Insurance Analytics

Improve Insurance Claim Processing With Automated Analytics

An employee gets hurt on the job, leading to a workers’ compensation claim. This employee hasn’t been injured before and is only expected to miss a couple days of work. Sounds like it should be a simple claim, right? It turns out this claim ends up costing the insurance company 3 times what they initially thought it would cost! What happened?!?

Oftentimes a claim which initially seems simple can end up costing much more than expected. This is a major challenge for insurance companies. Deciding how many resources to allocate to the processing and investigation of a claim to minimize the total cost to the insurer is not an easy task.

In this post, we will explore workers’ compensation insurance claims and how Tellius can be used to understand claims and predict when they will become complex. In this example, a complex claim is a claim that ends up costing much more than the initial cost. If an insurance company can better predict when this is likely to happen, they can be more efficient in their claims processing and better manage their fixed resources.

What’s in the Data?

The claims dataset we are exploring contains information on the injured employee such as

  • Age, tenure, and wage information
  • Type of injury, injury history, and medical prescriptions
  • How long it took to report the injury and whether there is an attorney involved

When an employer makes a claim, there is an initial cost. For claims that have been closed we know the final claim cost. Taking the difference between the final cost and the initial cost gives us a notion of claim complexity. A large difference between the final and initial cost indicates a complex claim.

Since there are many pieces of information about each claim, it is difficult to investigate each one and figure out if there are certain patterns that make it more likely for a claim to be complex. This is where Tellius can help by leveraging machine learning techniques to tell us why some claims end up being complex.


Automated Analytics for Your Claims Data

Tellius offers a new breed of analytics that combines the power of machine learning with a user’s domain knowledge. To illustrate the power of Tellius, we can create a story (a type of automatic analysis) to better understand the characteristics of complex claims. The interface below shows how we can create a story to investigate what drives complex claims.

All that is required is to tell Tellius to investigate what causes complex claims. Taking this simple input to define a behavior present in the data, Tellius will automatically

  • tell us which features in the data are the most useful to predict which claims will be complex
  • show us different groups of claims that are often complex
  • create a model to allow us to predict which future claims will be complex

Complex Claims Tell a Story

This story focused on complex claims shows several “segments” or groups of insurance claims that are very likely to be complex. For example, we can see that claims which

  • take more than 5 days to be reported
  • involve an opioid prescription
  • and involve legal representation

turn out to be complex claims 89% of the time, which is 8.7 times more likely than the average claim.

This is one of many segments which are automatically learned from historical claims data. From here we can dive deeper to understand these segments. We can see which features have the largest impact on the likelihood of becoming a complex claim and investigate the interactions between these features. This is a much more efficient alternative to manual exploration of complex claims.


Understand and Operationalize

The output of this story allows us to understand different situations that lead to complex claims. We can also take action by making predictions about what will happen when a new claim comes in.

Behind the scenes Tellius has trained a machine learning model which can be used to predict how likely a new claim is to be complex. With one click, we can use that model to make predictions or set up a workflow to make predictions on a recurring basis. You can also create a customized model fine-tuned to your specifications. Taking this step to operationalize what you can learn from past claims data is the key to cost savings and more efficient claim processing.

In summary, Tellius provides a very user-friendly way to leverage the power of machine learning to investigate claim outcomes. With this increased exploratory and predictive power, insurance companies can become much more efficient in their claim processing.


Want to learn more?


Contact us here to schedule a demo to learn more about how you can use Tellius to improve insurance claim processing.


Leave reply

Read Similar Posts

  • Insurance Analytics

    Using Tellius Automated Machine Learning to Predict and Operationalize Credit Risk Insights

    In this post, we use publicly available data to classify segments of customers who are at a higher risk of credit default. Read on to see how Tellius can help financial services organizations make better credit risk decisions today.

  • Insurance Analytics

    How Machine Learning is transforming the Insurance industry

    For Risk Managers, taking chances simply is in their DNA. New systems aren’t adopted unless everyone is 1000% certain that...

  • Insurance Analytics

    Customer Segmentation via Clustering in Tellius

    Background In a previous post, we described two approaches to performing customer segmentation in Tellius: