bias or error? | Advances shared authorship and AI governance theories | Helps build accountability mechanisms and clearer credit structures |
Quality in Agile Workflows | How can quality be assessed when workflows are fast, iterative, and tool-assisted? | Refines quality assessment beyond binary accuracy | Informs development of integrated, adaptive QA systems |
Updated competence models | What new skills and mindsets should future financial translators develop? | Updates pedagogical frameworks to match evolving roles | Supports curriculum reform and certification benchmarks |
Tools customised for financial subdomains | How well do current tools serve specialized financial content? | Links translation technology to discourse-specific needs | Encourages more targeted tool features and user interfaces |
Institutional Logics and Market Pressures | How do financial institutions and platforms shape translation workflows? | Extends ANT into real-world decision contexts | Guides procurement, staffing, and workflow optimization |
Sustainable integration of AI will also require standards with the practical force of ISO 17100, adapted for AI-assisted translation. These must cover data handling, define authorship, and assign responsibility when errors occur. Developers, for their part, should design tools with adaptability, transparency, and human judgment in mind. Ethical concerns, including bias and over-reliance on automation, should be addressed from the outset. Where technology grows in step with practitioner insight, it can strengthen rather than diminish the expertise that continues to define financial translation.
Acknowledgement
The authors express profound gratitude to the editor and the anonymous reviewers for their invaluable feedback and insightful recommendations, which have significantly enriched this research. We acknowledge the contributions of scholars whose works have paved the way for this research.
Disclosure
statement
No potential conflict of interest was reported by the authors.
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Funding Information
2025 General Project of the Guangdong Provincial Philosophy and Social Science Planning Program: A Study on the English Translation of Metaphors in Chinese Political Discourse with Chinese Characteristics in the New Era(Number: GD25CWY02)
Ethical Statement
This study adheres to high academic standards. No empirical research involving human or animal subjects was conducted, ensuring ethical integrity and respect for all cultural and philosophical references.
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Author Contribution
A.S. conducted the literature search, performed data extraction and analysis, and drafted the initial sections of the manuscript, including methodology and theoretical framework. W.H. conceptualized the study design, developed the Human–AI collaboration model, synthesized thematic findings, and revised the manuscript for theoretical depth and coherence. All authors contributed to editing and approved the final version of the manuscript.
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