How NLP and Artificial Intelligence Can Optimize Claims Processing In Insurance

How NLP and Artificial Intelligence Can Optimize Claims Processing In Insurance

Do you know why insurance has always been one of the most complex & comprehensive industries? Let’s discover why.

Basically, Insurance is based on three main components:

  1. Legal aspects
  2. Data Analysis
  3. People on the Frontline

Claims processing has always been one of the most difficult and one of the most important areas to make a customer happy.

One of the simplest ways to improve customer service – organize claims and make the processing as quick and smooth as possible.  Also note that it is very important to detect fraud in time and react as soon as possible to avoid negative consequences for you business. The quality of claims processing should also be far better and accurate than average to satisfy clients.

Today is the era of digital revolution. Youngsters became more mobile than ever, and use all types of devices in daily life. It’s not enough to provide high-quality insurance services to stay in trend and increase revenue. Companies should become more flexible and agile if they want to grow.

We will analyze claims processing to propose the optimal solution for AI and NLP integration at the lowest cost and maximum profit both to the company and clients.



Traditionally, insurance companies use people on the frontline without any digital support. The widely spread software that is used: Word, Excel and a simple CRM without integration with email or IP telephony. A lot of time is wasted for filling client information into a table, and later to CRM.

Another issue is personnel. Claims processing is a headache for some companies because of the way managers communicate with clients. Without a doubt, in every company, there are rules and policies that govern the communication with clients, but your managers can’t always be absolutely correct in dealing with customers even if they follow the scripts step by step. The situation when your client is calling his agent and is being transferred from one manager to another also appear occasionally. So the customer service is not perfect.

Another well-known problem is the applications and websites that are used by insurance companies to provide remote services. Usually, interfaces look pretty modern but are not easy to use for the first time. Also, insurance companies are trying to include all possible functionality in applications and websites. The main concern is that the new user can’t deal with that functionality, especially when there are dozens of menus, submenus, checkboxes and so on. In an attempt to create the complex application that can satisfy all customer needs, developers and insurance companies forget about usability. In that situation, a user would prefer to call a live person rather than to read the manual.

In other words, the main problems of claims processing on the frontline are people and uncomfortable user interfaces with poor user experience. That situation can be easily improved with modern technologies such as Artificial intelligence and Natural Language Processing.



Unfortunately, when people call their insurance company to make a claim, they are usually not prepared properly and can’t provide all required information for fast claim processing. Also, some clients call exactly after the accident in the affected state, so they can’t even explain what happened to them. It is quite hard to get information from such people. As a result, clients create delays and reduce the productivity of the claims processing department.

There may also be a problem with the information that was provided by e-mail. It is great when that information is filled in the correct way, but anyway, the employee must check and formalize that information.

It often happens that the provided information is incorrect, for example:

  • Incorrect patient information
  • Incorrect provider information
  • Incorrect insurance provider information
  • Incorrect codes
  • Mismatched medical codes
  • Duplicate billing and etc.

Usually, all client information is automatically validated on a website, later by human. But it can also be processed by an algorithm, and even more efficiently. Neural Networks find all necessary information much faster than human due to features of the algorithms, that machines use for data processing. It’s not surprising that today more and more companies prefer custom software to process data instead of the personnel because the probability of mistake made by machine is much lower. If you rely on custom software, you will forget personnel complains about difficult and routine work. The only thing your personnel should do is checking the results.

Finally, you need to control your personnel for resultative work, because your profit and customer satisfaction directly depends on your managers and the way they work.



If you haven’t heard about NLP yet you might be interested in it.

Natural language processing (NLP) is the area of Artificial Intelligence related to the interaction between computers and natural languages. The main purpose of NLP is to process human language in different ways and find relations between input language and the sample data. Based on that relations a computer decides what to do. Google Assistant, Amazon Alexa, Apple Siri, and Microsoft Cortana use NLP in this or that way. Basically, they record a voice message, compress it and send to the server. At the server side, that message is uncompressed and analyzed by machine learning algorithms. As a result, the system understands the user request and sends back the feedback. It is quick enough, but it depends on many factors – from language support to bandwidth speed.

Real-time NLP is slightly different from traditional NLP used by voice assistants: computer processes incoming data and sends the result as soon as possible, without sending that information over the Internet. Usually, it is fast enough and extremely close to real-time.

In the first place, real-time NLP can be used during the phone call. AI-based solution recognizes client’s speech and automatically fills out the claim form. Speech recognition can be based on the list of words related to every field of the quote. If some fields are not filled or filled incorrectly, the system could ask the respective questions.

In the second place, Machine Learning can be used for email processing. Text messages are easier to process, as there is no need to recognize the human speech. The information is already provided in an easy-to-work-with format.

Moreover, AI that uses real-time NLP can be used alongside with an existing manager that processes claims. That approach will boost manager’s productivity and decrease the number of mistakes made due to negligence.

Finally, the client could be fully served during only one phone call, without redirects to other managers and repeated calls.



Even a few years ago Artificial Intelligence software development was quite expensive, but today situation has changed. Nowadays, AI solutions and neural networks use popular frameworks and libraries in their core, most famous of them are:

  •    TensorFlow maintained by Google
  •    Microsoft CNTK created be Microsoft
  •    Theano used by Amazon
  •    Caffe used by Motorola
  •    Keras used by Bell AeroSpace

Almost all the frameworks and libraries are fully open-source and contributed by well-known companies or independent developers. They are secure and respect user privacy. For a common developer this means that everyone can use it to build their own AI. For business this means a great reduction of development cost, for instead of developing a complex framework that will become the core of the system, developers have the opportunity to use well-known and documented solution.

It should be also noted that an hourly rate of development is also decreasing. It’s easy to find highly qualified developers at affordable rates all over the globe. If you have an international project, you can rely on outsourcing dedicated team. As already been said that price would be also reduced if the developers use open-source frameworks.

Another issue is data that is needed for successful neural network training. It’s quite hard to provide the sufficient data amount. But it doesn’t totally applies to insurance industry, as an insurer stores thousands of phone call records. So most of the companies are already prepared to analyze existing phone calls and compare the data from the forms that were filed manually by the managers.

As the matter of fact, the receipt to minimize development and integrations cost consists of three points:



Real-time NLP provides several benefits to an insurance company:

  1. Great improvement of customer service
    No more impolite managers. No more redirections from one manager to another. One manager can provide full range of services in short terms and without delays.
  2. Accelerated claims processing
    Intelligent software can analyze incoming information – both human speech and plain text – faster than human. Algorithms can process data 24/7. The only thing that people would do –  check data after machine and confirm the result.
  3. Faster manager response time
    No need to wait until the manager finishes previous call, you can address the client to any free telephone line, where chatbot will intelligently collect data. And any time manager can connect to the phone call and ask several questions directly to the customer.
  4. Increased claim department productivity If to use a chatbot like an assistant, not as an independent solution, it will decrease the number of mistakes made by personnel and increase a number of processed claims offers per manager.

By the way, such a system can be easily integrated with insurance claim anti-fraud solutions. Such integration will lead not only to claims processing optimization but also decrease the number of fraudulent claims.


Are you thinking about a custom AI project related to Insurance Industry? Call us +1 973 577 72 76 for a quote!

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