Why Large Language Models Will Change Everything for the Insurance Industry

6 min read

Author: Jannik Malte Meissner

Why Large Language Models Will Change Everything for the Insurance Industry

The advent of large language models like is about to disrupt the insurance industry significantly. These advanced deep learning models have an unprecedented ability to process and generate human language, allowing for more natural conversations and accurate text analysis. Here are some of the key areas where large language models could lead to innovation in insurance:

Customer Service

Chatbots powered by large language models are exceptionally adept at language processing. They can analyze customer queries to derive context and determine intent. For example, understanding if the customer is making an inquiry about a claim, changing their policy, or has a general question.

These chatbots can then respond appropriately, answering directly for common inquiries by accessing knowledge bases and policy documents. For more complex or sensitive queries, they can triage the customer to the right human agent with the relevant expertise. This combination of automated and human assistance provides a seamless customer experience.

Virtual assistants take this a step further with voice-based interactions. Customers can simply speak to the assistant to make inquiries, file claims or conduct other transactions. Voice analysis to understand requests and natural language generation for voice responses make these interactions efficient and high quality.

As a result, customer satisfaction metrics like Net Promoter Score (NPS), Customer Effort Score (CES) and Customer Satisfaction (CSAT) ratings improve. Customers get quick resolution without frustration. And the automation handles high volumes of routine inquiries, freeing up human agents to focus on complex issues. This is possible at a fraction of the costs of traditional call centers.

Overall, the natural language capabilities of AI chatbots and assistants unlock the next level of customer service for insurance companies. Hyper-personalization, contextual interactions and automated self-service will be the norm, leading to happier customers, and in turn, more business.

Claims Processing

Processing insurance claims often requires manual review of lengthy documents like medical reports, police reports, assessor summaries, and policy documents to extract relevant details. Claims processors spend a significant amount of time reading these materials to identify key information needed to update claims systems.

Large language models are adept at natural language comprehension of free-form text. For example, a model trained on medical corpus data can analyze a diagnostic report to automatically extract details like cause of injury, treatment procedures, prognosis etc. and input this structured data directly into the claims system.

Similarly, information from police reports, assessor damage estimates, and policy documents can be automatically extracted to pre-fill relevant fields in the claims system, reducing human effort. Coreference resolution and entity linking capabilities enable the models to connect references across multiple documents and link entities to existing databases.

In Europe, this capability can help streamline claims processing for complex multi-peril insurance policies covering home, auto, health etc. In North America, auto insurers can benefit as medical claims analysis is a major cost factor. Large language models can ingest thousands of claims reports and structured data to continuously improve understanding accuracy over time.

For claimant communications, models can generate customized letters and notices with proper technical terms, coverages and settlement information included, avoiding the need for staff to manually create correspondences. This improves speed and consistency of interacting with claimants.

Overall, we estimate large language models have the potential to automate up to 60-70% of insurance claims processing tasks by leveraging natural language comprehension at scale. This results in significant time and cost savings, while improving accuracy. Some early adopters saw as much as 27% cost reduction with a human in the loop approach.


Underwriting involves the critical process of evaluating customer risk profiles to determine policy terms and pricing. Currently, underwriters manually review documents like medical questionnaires, financial statements, and driving records to assess factors like health conditions, assets, and driving history.

Large language models can ingest these unstructured data sources and extract important risk indicators using natural language processing techniques. For example, medical questionnaires can be analyzed to detect conditions like diabetes, heart disease, etc. Financial statements can be parsed to determine net worth, income stability, defaults, and other financial health attributes. Driving records can be reviewed for past accidents, violations, suspensions to understand risk behavior.

This allows the creation of structured, codified risk profiles for each customer that can be input into pricing algorithms. The algorithms can then accurately calculate premiums, coverages, and policy terms tailored to that particular risk profile. This automation enables exponentially faster underwriting at higher accuracy levels compared to manual underwriting.

Additionally, language models can generate explanations for underwriting decisions which help with transparency and fairness. For instance, clearly citing higher premiums due to specific medical conditions or financial behaviors identified through the risk analysis. Providing explanations is important for regulated insurance markets like the EU.

