Solutions & Use Cases

How Generative AI Helps Insurers Automate More Processes

Kognitos

Key Takeaways

Generative AI for insurance automation addresses a paradox this post highlights: insurers overwhelmingly recognize the need for digital innovation, yet nearly all struggle to implement it, and only a fraction of even back-office work is automated. The reason is that traditional RPA depends on standardized processes, while insurance data varies from agent to agent and source to source, and high document volume and variability crash automations or demand constant maintenance, undermining ROI, especially for rule-heavy claims. The post argues that Kognitos combined with large language models opens automation to these variable processes: claims processors can teach the system in plain English, such as where a patient ID sits on a specific vendor’s form, and exceptions are resolved conversationally. Because every step is recorded in English, anyone can audit what happened and set approval checkpoints. The result: lower implementation and maintenance costs and stronger ROI on generative AI automation.

Statistics show:

80% of insurers realize the need for digital capabilities.
99.6% of those found it difficult to implement digital innovation
There is potential to automate nearly 50-60% of the back-office processes by 2025, resulting in 66% time savings for insurers.

Insurers are Deploying RPA but Yet Still Limited in What They Can Automate

The statistics above seem to paint a conflicting tale. On the one hand, it is clear insurers recognize the potential power of automation. Insurers can use automation to de-risk their business and position the company to compete both with other legacy players and insurtech startups. It is projected that by 2025, 25% of the insurance industry’s operations and activities will be automated. The growing implementation of automation in the industry gives insurers a solution to reduce cost, collect more accurate data on the insured, and gather feedback to deploy new, more personalized products. The use of traditional RPA tools in this industry has also reduced the number of human errors incurred and improved customer service. RPA succeeded in automating many of the highly standardized processes, and yet 99% still express a challenge in implementing digital innovation and only a fraction of even back office processes have been automated today. Why this disconnect if it’s such a focus?

The challenge lies in the lack of standardized processes and pain of implementing RPA. Particularly in insurance, data required for many processes may vary from agent to agent, or source to source. High volumes of documentation + variability in those documents can crash automations or require continuous maintenance. In processes like claims, arguably the biggest opportunity for automation for insurers, the vast number of rules and logic required makes the initial implementation of RPA often a burden, and requires frequent meetings between the subject matter experts (claims processors) and the people implementing automation (RPA developers) to detail and communicate all of the different rules. In most cases, the ROI is not sufficient with this approach. Instead, an automation solution is needed that can learn logic, handle variability and be easier to deploy.

Generative AI Automation: Flexible and Easy to Modify

The average business user is not technology savvy, nor do they come with deep domain experience with various coding languages or training on RPA. These skills are in high demand, specifically in the insurance industry, therefore there is a need to be met in this marketplace. Imagine if there was a more user friendly version of these game changing RPA tools that enables the average user to build and manage automations? What if the automation tool itself could learn how to handle each specific document, and problem solve exceptions without a lot of up front work?

Here Kognitos, combined with large language models like GPT3 steps in to open up the power of automation to less standardized, highly variable processes. Claims processors can now teach automation products how to get the desired information from a claim with simple statements like “For this vendor, the patient ID is always under the group number.” Logic can be taught to automation in English, like “If the claim address does not match the account address, send an email to the insured for clarification.” Using Generative AI, all exceptions are handled in a back and forth, conversational manner that any user can understand. All steps of the automation are in English so anyone can audit exactly what occurred, and set approval steps as needed.

Using GPT3 to Teach Rules to Claims Automation

Kognitos is Generative AI for Automation. With Kognitos, implementation costs are reduced, maintenance costs are all but eliminated and many processes now have strong ROI potential with automation. Additionally, the business users are able to interact with automation in a way that requires no training, and is easy to understand. Now insurers can develop a competitive edge and cater to customers without the frustrations previously experienced when rolling out digital innovation.

How Generative AI Helps Insurance Companies Automate Operations

Map the insurance operations workflows where generative AI delivers the most value. Submissions triage, claims document processing, policy endorsement processing, customer inquiry routing, and regulatory filing preparation are insurance operations with the highest generative AI value.
Deploy AI for claims document extraction across all claims types. Claims arrive with varied document packages: ACORD forms, incident reports, medical records, repair estimates, and photographs. Generative AI extracts the relevant data from any claims document type without per-document templates. Test extraction accuracy on a sample of each claims type before enabling automated processing.
Configure generative AI for submissions triage and underwriting data population. Submissions triage is a high-volume, time-sensitive process. Configure AI to classify submissions by line of business, extract key underwriting data, and populate the underwriting workbench automatically. Faster submissions triage reduces broker response time and improves hit rate.
Automate policy endorsement processing for standard endorsement types. Standard policy endorsements (address changes, additional insureds, coverage modifications) are rule-defined and high-volume. Configure AI to process standard endorsements automatically and route exceptions to underwriting staff.
Measure submissions response time, claims straight-through processing rate, and endorsement cycle time. These three metrics measure generative AI impact across the primary insurance operations use cases. Track all three before and after deployment.

Frequently Asked Questions

Generative AI for automation is a technology that combines large language models with automation platforms to enable insurers to build and manage workflows using plain English instructions instead of complex code. Unlike traditional robotic process automation (RPA), it can understand natural language rules and handle variability in documents and processes. Platforms like Kognitos use Generative AI to allow claims processors and other business users to teach automation logic conversationally. This approach makes it possible to automate a far wider range of insurance processes than RPA alone can handle.

Generative AI automation learns document-specific rules through conversational instructions, so it can adapt to different formats and sources without breaking. For example, a claims processor can tell the system something like 'For this vendor, the patient ID is always under the group number,' and the automation learns that rule. All exceptions are handled through a back-and-forth dialogue that any user can understand, rather than requiring a developer to recode the workflow. This means high volumes of variable documents no longer crash automations or require constant maintenance.

The primary benefits include reduced implementation costs, near-elimination of maintenance costs, and strong ROI potential for a much wider range of processes. Business users can interact with and manage automations without technical training, which frees up scarce developer resources. Insurers gain the ability to automate complex, logic-heavy processes like claims that were previously too costly with RPA. Additionally, all automation steps are written in plain English, making it easy to audit what occurred and add approval steps as needed.

Traditional RPA works well for highly standardized processes, but insurance data often varies significantly from agent to agent and source to source. High volumes of documentation combined with variability in those documents frequently cause RPA automations to crash or require ongoing maintenance. In complex processes like claims, the sheer number of rules and logic required makes initial RPA implementation a heavy burden, demanding frequent collaboration between claims processors and RPA developers. In most cases, the ROI from this approach is not sufficient, which explains why 99% of insurers still report challenges with digital innovation despite recognizing its potential.

A claims processor can instruct a Generative AI automation platform in plain English to handle specific document rules, such as 'If the claim address does not match the account address, send an email to the insured for clarification.' The system can also be taught vendor-specific logic like where to find a patient ID within a particular document layout. All of these instructions are given conversationally and stored as auditable English-language steps within the automation. This allows claims teams to build and modify automations themselves without needing to involve RPA developers for every rule change.

Insurers should look for a platform that allows business users, not just developers, to build and modify automations using natural language. It is important to assess whether the solution can handle document variability and exception management without requiring frequent developer intervention. Evaluating total cost of ownership, including implementation and ongoing maintenance, is critical, since traditional RPA often has a poor ROI for complex insurance processes. Organizations should also confirm the platform supports auditability, with all automation steps visible in plain English, and allows for approval workflows to meet compliance requirements.