Case Study

Insurance Industry

Empowering companies big and small

How can you improve the insurance claim process for customers?

Improving the insurance claim process for customers is extremely important for insurance companies that want to remain relevant. With the increasing demand for personalized experience and other disruptions brought by the pandemic, several insurance practices need to be optimized.

Claim processing can sometimes be complex, expensive, and stressful for customers and insurance companies. Most of its operations are usually not suited for the human intelligence as they are very repetitive and are prone to error.

With recent advancements in Artificial Intelligence (AI) and Natural Language Processing (NLP), claims processing can be automated with great speed and accuracy.

Improve the insurance claim process for customers with UBIAI

In this article, we focus on how we can improve the insurance claim process for customers using AI and NLP.

The world is currently undergoing a Data Revolution. Digitization has been overhauling every aspect of human life and the banking sector is no exception too. Time is money and it couldn’t be any truer than it is in present circumstances.

Owing to the abundance of customers’ data and bank statements, it has become crucial for banks to automate their bank statements for quick and error-free storage and retrieval.

Using NLP to improve the insurance claim process for customers

NLP is a branch of AI that provides computers with the ability to read, process, and understand human language and communication.

Insurance companies can leverage NLP tools to automate many claim processes, speeds up decision-making, reduce costs and errors, and improve customer satisfaction.

We will look at how NLP can be used to enhancing the following practices and how it improves the claim process for customers.

  • Customer service
  • Underwriting
  • Claim processing
  • Fraud detection
  • Virtual assistant

Customer Service

At some point, a customer would have questions about the products and services of your company and would expect a quick response. But due to the high volume of these kinds of inquiries, their case might not get resolved on time.

Interestingly, 40% – 80% of common customer service inquiries can be handled by a chatbot.



That means an intelligent chatbot capable of understanding the customer’s query can handle a large chunk of this load on time, increasing the customer experience.



Equipped with NLP, these chatbots can immediately answer questions about the company, its policies, billing, and products and direct customers to the appropriate information they need. Customer service agents can then focus on more complex issues.


Underwriters usually have to analyze a large amount of data like policies and other documents to assess the risks in insuring a customer asset. Speed and accuracy are much desirable here.


Unfortunately, this process is slow, manual, and error-prone since one is dealing with a high amount of data.


In this case, NLP tools can extract key information like names, dates, diagnoses, treatments from an healthcare claim management record for example, which helps underwriters make their decision faster. Information that otherwise would have taken hours to find is now easily extracted.

Claim processing

An entire article can be written on how to improve the claims process as it is a central practice within the insurance industry.


One method is to use NLP-powered chatbots to help customers automatically fill out claim reports and collect the necessary documents.


NLP models can also be trained to extract information like the damage details or injuries sustained directly from the customer’s report. The agent handling the case can then use this information to verify the claim faster.



With this, submitting and settling claims can be automated, made less expensive, and less stressful for both agents and customers.

Fraud Detection

Unfortunately, some claims will be fraudulent and need to be detected early. It is estimated that over $18 billion is lost to fraud within the medical claim process alone. However, as much as we want to find these cases, loyal customers should be taken into consideration and not have to pass through a more rigorous process.

Manually reviewing claims, emails, forms, and other related documents for fraud does exactly that and can be incredibly error-prone. Thankfully, this process can also be automated with NLP models.

These models are trained on past insurance claim applications (in the medical claims process for example) that have been labeled as either fraudulent or not. They then learn to identify applications with similar characteristics and assign scores based on the likelihood of them being a fraud.

Applications that are flagged to be suspicious are then reviewed by a human who then makes the final decision.

Virtual Assistant

With many procedures involved in claim processing, things sometimes get scattered across different platforms as you might need tools you can’t get from a single platform. For a customer, it poses a lot of inconveniences as they have to refill forms or send the same documents multiple times.


A virtual assistant is an excellent way of integrating these different parts to improve the claim process for customers.

For example, an AI-powered virtual assistant can help collect, process, and review claims, verify policies, pass claims through a fraud detection model, schedule payments, and make payment for the claim settlement, all within minutes.


This quick processing of claims ensures customers are satisfied. And with these virtual assistants running concurrently more claims can be processed.

Data Annotation

The various NLP models and tools mentioned have a lot in common. First, they need to be trained before they can be used. Secondly (and very importantly), their performance depends on the quality of the data used in training them.


While the insurance industry generates tons of data, not all of it is useful. Also, most of this data are unstructured, meaning they have to undergo some form of transformation before they can make sense.


Data annotation tools are at the core of this process. They provide a great way to label and extract the information you need from the vast amount of data and documents in a fast and efficient way.


UBIAI annotating tool, can automatically do this for you. Even more interestingly, it provides support for multiple languages.



With UBIAI tool, you can also digitalize your physical documents like invoices, contracts, medical reports, using Optical Character Recognition (OCR) and extract the relevant information in an intelligent way.


The insurance industry is under extreme pressure even more due to the pandemic. With new AI tools constantly being developed, fraud detection can be smarter, settlements made faster; a lot can be done to improve the claim process for customers.

Integrating these powerful tools into your insurance system will not only increase your business overall productivity, it will also lead to higher profits.

At the core of all these is having a smart annotating tool to transform your data into high quality for training your NLP models.

Whether it’s for a virtual assistant or a fraud detection model, you can easily transform your data, train your NLP models, extract the important information, and deploy, all on a single platform, with the UBIAI tool.

Insurance companies also can take advantage of the several advancements in AI and NLP to improve and automate the claim process for customers.

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If you’re looking for a tagging platform on NLP projects, UBIAI probably has all the features you need. lt supports many document types and multiple OCR engines. It’s super simple and powerful.

Ihsan S.

Machine Learning Engineer

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Jetson W.

Data Architect

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Jasmeet K.

Data Scientist