NLP in insurance
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NLP in the insurance industry: top 5 use cases and benefits

date: 30 January 2024
reading time: 6 min

The insurance industry deals with a huge quantity of data – think of all the files, claims, processes and clients' information that are processed on a daily basis just to get the job done. Traditional ways of dealing with that amount of data are simply not enough, hence the industry's interest in AI in general and in NLP in particular. Let's look at the details.

A brief overview of Natural Language Processing in the insurance industry

Just like in other sectors, for example, finance, NLP entered insurance industry some time ago, and it is gaining more and more attention, revolutionising various aspects of the business.

Natural Language Processing is used in:

  • customer service and support,
  • in claims processing,
  • underwriting and risk assessment,
  • fraud detection, policy recommendation,
  • compliance and regulatory reporting,
  • customer sentiment analysis,
  • document summarisation,
  • risk communication,
  • emergency response.
The benefits of NLP for business
The benefits of NLP for business

With so many different use cases, NLP is enabling the insurance industry to streamline their processes and enhance customer experience while reducing costs and allowing for better, data-driven decisions and more efficient work.

Top 5 game-changing use cases of NLP in the insurance industry

To truly understand the extent to which NLP is a game changer in the insurance world, let’s focus on the five important use cases, showing what exactly they mean and how beneficial they are in the industry.

Streamlining claims processing with NLP-driven automation

Streamlining claims processing with NLP-driven automation leverages NLP to improve the efficiency and accuracy of handling insurance claims.

Instead of dealing with claims manually, NLP algorithms are used to extract relevant information (such as policyholder data, claim descriptions, dates, and other critical details) from unstructured data sources: claim forms, emails, and documents.

NLP and Machine Learning: examples of applications
NLP and Machine Learning: examples of applications

Once done, they automatically categorise and prioritise claims based on their severity and complexity, ensuring that urgent or complex claims receive prompt attention while routine claims are processed efficiently.

NLP also helps identify potential instances of fraud by analysing claim descriptions and comparing them with historical data and known fraud patterns. This method prevents fraudulent claims from progressing further in the process.

What’s more, NLP assist claims adjusters by providing them with relevant information from historical claims and policy documents.

Once a claim is approved, NLP can initiate the process of settlement and payment. It can calculate the amount to be paid based on policy terms, coverage, and claim details, ensuring accurate and timely disbursements. All finished, it can generate detailed and structured reports about claims processing.

In summary, NLP-driven automation in claims processing reduces manual work, accelerates processing times and minimises human error, allowing for more efficient and better operations.

NLP for enhanced customer support and virtual assistance

NLP keeps enhancing customer support literally everywhere, so it’s no wonder the insurance world is taking advantage of it as well.

Insurance companies deploy NLP-driven chatbots and virtual assistants on their websites, mobile apps, and customer portals to respond to customer queries, provide policy information, and assist with various tasks, such as policy renewals and claims submissions.


NLP-driven virtual assistants can guide policyholders through the claims process, helping them complete claim forms and providing updates on claim status. This improves the efficiency of claims processing and reduces the burden on employees.

It can also be used by clients to inquire about policy details, coverage, premiums and other information. Thanks to the translation options, NLP can facilitate multilingual customer support, allowing customers to use chatbots no matter the language they use.

Virtual assistants can also interpret and simplify complex policy documents, making them more understandable to customers, which enhances transparency and reduces confusion.

Another great example of the use of NLP-driven virtual assistants is that they help in handling customer complaints, escalating them to humans when required and tracking the resolution progress.

All of those use cases facilitate better customer service and retention.

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Risk assessment reinvented: NLP’s role in policy underwriting

When it comes to NLP’s role in policy underwriting, it can be used to extract and analyse information from a wide range of unstructured data sources, including news articles, social media, medical records, and customer correspondence.

This data enrichment process provides insurers with a more comprehensive view of the potential policyholder, enabling more accurate risk assessment.

Using NLP techniques we can create detailed risk profiles for applicants by assessing their historical data, behaviour, and public sentiment. This helps underwriters make informed decisions about policy and pricing.

Automation of the collection of data from various sources reduces the administrative burden on the underwriter and ensures a more complete assessment of risk factors, further improving the way the insurance sector works.

Want to know more about NLP? Take a look at the related articles:

Sentiment analysis for customer feedback

Sentiment analysis is a very new and hot subject, allowing companies to analyse and understand customer feedback to improve their operations.

NLP-driven tools allow for automatic data collection from various sources (feedback forms, surveys), processing and sentiment classification, and categorisation of feedback into various sentiment levels.

By analysing historical sentiment data, insurance companies can recognise trends and patterns in customer feedback, which can reveal recurrent issues or changing customer preferences.

Based on sentiment analysis results, insurance companies can make data-driven decisions to enhance customer service, streamline processes, or improve policy offerings.

Sentiment analysis can also serve as an early warning system for customer satisfaction issues. When negative sentiments spike, insurers can take immediate actions to address the problems and prevent customer churn.

NLP for personalised policy recommendations

NLP is also employed to provide personalised policy recommendations, which enhance customer experience by tailoring insurance coverage to the specific needs of individuals.

It serves in the collection of data and its analysis, customer profiling, assessment of the level of risk associated with each customer and the recommendation of suitable insurance policies.

NLP allows also for customisation by adjusting coverage levels, deductibles and other features to fit clients’ budgets and preferences.

How insurers win with NLP integration: the immediate benefits

There is no doubt insurers win with NLP integration and are taking advantage of this new advancement in technology. NLP tools improve organisations’ efficiency, customer service and decision-making processes and reduce the company’s costs.

Predicting the next evolution of NLP in insurance

Although predicting the future is never easy, we can tell which of the uses of NLP in the insurance industry will be further developed in the months and years to come.

The most important of them include:

  • advanced sentiment analysis, allowing not just to understand customers’ sentiments but also to predict them,
  • further development of virtual assistants and chatbots, which will be able to conduct more complex conversations with customers,
  • behavioural analytics, which will allow insurers to gain insights into customer preferences and needs,
  • real-time risk assessment by analysing social media, news and other data sources to adjust policy pricing and coverage dynamically, based on emerging risks.

All of those advancements will help insurers stay competitive, improve customer satisfaction and effectively mitigate risk.

Keen to know more about NLP in the insurance industry? Thinking about investing in it to enhance your efficiency?

Get in touch with our team of experienced experts, ready to share their expertise and knowledge with you to help you achieve your goals!

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