By Tom Moran, Head of Growth at LanguageLine Solutions
Historically, digital transformation in international insurance has centred on velocity, be that processing cross-border claims faster, assessing multinational risks in real time or settling disputes with minimal friction. Workflows stripped delay out of the customer journey, but that drive for speed created an unscrutinised vulnerability across financial services – the rapid rise of unverified generative AI.
Facing a complex international claim file, a foreign medical report or nuanced policy wording, underwriters and claims handlers naturally seek the path of least resistance. To meet internal speed targets, free web tools or general-purpose AI models offer the path of least resistance, given they can generate a translation in seconds, but carry an inherent risk of inaccuracy, compliance and lack of localisation.
Facing a complex international claim file, a foreign medical report or nuanced policy wording, underwriters and claims handlers naturally seek the fastest route. To meet internal speed targets, turning to free web tools or general-purpose AI is tempting, with 72% of financial firms identifying unauthorised “shadow” AI usage within their operations[1] and 65% reporting employee use of unapproved tools with clients[2].
While driven by operational efficiency rather than malice, this habit skips essential compliance safeguards. Standard Large Language Models (LLMs) – even secure enterprise platforms – and public web tools are built for general speed, not contract law, localised dialect or regulatory precision, introducing severe risks around accuracy and compliance.
In an industry where words dictate legal liability, relying on unverified AI translation creates unprecedented compliance, data privacy, and litigation exposure. For financial institutions, valid translation requires domain-specific verification – ensuring AI outputs align against specialised medical and insurance glossaries and local jurisdictional contract law – along with mandatory expert human oversight before any claim or policy decision is finalised.
Why unverified AI translation is so dangerous
In insurance, language is a binding contract, evidence, consent and liability. A policy term, exclusion, warranty or medical detail can materially alter coverage interpretation and legal obligations.
For cross-border claims and international policies, this challenge is only amplified. Teams must scrutinise every policy detail, customer communication, and claim submission, all while navigating complex language barriers under tight time constraints.
Many firms believe they have mitigated this risk by deploying secure enterprise AI environments. While these walled-garden LLMs resolve the immediate issue of public data leaks, they create a dangerous false sense of security regarding translation and localisation.
Enterprise models are trained on general-purpose datasets and lack the domain-specific calibration needed for legal precision, jurisdictional nuance and localised dialects. The fundamental danger of generic AI models is that they rarely fail loudly – they fail fluently.
A standard AI tool produces an output that looks polished, grammatically seamless and entirely convincing on the surface, while quietly stripping away legal intent, regional context or regulatory nuance underneath. The frontline employee sees instant execution and zero security alerts; the firm silently inherits unquantified legal risk. Where employees do resort to unapproved public tools, those compliance issues grow instantly into severe GDPR and confidentiality breaches.
The Consumer Duty trap and the audit trail gap
This issue directly engages the Financial Conduct Authority’s (FCA) Consumer Duty framework, which requires firms to deliver clear, accessible communications tailored to their intended customers – particularly those displaying characteristics of vulnerability.
Non-native English speakers often face capability-related vulnerabilities. Relying on enterprise AI that misses localised dialect or nuance can lead to severe customer misinterpretation regarding coverage or claims decisions.
As the regulator scrutinises vulnerable customer outcomes, compliance leaders must look past written security policies and evaluate daily operational realities by asking four critical questions:
- Where do frontline teams go for fast translations? Uncovering daily workflows reveals the gap between official Standard Operating Procedures (SOPs) and real-world execution.
- Are existing internal enterprise AI tools configured specifically for legal and linguistic precision, or merely general fluency? Identifying where unverified, general-purpose outputs enter official record systems exposes hidden operational liabilities.
- What policyholder data has entered tools outside approved procurement and processing arrangements? Pinpointing unapproved public tool usage isolates active GDPR and confidentiality exposures.
- Can the firm produce a defensible audit trail for every customer-facing translation? Evidencing the source, output, tool used, risk classification, review decision and final communication is critical during regulatory reviews.
Moving to continuous governance
Banning AI in today’s digital landscape is unrealistic. Instead, compliance and operational leaders should consider how teams navigate existing workflows using both public web tools and enterprise platforms.
When facing heavy caseloads, employees understandably look to accessible tools to manage immediate operational demands.Outright bans can unintentionally push this behaviour underground into “shadow AI” usage, while assuming enterprise AI is “good enough” can leave the firm exposed to quiet accuracy failures.
Rather than relying on generic shortcuts, leadership must establish governed, secure pathways. A modern translation strategy relies on an end-to-end hybrid architecture rather than an all-or-nothing approach.
In a governed pipeline, high-consequence, customer-facing or legally binding content is first processed through secure, specialised machine environments calibrated for legal accuracy. It is then dynamically routed to qualified human linguists for validation and nuance checks before reaching the customer.
By partnering with specialised language partners to embed secure machine translation and expert human oversight directly into existing operations, insurers give teams the speed they crave while providing compliance leaders with the full auditability, data security and legal precision required by regulators.
About the author
Tom Moran is Head of Growth at LanguageLine Solutions, where he leads the organisation’s strategic initiatives to deliver secure, compliant and technology-enabled language access solutions across regulated industries. With deep expertise at the intersection of operational efficiency, regulatory compliance and language access, Tom works closely with executive leaders in insurance and financial services to modernise their multilingual communication infrastructure. His focus centres on helping global firms communicate clearly across every customer touchpoint – from policy wording and complex claims processing to real-time customer support – with accuracy, linguistic precision and speed at scale. He helps firms deploy audited, hybrid workflows, combining on-demand and scheduled interpretingand AI-enhanced translation validated by expert human review.
[1] https://www.culture.ai/solutions/financial-services
[2] https://financialit.net/news/banking/70-uk-finance-professionals-see-ai-driving-future-cross-border-banking