Viewpoint: How Generative AI Enables a Brighter Claims Future in 2024 and Beyond

As generative AI transitions from an emerging innovation to a tried-and-true tool, this multi-use technology has given way to key use cases for the risk and insurance space. According to a recent study, by the end of 2023, half of insurers will have tested generative AI solutions, with more than 25% planning to have solutions in production by year-end.

For claims administration, a function characterized by heavy workloads and repetition, generative AI can reduce administrative costs, streamline workflows, and reduce reliance on manual processes. Ultimately, leveraging generative AI for streamlined claims handling will enable adjusters to spend more time engaging with claimants and injured workers and produce more efficient patient outcomes.

While still early in inception, insurers, third-party administrators, and medical management firms are leveraging generative AI for key claims use cases internally, which also helps lower the total cost of risk and add stronger return to work metrics across the patient landscape. Let’s look at which claims tasks generative AI is currently being used for and what remains on the horizon.

Workflow Automation

Jeff Gurtcheff

Claims professionals spend countless hours reviewing medical documents, piecing together insights, and documenting findings. Amid looming talent gaps and worker shortages, easing workloads is a top-of-mind challenge for insurance industry leaders and a clear starting point for generative AI integration. According to EY, more than 50% of insurers surveyed from June 2022–June 2023 held improving claims efficiency as a priority area for near-term investment.

Using natural language processing, generative AI models can summarize medical documents, extract key information, and bring hidden insights to the attention of claims adjusters. Automated document review and summarization saves time for adjusters, allowing them to focus on value-driven tasks and increase their direct engagement with employers, injured workers, and claimants. More hands-on time with claimants translates into lower total cost of risk, better return to work metrics, and enhanced claims outcomes overall.

Early adopters of generative AI for claims processing are automating simple medical-only claims, clear liability property damage-only claims, and some first-party collision, comprehensive, and property losses.

Generative AI-powered document review also streamlines the claims review process, reducing costs associated with administrative tasks and decreasing average handling time. In addition, generative AI tools can leverage predictive decision-making to learn from past claims and identify new claims that match similar patterns. With this capability, claims that meet certain criteria can be processed automatically.

Risk Assessment

For carriers and third-party administrators, having a deep understanding of risk factors is paramount to underwriting, developing accurately priced insurance policies, and mitigating losses due to overpayment.

For healthcare and medical risk assessment, understanding risk factors enables stakeholders involved in the payment of care to better anticipate and respond to the needs of care recipients.

Currently, stakeholders are pairing generative AI models with predictive analytics to develop claim and clinical risk scores, enable litigation avoidance, and conduct severity modeling. To address demands for more customized, consumer-friendly offerings, insurers are employing generative AI models for dynamic policy pricing that reflects insureds’ unique needs and risk profiles.

Generative AI risk management tools are making a particular impact on the workers’ compensation space. Using predictive analytics, nurses and adjusters can analyze return-to-work estimates, physical demand requirements, and pain levels found within a worker’s compensation claim to conduct a more accurate evaluation of claims risk and identify potential barriers to recovery for injured workers.

Fraud Detection

According to the latest reports, U.S. insurance fraud costs an estimated $308.6 billion every year, and fraudulent workers’ compensation claims amass $32 billion in losses annually. To reduce material losses and fraud risk, insurers and third-party administrators are leveraging generative AI for enhanced fraud prevention and detection.

Leveraging historical claims data, generative AI tools can highlight anomalies for escalation and identify inconsistencies based on established patterns. For example, generative AI models would kick off the investigation process for workers’ compensation claims with limited improvement, inconsistent injury complaints, and excessive use of medical resources by first flagging the claim to adjusters for further review. If it is determined that the claim requires further escalation, adjusters can work with healthcare providers to develop a plan for action and intervention. In addition, generative AI can analyze prior events, incidents, and pre-existing conditions to highlight inconsistent data introduced into the claim.

What’s Next?

As workplaces evolve, a crucial aspect of integrating generative AI into claims processing will be maintaining standards for the ethical and responsible use of AI in insurance. Employers will be called to have open conversations about generative AI best practices, as well as generative AI’s limitations.

Ultimately, cultivating a “high-tech, high touch” culture that upholds AI as a tool to enhance worker capabilities and overcome knowledge gaps while still underscoring the value of human insight will be critical for leveraging generative AI’s full potential for claims processing. In the coming years, insurance leaders will continue to explore new use cases for generative AI in claims, including advanced predictive insights, adjuster productivity tracking, and fraud recovery analysis. At the core of advanced generative AI capabilities for claims management is the enhanced ability to connect injured workers quickly and efficiently with critical care, develop customized treatment plans, and enable whole-person recovery.

Gurtcheff is chief claims officer of CorVel. He joined CorVel in 2019 as vice president of national accounts and strategic insights, providing executive strategic consulting by identifying areas of opportunity based on data-driven analytics. He has more than 30 years of experience in the industry. His background spans the third-party administrator space, independent insurance and the carrier market.