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Why AI is Required for Transformative Insurance Claims Automation

July 22, 2021 | Artificial Intelligence, Insurance, Intelligent Document Processing

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Carriers know that insurance claims processing is costly mainly because it has traditionally required lots of human resources to examine and process all the required documents and images. There is, however, a viable, transformative solution: using a combination of robotic process automation (RPA) and artificial intelligence (AI) in the form of intelligent process automation to achieve claims automation.

Introducing more technology is an imperative for the insurance industry. A recent report by McKinsey & Company said the insurance industry was “running in place” while other verticals are being transformed by technology and innovation. There are exceptions, however, including “market shapers” – those in the top quintile of the industry. These firms generate more than $750 million in profit annually, more than 20 times the industry average of $37 million. 

Insurance carriers that have made the most progress in growing profits have pursued five bold moves, the report says, one of which is, “Make game-changing improvements in productivity. Reduce costs in line with the top 30 percent of the insurance industry.”  

Improving productivity and reducing costs is what the combination of RPA and AI is all about when it comes to insurance claims automation. It’s a combination that can take out many man-hours, and drudgery, from the process. 


Related content: Achieving unstructured data automation with RPA



Typical claims processing scenario

The typical claims process involves processing numerous documents and images, including First Notice of Loss (FNOL) forms, adjuster notes (which may be hand-written), images of the damage, driver’s licenses and more. 

Electronic FNOLs are becoming more common, with the client using a chatbot on the company website or mobile app to input required information and upload documents. That’s helpful and may make it possible for an RPA bot to pull out some highly structured data from the electronic FNOL form. RPA tools can deal effectively with structured data and processes that involve the same steps over and over, enabling a measure of clams automation.

But even with electronic FNOLs, the there’s still going to be a significant amount of unstructured content to process, including photos and other documents the client may upload. 


Related Post: How Intelligent Automation Helps Insurers Streamline Enrollment Processes



Role of AI and intelligent document processing

This is where AI comes in, or, more specifically, an intelligent document processing tool that takes advantage of AI technologies such as machine learning, natural language processing and transfer learning to achieve greater levels of claims automation.

A good IDP tool that’s trained on a sufficiently large base of labeled data points will be able to process images, PDFs and even those hand-written adjuster notes. The idea is to use the IDP tool to “read” the documents and images much like a human does, pull out all the relevant data required to process the claim, and turn it into a structured format the RPA tool can understand. 

The RPA tool can then be used to populate downstream claims processing systems with this now-structured data. 

Such a process can take a significant amount of manual work out of the claims process. For simple claims, it may even make straight-through processing possible, where the process is 100% automated. But the more likely scenario is automation will take some percentage of manual labor away, maybe 40% or more. That would qualify as the kind of “game-changing” increase in productivity that McKinsey says top insurers are achieving. 


Related content: 4 ways RPA and intelligent automation transform shared services centers



The Indico difference 

Such productivity gains are certainly in line with what we’ve seen with customers that implement the Indico Intelligent Process Automation platform. Reductions of 85% in process cycle times are common, leading to an 80% reduction in human resources. Game-changing numbers for sure. 

The Indico IPA platform is based on some 500 million labeled data points, enough to enable it to understand the context behind virtually any kind of content. Our tools make it a simple matter for customers to label maybe 200 of the actual documents involved in their process to build a claims automation model. And the folks who use the tools are the same ones who currently own your claims processes; those who know the processes best. No data science expertise is required. 

If you’re ready to experience the transformative power of IPA, or have any questions, just get in touch. You can also sign up to request a demo. It could be your first step in boosting profits and joining that top quintile of insurers.  

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