How to overcome language barriers: AI translation for the insurance industry

Insights

• As the insurance industry pursues new markets, linguistic complexities have rendered 10% to 25% of insurance records unreliable.
• More than 26 million people in the US have limited English proficiency. Globally, linguistic diversity is even greater; the European Union recognizes 24 official languages, and Asia is home to roughly 2,300 languages.
• Compliance with legal requirements underscores the need for multilingual services in the insurance industry, with many jurisdictions mandating that insurance policies and customer support be available in local languages.
• Traditional translation costs can add 10% to 15% to project expenses in the insurance industry. By contrast, AI-powered translation tools have significantly reduced operational costs and translation time for insurers, with some companies reporting up to an 80% reduction in costs and time.
• The global AI language translation market is expected to reach $13.5 billion by 2033, growing at a compound annual rate of 22% from 2024.

The insurance industry faces major upheavals fueled by continuing globalization, heightened customer expectations, and rapid digital innovation — especially in AI. As insurers enter new markets and serve more diverse populations, seamless multilingual communication has become essential for customer engagement, regulatory compliance, and sustained growth.

More than 26 million people in the US have limited English proficiency (LEP), and 68 million speak a language other than English at home. Globally, linguistic diversity is even more pronounced. The European Union has 24 official languages, while Asia is home to approximately 2,300 languages. India alone recognizes 22 official languages and more than 1,600 spoken dialects, while China has over 300 languages. Insurers that fail to provide multilingual support risk alienating potential customers and losing market share.

Beyond accessibility, multilingual capabilities enhance operational efficiency. Industry insights show that linguistic complexities make between 10% and 25% of insurance records unreliable, leading to misinterpretations in policy agreements, claims processing errors, and financial losses. A survey by CSA Research further underscores the importance of language, revealing that 76% of online shoppers prefer buying in their native language, while 40% refuse to buy from websites in other languages. Offering customer service in a customer’s native language fosters trust and loyalty, with companies seeing up to a 20% increase in retention.

Compliance with legal requirements further underscores the need for multilingual services in the insurance industry. Many jurisdictions mandate that insurance policies and customer support be available in local languages. For example, in the US, some states require insurance policies to be provided in Spanish to meet the growing demand for language accessibility. Failure to comply can result in financial penalties and reputational harm, reinforcing the importance of multilingual solutions.

Investment in scalable, AI-driven multilingual solutions is a strategic imperative. The technology allows insurers to enhance customer experience, streamline operations, ensure compliance, and expand into new markets while strengthening their positions in existing ones.

What is wrong with traditional multilingual solutions?

Language has long challenged insurance companies, which traditionally relied on skilled translators and interpreter services to bridge the gap. However, these methods are costly and often do not scale effectively to meet the diverse linguistic needs of a growing customer base. For instance, translation costs can add 10% to 15% to project expenses. As global migration expands and markets become more linguistically diverse, the limitations of traditional translation methods — some of which are outlined below — become increasingly clear.

• Communication delays: Traditional translation workflows slow down critical processes, affecting timely claims processing and customer support.
• Limited scalability: Finding enough bilingual agents to meet language demands is challenging, and staffing often falls short when demand surges across multiple languages.
• High costs: Traditional multilingual support requires substantial investment in staffing, training, and technology, making it less viable for smaller insurers.
• Compliance risks: Inaccurate translations in legal documents can lead to fines and erode customer trust; traditional solutions often struggle to ensure regulatory compliance.
• Inconsistent customer experience: Traditional solutions often fail to deliver uniform service across channels, resulting in customer dissatisfaction.
• Lack of real-time interactivity: Traditional methods do not support instant, dynamic communication, which is crucial for claims processing and other urgent insurance needs.

The shift to AI-powered translations

Organizations accepted the limitations of human translation when it was the only viable option. The first translation software emerged in 1968, and while technological advancements have progressed steadily, they have done so at a slow pace. Only recently have improvements in speed and accuracy fundamentally shifted the cost-benefit equation for insurance companies.

