With growth estimates for the insurance industry in the US estimated to reach $6.4 Trillion by 2025, this industry is a critical part of our economy, but at the same time is also complex and challenging to understand. Even if you’re not in the insurance industry, you’ve probably been affected by it at some point. Whether you’ve had to file a claim or just paid your premiums, the insurance industry impacts all of us. 

With the pandemic, economic downturn, political polarization, climate crisis and other global events, insurance companies have had to increase premiums and tighten underwriting standards to offset the increased costs of claims. In this trillion-dollar industry, the full potential and efficiency are yet to be unlocked. 

As the insurance market rapidly develops, Natural Language Processing (NLP) technology will play a crucial role in its progress. Many AI platforms make it easier for more companies to harness the power of Machine Learning (NLP) and NLP for their business; Accern is one of them.

In this article, you will learn how NLP can help insurance firms can get insights into: 

  • Accelerated Underwriting / Insurance Application Processing
  • Accurate Underwriting Risk Assessment
  • Claims Reserve Optimization Model Enhancement
  • Automated Claims Processing
  • Claims Fraud Detection Model Enhancement
  • Improved Customer Service
  • New Product Development

But first, let’s understand the impact of NLP on the insurance industry.

Impact of NLP on the Insurance Industry

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that deals with the interaction between computers and human (natural) languages. 

NLP is used to develop applications that can understand and interpret human language and respond naturally to humans. With the proliferation of the written word – text in online news, blogs, reviews as well as emails, chats and other internal documents – this new technology is a must-have for all companies.

NLP provides two key benefits to the insurance industry: cost reduction and fast and accurate results.

NLP Reduces Costs for Insurance Companies

NLP is helping companies reduce costs by automating tasks that previously required human labor. For example, companies can use NLP to extract information from unstructured data sources, such as: 

  • Applications for insurance
  • Company documents
  • Insurance policies
  • Insurance claims
  • Internal and customer emails
  • Customer support tickets
  • Chats and reviews
  • News and blogs

This can free up employee time to focus on more valuable tasks, from developing new products to providing customer service. Additionally, NLP can help automate customer service tasks, such as answering frequently asked questions or routing customer calls. This can drastically help companies save on labor costs and reduce operating costs.

NLP Delivers Fast and Accurate Results for Insurance Companies

The insurance industry deals with an extensive amount of text documents and manual review is both time consuming as well prone to errors. NLP can automate the extraction of relevant information from such text documents, classify documents as well as use relevancy scores and sentiment scores to rate documents for risk or urgency. 

Once processed and scored using NLP, documents can be automatically routed to appropriate departments or analysts for further review. Automated document workflows, extraction and categorization can significantly reduce the amount of time it takes to process insurance claims and respond to support tickets as well as increase the accuracy with which they are processed.

Let’s dig in and understand the use cases of NLP in insurance.

1. Accelerated Insurance Underwriting

Underwriting is the process of assessing risk and determining whether or not to provide insurance coverage for a business and accepting liability if something goes wrong. It also includes the calculation of the amount of premium to charge.

Insurance companies use a variety of factors to determine whether or not to provide coverage, including the type of business, how long it has been operating, financial metrics of the business, for example amount of revenue, financial history, for example bankruptcies, property and other assets it owns, safety and security systems and history of prior insurance claims. 

Document Tagging

NLP can help with insurance underwriting by automatically extracting information, tagging and classifying documents from within the applications for insurance packages. With NLP, an underwriter does not have to waste time extracting key information from the variety of documents that make up an application for insurance.

With the Accern built-in taxonomies, you can get a quick start extracting relevant information from application forms and integrating it into your workflows. Accern then lets you further enhance and customize taxonomies for your particular workflow.

2. Accurate Underwriting Risk Assessment

Risk assessment helps insurance companies determine the likelihood that a policyholder will file a claim. An insurance company can set premiums covering the expected claims costs by understanding the risk factors associated with a particular policy. 

Document Classification

Once NLP has been used to tag, classify and summarize documents within an application for insurance, this information can be used to flag applicants who are at a higher risk of filing a claim as well as speed up manual reviews. 

Predictive Model Enhancement

Additionally, NLP can help insurance companies turn qualitative data about a business – descriptions of its operations, news, reviews about the business or other factors like local weather conditions or social unrest – into numbers or quantitative parameters that can be fed into predictive underwriting models that factor in other risk parameters associated with similar businesses.