Overall, large language models have the data processing capabilities to replicate and exceed human underwriting expertise by orders of magnitude, allowing insurers to accurately underwrite policies at massive scale. This unlocks huge efficiency gains while preserving underwriting precision.

Insurance companies operate in highly regulated environments with numerous laws around fair claims handling, data privacy, financial disclosures, and more. Compliance is complex as regulations differ across states and countries.

Large language models can be used through fine-tuning and retrieval augmented generation (RAG) with these regulatory rule books and insurance codes to gain an expert-level understanding of compliance requirements. They can then analyze policies, claims, contracts, marketing materials, and other documents to check for violations of applicable regulations. This allows catching issues early before they become compliance failures.

For example, analysis of claims records can identify patterns like repeated delayed payments to certain demographics of claimants, which may violate fair claims settlement rules. Reviewing policy documents can surface inconsistencies with mandated disclosures. Automated review at scale is much more comprehensive and efficient than sporadic human reviews.

Additionally, for frequently used legal documents like insurance contracts and disclosures, the models can generate customized versions for each client that incorporate the precise clauses, language, and terminology needed to comply with regulations for that client's region and lines of business. This reduces the complexity of ensuring every contract adheres to laws.

In summary, large language models enable insurance companies to integrate compliance checks and controls natively into their core document workflows rather than rely on after-the-fact audits. This proactive approach improves compliance effectiveness and reduces regulatory risk.

Fraud Detection

Insurance fraud through inflated or falsified claims is a major problem, that cost insurances in the United States US$ 308.6 Billion annually according to the Coalition Against Insurance Fraud.  These unnecessary claim costs must be recovered through higher premiums.

Large language models can be highly effective at identifying potential fraud through analysis of claims data. During training, these models can learn complex linguistic patterns that are indicators of fraud by ingesting past labeled claims found to be legitimate or fraudulent.

Some key indicators that can be detected through language analysis include logical inconsistencies in claim narratives, unusual word choices, mismatch between claim details and expected terminology for the type of loss described, and other subtle anomalies. For example, a homeowner’s claim describing stolen jewelry using terminology more consistent with professional appraisals may be suspicious.

The deep neural networks in transformer models are exceptionally good at learning these linguistic nuances from large training datasets. Once trained, the models can score new incoming claims and flag high-risk ones for further investigation. This allows insurers to catch many instances of fraud before processing payments.

With their robust natural language capabilities, large language models have the potential to reduce fraud leakage rates significantly, resulting in major cost savings. Their pattern recognition abilities significantly augment human efforts in fighting insurance fraud.

New Products & Services

Large language models open up possibilities for insurance companies to create innovative products and services powered by AI.

One area of innovation is real-time, contextual insurance for specific events rather than broader policy coverage. For instance, using natural language processing of social media and news feeds, an insurer could temporarily offer flight insurance to travelers specifically affected by a detected airport closure. Or real-time weather data could trigger temporary event insurance for outdoor weddings in case of forecasted rain.

Another opportunity is hyper-personalized health and wellness plans generated by language models analysing a customer's medical records and health history. This could identify specific diets, exercises, lifestyle changes matched to the customer's conditions, demographic profile, and risk factors. The wellness plans can even be iteratively refined based on the customer's natural language feedback on their experience.

More broadly, large language models can digest volumes of unstructured data from weather forecasts, traffic patterns, social media trends, financial indicators, and more to derive unique contextual signals that feed into parametric insurance products covering very specific real-world events and risks. This enables highly customizable and relevant insurance tailored to individual behaviors and needs.

Language models can also generate creative pricing models and personalised policy terms using conversational interfaces. This empowers insurers to experiment with innovative products and underwriting approaches that are data-driven and responsive to each customer's profile and actions.

In summary, insurers can leverage large language models to create the next generation of insurance that is live, contextual, personalized, and creatively tailored to the customer. This unlocks immense potential for product innovation beyond traditional insurable risks.


As evident, large language models have vast potential to transform nearly every aspect of the insurance industry. From improving customer experience to reducing costs and fraud risk, AI-driven automation will enable increased efficiency and competitive advantage. Insurance companies that embrace these technologies early on will be well-positioned to disrupt the market.

Overall, it is an exciting time for insurance as AI propels the sector into a new technology driven era.