AI-powered tools can now automate translation, significantly reducing time and costs. For example, Yellow.AI deployed a multilingual voice bot for one of the world’s largest insurers, cutting operational costs by 80%. Similarly, LILT’s AI-powered translation services helped Intel lower translation expenses by 40%. Meanwhile, Schroders reported an 80% reduction in translation time.

These individual successes are driving massive growth, reshaping operations in a wide range of industries. The global AI language translation market is projected to reach about $13.5 billion by 2033, with a 22% compound annual growth rate from 2024 to 2033.

However, to implement machine translations effectively, insurers must first understand its key capabilities to make informed decisions that align with their strategic goals.

Advanced natural language processing

To manage the complexities of insurance terminology across multiple languages, insurers benefit the most when they prioritize solutions with robust natural language processing (NLP) capabilities, which allow machines to understand, interpret, and generate human language. NLP offers the following benefits:

• Accurate contextual understanding: Insurance jargon, legal terms, and local idioms require precise translation to avoid miscommunication. Advanced NLP systems can discern subtle differences in meaning and context, ensuring translations are accurate and relevant.
• Support for diverse communication formats: Advanced NLP ensures consistent translation across text, speech, and other mediums, from policy documents to websites. For example, optical character recognition enables accurate processing of scanned policy documents, while voice-to-text translation enhances call center interactions by enabling real-time transcription, improving accuracy, automating call summaries, and supporting sentiment analysis.

AI translation models

The integration of AI-driven translation models, such as neural machine translation, is essential for scalability and accuracy. Important factors to consider include:

• High accuracy rates: The model must provide consistent, error-free translations, particularly for sensitive policy documents and claims processing. Industry-specific training datasets can significantly improve accuracy and contextual understanding.
• Scalability: Before implementation, companies should evaluate whether the solution can add new languages without compromising performance. A scalable system should seamlessly accommodate growing customer bases across diverse regions.
• Integration with human expertise: The combination of AI and human linguistic oversight enhances accuracy, captures nuanced language differences, and ensures cultural relevance in translations.

Integration with existing systems

Efficiency depends on seamlessly integrating multilingual services into existing workflows without major disruptions. Key priorities include:

• API compatibility: The solution should integrate smoothly with existing customer relationship management (CRM), enterprise resource planning, and claims management systems. APIs must support real-time data exchange to ensure translations remain accurate, up-to-date, and contextually relevant.
• Omnichannel support: Companies need to expand multilingual capabilities across websites, mobile apps, chatbots, and call centers to ensure consistent customer experience. Multilingual self-service portals empower customers while reducing operational costs.

Comparing translation solutions for insurance companies

The market offers a diverse range of multilingual translation solutions, each with unique capabilities and strengths. To find the most suitable options for an insurance client, Infosys evaluated more than 25 tools and systematically shortlisted solutions based on key industry-specific criteria. These included support for various file formats, multilingual processing capabilities, regulatory compliance adherence, underlying translation technology, accuracy and automation performance, and the level of vendor support.

To ensure a structured and objective assessment, we assigned weighted scores to each criterion based on our client’s priorities. This approach enabled us to evaluate vendors not only on their technological capabilities but also on their ability to meet the operational and compliance requirements of the insurance industry.

The table below provides a snapshot of our findings, highlighting the key attributes used to assess vendors. This evaluation framework serves as a guiding reference for insurers looking to implement scalable and effective multilingual translation solutions, without endorsing specific vendors (Figure 1).

Figure 1. Vendor analysis of multilingual capabilities

Source: Infosys

Choosing the right multilingual translation solution requires a strategic approach. Insurers must evaluate potential solutions based on their specific operational needs, regulatory requirements, and customer expectations. The following takeaways can help guide decision-making and ensure the selected solution aligns with business goals.