3. Claims Reserve Optimization Model Enhancement

Automated NLP workflows can also provide real-time monitoring and risk assessment so that insurance companies can continuously adjust how much they need to set aside to cover claims. For example, natural disasters can have a significant impact on the insurance industry. In fact, Hurricane Katrina resulted in over $40 billion in insurance claims. With NLP, insurance companies can continuously monitor news and view changes to potential exposure.

With Accern’s built-in data sources including news and blogs, you can quickly get insights that will help you assess risks more effectively than you can with manual analysis.

4. Automated Claims Processing

Claims processing is the process of reviewing and approving insurance claims. An insurance company receives thousands of claims a year. This significant number of requests generates high volumes of tasks that are both time-consuming and tedious for claims examiners. 

Integrating NLP-based solutions into a firm’s workflow can save claims agents hours on other activities, prevent losses, and reduce human error in these tasks. Insurance professionals must check eligibility, coverage, loss details, severity of the damage and the policyholder’s history of filing claims and more in determining whether or not to approve a claim.


NLP can help companies comply with FINRA Rule 2090 and automatically extract Know Your Customer (KYC) information from documents related to a claim. The KYC due diligence process was put in place to protect firms from fraud and corruption and prevent them from unknowingly facilitating illegal activities through their organization.

NLP is also helping insurance companies extract additional KYC data from proprietary, third-party and alternative, public, open sources to capture a better understanding of the customer.

Document Summarization

Another use for NLP in claims processing is for extracting information from insurance policies and claims submissions, tagging and scoring claims and summarizing statements in documents.

NLP models can automatically sort, file, and extract meaning from thousands of documents and records for each customer and compare them against historical records to gain a deeper understanding of the situation.

Simpler cases can be handled with automated workflows and quick manual reviews if needed while high value and high complexity claims can be reviewed by expert claims adjusters. 

5. Claims Fraud Detection Model Enhancement

Fraud detection is essential for insurance companies for several reasons. 

First, insurance fraud costs the industry billions of dollars yearly, raising premiums and making it more expensive for the majority of honest policyholders. 

Second, insurance fraud undermines the public’s trust in the industry, which makes it difficult for insurance companies to sell policies and operate effectively. 

Third, fraud can cause insurance companies to become insolvent, which would devastate the economy. 

Finally, insurance fraud is a crime, and insurance companies have a responsibility to help law enforcement agencies investigate and prosecute the fraudsters. Natural language processing can help detect fraud and prevent money laundering in many ways. 

First, NLP helps to process and analyze large amounts of text data, allowing the identification of patterns of fraud and money laundering. For example, loss descriptions that are duplicated across multiple claims filings or involving common connections between entities.

Second, NLP quantifies textual data and enhances predictive fraud detection models that can be used to flag potential fraudulent or money laundering activity. 

6. Improved Customer Service & New Product Development

Customer service is essential for insurance companies for many reasons. 

First, insurance is a service industry, and customers must be able to rely on insurance companies to provide them with the coverage they need. 

Second, insurance companies must be able to resolve customer complaints promptly and efficiently. 

Third, insurance companies must be able to build and maintain good relationships with their customers. 

Finally, insurance companies must be able to attract and retain customers to stay in business.

One of the ways that NLP can help improve customer service is through chatbots. NLP can help process and analyze customer queries, which can help the chatbot understand the customer’s needs and provide a more accurate response. 

Furthermore, key insights from customer emails and chatbot conversations can be used to improve support with FAQ documents or other help for common requests and proactively offer customer assistance, improving customer satisfaction and loyalty. These insights can also be used to discover new products or serve new customer segments. 

How Accern No-Code AI Can Help Insurance Firms

While the insurance industry begins to embrace recent technological advancements in a way it hasn’t before, AI and ML use in these firms will continue to rise. Saving firms valuable time and money, the sub-specialty of NLP is helping firms extract meaningful insights from text data and operate around the clock to provide real-time updates. 

With a no-code NLP solution, like the one offered by Accern, underwriting departments, claims departments, call centers and data science teams can more easily test these ready-made models without the need for coding. They can then work with IT to quickly integrate them into their daily processes, current workflows and technology stacks. 

Schedule a free demo to learn more about how Accern can power ROI for your department.

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