Impact of Agentic AI on Insurance Quotes

I adoption has increasingly been a topic of conversation among insurance professionals. While the primary focus has been on how companies can utilize AI in their offerings to better their product and simultaneously increase workplace efficiency, it is important to consider the shift that increased customer adoption of AI will bring about in the insurance marketplace. Although most industry professionals are familiar with generative AI, customer use of agentic AI is a much bigger transformation than most insurance companies might be prepared for.

Unlike generative AI, agentic AI works like an assistant assigned an overarching goal that breaks down tasks. These tasks may include browsing online shopping websites to find the best price for a pair of your favorite boots or acting as a customer service representative. Most importantly, agentic AI is able to learn as it completes tasks, course correct if it runs into issues, and tweak its process to make it increasingly easier to execute. It also incorporates memory capabilities, enabling it to redo repetitive tasks multiple times (Stryker 2026). These features mean that customers now have the power to use agentic AI to simplify multiple insurance tasks, like request online quotes, submit claims, and dispute claims.

It seems like it was yesterday when ChatGPT burst onto the scene and brought about concerns of an AI revolution and takeover. However, over the last few years, AI has evolved to mean ease of access, reliability, and convenience. Gen Z reports using AI to form brand opinions, understand pricing, and decide which products to but with their hard-earned money (Loktionova 2026). With AI adoption and reliance increasing, it is only a matter of time until people begin relying on AI to help pick their insurance plans and understand how to eke out the best price from quoting engines.

Gemini recently rolled out their “Auto Browse” feature, making it easier than ever to automate repetitive tasks (Tabriz 206). This feature, utilizing agentic AI, is able to carry out routine tasks by taking control of and opening multiple browser tabs to accomplish a specified goal. This feature is built-in and incorporated for free into everyone’s Chrome browsers, and as of June 2026, Google has rolled out this feature to all consumers (Reid 2026). While the free version of this feature has usage limits, Google includes the upgraded version of Gemini Pro with their yearly 5TB storage plan, which costs about $200 a year (Google 2026). With this ease of access, customers have already reported using Gemini Auto Browse to track order statuses, quickly and efficiently fill out forms, and do their online shopping for them (u/NecessaryBear98 2026). Agentic AI also has multiple uses for insurance customers, in both quote generation and claim filing.

It is now easier than ever for customers to be aware of how agentic AI can help them find the best prices. The easiest practical use case for agentic AI for insurance customers is using it to try to game online quote systems. To test my theory, I used agentic AI to understand whether I could find the best price for my boyfriend’s sports car using a popular online quote engine for a large auto insurer. As a control sample, I manually created a quote on the insurer’s website to understand my “true quote,” analyze all the different fields included, and identify all the ones I could technically change and get away with. For these test scenarios, I kept my changes very realistic. There were minor differences in the number of days I commute to work, miles of commute, and whether I was single or in a domestic union.

With my options finalized, I asked AI to build out every single test case scenario to input into the engine with various combinations of the fields I could change. In my testing, I also used two customers — one included all my information, and the other profile was built around my boyfriend. Ultimately, within two minutes, I had Gemini output about 1,900 iterations, out of which I asked it to pick 50 iterations that could lead to the lowest quote, half with my profile and half with my boyfriend’s. Within seconds, Gemini had generated a spreadsheet that I could use in my agentic AI testing (see Figure 1).

The next step was to navigate to the insurer’s website and fire up my built-in Chrome Auto Browse features. For this, I asked Gemini to give me instructions to build an agentic AI that could run these 50 iterations in groups of five runs and to provide me with a spreadsheet output of the premium value of each run. With Google’s built-in Workspace integration, for each run the AI would extract my spreadsheet data for that iteration, reopen the website URL, fill in the information to the matching fields, and include “human mimicry” to avoid bot sensors (see Figure 2).

With my instructions finalized, I asked Gemini to kickstart its loops of iterations. I left the process running overnight, and the next morning, I had 32 premium outputs ready. At some point, Gemini had run into scheduled portal maintenance and had asked me to manually restart at the end of the maintenance period, but since I was asleep, Gemini temporarily abandoned the task and gave me as many premium outputs as it could. Table 1 summarizes the true quote for me and my boyfriend and the lowest quote Gemini was able to obtain for each of us:

While I assumed that Gemini would most likely only be able to obtain at most two or three unique premium values, I was surprised to see a range of different values. With almost every iteration, Gemini was able to extract a small increase or decrease in my premium. While changing some variables did not show any variations in premium quotes, certain variables like previously having auto insurance and purpose of car use impacted the quotes in every iteration. Ultimately, my lowest premium was $716 in the ninth iteration. My boyfriend’s profile started out with a lower premium, but it also did not decrease as much as mine despite changing the same variables. However, this might have been impacted by the overnight scheduled maintenance that caused Gemini to stop running its iterations. The quote did increase or decrease by small amounts through each iteration, implying that Gemini was successfully able to tweak variables to my advantage (see Figure 3).