• No perfect solution: There is not a single vendor that excels in every category. The best choice depends on the organization's specific needs and priorities.
• Prioritize needs: Identify the most essential features, such as specific language support, regulatory compliance, and real-time translation accuracy.
• Evaluate customization needs: Assess the level of custom implementation required for each vendor, along with the associated costs and deployment time.
• Assess vendor support quality: Reliable vendor support is essential, particularly for complex implementations that require ongoing maintenance and troubleshooting.
• Account for language nuances: If the target audience includes diverse dialects, especially tier 3 languages — ones that are less widely spoken, regionally confined, or less frequently translated — rigorous testing is essential to ensure translation accuracy and cultural relevance.

Build vs. buy

AI-driven machine translation has become the only scalable solution for managing the insurance industry's vast linguistic demands. However, implementing these capabilities presents a critical choice: build or buy? Companies must evaluate the benefits and challenges of developing in-house AI translation systems versus adopting third-party solutions. Each approach carries significant implications for cost, scalability, control, and time to market. Insurers must carefully weigh the following considerations to decide which option best aligns with their strategic goals.

When to build

Better customization: Developing an in-house AI translation system provides maximum flexibility to meet unique business needs, such as regional dialects and insurance-specific terminology. Insurers can tailor the system to align with workflows, branding, and proprietary data sources, ensuring seamless integration with existing platforms. This approach enables the development of unique features, deeper customization, and greater agility in adapting to future business requirements.

Stronger data control: Developing an in-house AI translation system ensures full control over data security, privacy, and ownership. Insurers can enforce strict data governance policies and maintain compliance with regulations such as the EU’s General Data Protection Regulation. This approach eliminates concerns about data sharing with third-party vendors and reduces the risk of data breaches.

Seamless integration: A custom-built solution can be fully aligned with the organization's existing technology stack, ensuring smooth integration with CRM, claims management, and underwriting systems.

Full maintenance control: Organizations keep complete control over system maintenance, enabling tailored updates and the flexibility to prioritize changes based on evolving business needs.

High scalability growth: As the organization grows, a custom solution can scale to support additional languages, regions, and product lines without relying on external vendors.

When to buy

Faster time to market: Buying a pre-built solution can significantly accelerate the implementation process. Commercial vendors often offer ready-to-use platforms with pre-configured features and integrations, allowing insurers to quickly deploy multilingual capabilities and start realizing benefits soon.

Reduced development costs: The initial investment in a purchased solution is typically lower compared to building a system from scratch. This eliminates the need for significant upfront development costs, including personnel, infrastructure, and project management.

Access to expertise: Commercial vendors often have deep expertise in multilingual technologies, including machine translation, NLP, and localization. This expertise can be valuable for insurers, providing access to best practices, industry benchmarks, and ongoing support.

Proven technology: Purchased solutions are typically well-tested and proven in the market. This reduces the risk of encountering unforeseen technical or performance problems. Vendors often invest heavily in research and development, ensuring that their solutions are up to date with the latest advancements in AI and language technologies.

How to navigate the AI translation frontier

Adopting multilingual AI solutions is no longer a luxury but a necessity for insurers looking to expand globally and serve diverse customer bases. However, integrating these solutions presents challenges. Traditional translation methods are slow, costly, and difficult to scale, while AI-based solutions require careful implementation to ensure accuracy and efficiency. Insurers that do not address these challenges risk losing customers, facing operational inefficiencies, and falling behind competitors that are rapidly embracing AI-powered multilingual capabilities.

To successfully implement multilingual AI, insurers must prioritize key capabilities, such as translation accuracy, domain-specific adaptability, real-time processing, integration with existing systems, and data security. A solution must do more than simply translate words — it must understand insurance terminology and nuances across different languages and cultures. Additionally, seamless API integration and compliance with regulatory requirements are critical for maintaining efficiency and trust.

Finally, insurers must carefully weigh the build vs. buy decision based on their resources, timeline, and strategic priorities. Building an in-house solution provides full control and customization but requires significant investment in AI development and ongoing maintenance. Buying a third-party solution accelerates deployment and benefits from pre-trained models but could involve higher long-term costs and reduced flexibility. The right choice depends on an insurer’s scale, budget, and need for specialized translation capabilities. Regardless of the approach, investing in multilingual AI solutions is essential for insurers to remain competitive in an increasingly globalized and linguistically diverse market.