3 ways gen AI can modernize and improve the claims process

Bogged down by manual interventions that can cause delays and errors, the claims process is ripe for improvement. Fortunately, many insurance companies are moving quickly to adopt generative artificial intelligence (gen AI) to enrich and accelerate the process and improve the customer journey.

Gen AI is the umbrella term for the subset of large language models that use data patterns to create new content. It can produce text, images, audio, and more. In the insurance industry, the technology is particularly useful for searching, generating, classifying, clustering, and summarizing information as well as extracting valuable data and insights.

Here are three ways gen AI can modernize and improve the claims process:

In today's environment of high customer expectations and surging costs, gen AI is perhaps most valuable as a tool for improving the insurance customer experience.

During FNOL intake, gen AI can improve the assessment of policy coverage and search internal and customer claim data to identify missing information, summarize key points, and highlight areas in need of clarification.

It’s already revolutionizing the contents pricing process. It can scan inventory lists, search online for replacements, and suggest the best and most cost-effective items for fulfillment. This saves countless hours for the claims adjuster while providing a seamless experience for the customer and faster, more accurate settlements.

Finally, gen AI is a valuable tool for enhancing communication. Gen AI-powered virtual assistants can help customer service agents provide real-time support and claims status updates for claims queries.

When adjusters face a large volume of claims documentation, such as medical or legal records, gen AI can digest the contents for them. In this scenario, Gen AI operates with speed and precision to provide the adjuster with summaries of key concepts and facts. Rather than slogging through a mountain of complex data and potentially missing details or making an error, insurers can use the gen AI takeaways to inform their next steps.

A wider insurance use case is to design AI-driven risk prediction models based on historical risk data. These machine learning models can produce predictions based on hundreds of records in seconds. This means underwriters can respond more quickly, so they win and retain more business and can refocus their efforts on higher-value work. Gen AI offers similar potential for drastically reducing turnaround time.

Gen AI not only enables insurers to settle claims faster but can also combat fraudulent claims by analyzing huge troves of data to detect fraudulent behavior patterns. At a time when insurers are losing billions each year due to unprecedented levels of fraud, this will be vital to maintain a competitive edge.

Looking at gen AI across today's insurance landscape, what's notable is not just how quickly the technology is advancing but how fast it's being adopted.

Considering the successes of early adopters across industries, we have found that the most dangerous thing about gen AI is assuming that it only delivers productivity. To get the greatest value, insurers should focus on outcomes, make gen AI a part of their technology stack and not a point solution, and build data foundations by establishing high-quality-data acquisition systems and standardizing data-quality practices.

Finally, experiment continuously and do not fear failure. Learning from your results will help you prioritize ruthlessly and accelerate ROI.

This article first appeared in Digital Insurance. It was authored by Jeff Saye, global leader of insurance claims at Genpact.