
How Non-Textual Gen-AI Improves Insurance Claim Decisions
Liberate Claims Teams from Tedious Work with Non-Textual Gen-AI
From locating medical data to complying with complex legal issues, tedious manual tasks cause headaches for claims teams. They aggravate inefficiencies, increase overpayments, and distract frontline staff from supporting customers with genuine claims.
That’s why generative AI is valuable for processing claims. It alleviates the burden of time-consuming work by summarizing insights to help claims teams save time.
And with non-textual gen-AI, claims teams don't just have text-based chatbots at their disposal. They also have access to instantly generated graphs, images, and more to analyze claims data.
In this article, we'll outline what non-textual gen-AI is and how it helps claims teams take back manual processes so they can do their best work.
Generative AI for Understanding Claims
Researching injuries, analyzing return-to-work plans, and looking for medical or legal documents, costs insurance carriers hundreds of dollars per claim.
That’s because finding paperwork, classifying data, and contextualizing claims information drains resources. Claims teams become overwhelmed, customers feel underserved, and carriers struggle to scale.
But eliminating manual work with AI isn't the solution. Replacing workers with AI compromises compliance, increases litigation risk, and undermines customer satisfaction.
Instead, AI should support claims workers. It's about removing tedious manual processes to emphasize higher-value ones.
Benefits of AI for claims processing include better decision-making so claims adjusters can use their creativity and think more intuitively. And carriers can better identify fraud for insurance and reduce claims costs by 20–30% with generative AI.
Textual vs. Non-Textual Gen-AI for Insurance
Generative AI helps by generating insights from claims documents so claims teams can work more efficiently.
It can take many forms, such as text-based responses that answer user queries like what injury a claimant has or if they've submitted a document.
But most queries are more complex. Text-based responses usually aren’t sufficient, and reading long paragraphs when AI has to explain something in detail isn’t optimal.
That’s where non-textual gen-AI comes in. It presents data in non-textual formats, like charts, graphs, and visuals.
The AI is smart enough to know when to provide a non-textual response. It's also trained for insurance claims to understand context, empowering claims teams to optimize their time.
At Owl.co, we’ve developed OwlAssist as a generative-AI tool that utilizes textual & non-textual responses and document generation for insurance to unravel complex details and generate helpful resources for claims, fraud, and legal teams.
But how does it work?
What Non-Textual Gen-AI Is and How it Works for Insurance Claims
What kinds of use cases from non-textual gen-AI are there for claims teams?
Say you’re investigating a short-term disability claim and need to review the status of the claimant’s medical appointments.
But information is often scattered across documents about doctor visits or physiotherapy appointments. Even after locating documents, taking in the relevant information in context consumes extra time and energy.
Non-textual gen-AI in OwlAssist creates an appointment chart with details and documentation links.
Now claims teams can spend more time on higher-value tasks instead of scouring through claims documents and trying to understand complex data.
Non-textual gen-AI in OwlAssist instantly creates charts and images like this list of a claimant’s medical appointments.
Examples of Non-Textual Generative AI for Insurance Claims
What about other examples of non-textual generative AI for claims teams?
With non-textual gen-AI, claims teams can instantly create:
- A claimant’s recovery timeline to compare the progress to both the assigned recovery plan and disability-duration guidelines to determine benefit continuation;
- Graphs of earnings potential within a suitable occupation based on workplace assessments for return-to-work studies;
- Body-part restriction diagrams showing injury details and vital-sign data like heartrates;
- Charts summarizing pertinent details in demand packages from a claimant’s counsel during settlement disagreements;
- And much more!
How Non-Textual Gen-AI Helps Make Insurance More Human
Non-textual gen-AI empowers staff to focus their creativity on improving claims outcomes.
With instantly generated charts and visuals to study claims data, carriers can automate tedious manual processes for claims adjusters and fraud & legal teams, boosting customer satisfaction and reducing operational costs.
Owl.co’s Claims Intelligence ecosystem features non-textual gen-AI so claims teams can expand their capacity for ingenuity and make insurance more human. Book a demo today to begin transforming your organization.