Note: These graphs are drawn from the 32 iterations Gemini was able to successfully complete before running into overnight scheduled maintenance.

The entire process took just one evening of dedicated time and research with no additional cost, except for my paid Google storage subscription. Surprisingly, the “human mimicry” request also seemed to have worked, because at no point did the insurer’s website terminate my session. While the quote might change and more accurately reflect pricing variables during the actual purchase process, using agentic AI gives customers unprecedented insight regarding variables and their influence on the cost of their policies. This enables bad actors to essentially game the pricing algorithm and exploit the rating variables to obtain the lowest possible rate with relative ease.

The use of agentic AI removes the barriers of time and effort that typically dissuades customers from shopping around for the best price and exposes insurers to a heightened risk of adverse selection. Customers could potentially manipulate their profiles in two ways: by outright fraudulent tweaking of annual mileage inputs or by optimizing their job descriptions to pick an ambiguous but technically truthful profession. Both scenarios create an information imbalance that current rating algorithms are not prepared to handle. Simultaneously, pricing actuaries will need to adapt to a market where even a 5% price increase over a competitor’s pricing could lead to a customer switching policies. To stay competitive, actuaries might need to start keeping a closer eye on competitor movements, keeping in mind thousands of bots running in the background to find the customer the lowest price for their insurance policies. Additionally, actuaries might also need to track how AI-assisted shopping affects premium trend over time, since that is a key input into rate indications. As customers rely on increased automation to drive down their premiums, actuaries might need to account for premium drift, which would produce a lower premium trend than would be historically expected.

With AI at the tip of our fingertips, customers could use AI to find policy loopholes that they could use in a court of law. Due to the increase in large language model (LLM) capabilities, cost efficiencies compared to human labor, and the ability to take on more cases, legal firms are also increasingly adopting AI in their practices. Compared to only 35% in 2025, about 69% of legal professionals reported using AI in their day-to-day work (LawPay 2026). There are also reports of lawyers uploading complicated policy forms into advanced LLMs to understand whether there a case can be built in the insured’s favor (Bracken II and Niczky 2026). Court proceedings have also started incorporating discussions regarding using AI in understanding policy language and specifications. In Snell v. United Specialty Insurance Co., although the final ruling was based on the insurance application, the judge discussed whether AI engines like ChatGPT and Gemini could be used to interpret the “ordinary meaning” of complicated policy jargon (Bruno 2026).

Additionally, there is now legal precedent to allow discovery of insurer systems to analyze whether AI was used to possibly unfairly deny claims, like in the case of the Estate of Gene B. Lokken v. UnitedHealth Group, Inc. (Bracken II and Niczky 2026). This could mean that while insureds can use AI to potentially identify if they can sue their insurer, the latter needs to be very careful about their use of AI in any part of the process. If an insured and their legal team can prove that a claim was denied without human review, it could expose the insurer to a host of bad-faith claims lawsuits. Additionally, with growing public outrage regarding the use of AI to approve or deny claims, insurance companies could face online backlash and, in a worst-case scenario, be embroiled in a public relations crisis.

Regardless of the verdict, a lawsuit means that insurers see longer claim settlement timelines, higher legal costs, and greater uncertainty about ultimate losses and severity metrics. With the rise in social inflation, we are seeing higher verdicts if the case goes against an insurer, as evidenced by courtroom severity more than doubling from 2020 to 2024 (Kansky and Manrique 2026). AI might further exacerbate this trend by enabling law firms to take on more cases at lower costs, making it easier for insureds to pursue litigation, and driving up the number of insurance lawsuits. To top it all off, third party litigation funding (TPLF) is also making it more enticing for insureds to go down the courtroom path rather than settling out of court. With investors looking for profit instead of trying to make the insured whole, the settlements coming out of these cases are in the tens of millions of dollars, if not hundreds of millions (Lynch 2025). With all these factors at play, reserving actuaries need to actively reassess trend picks, tail factor selections, and estimated case reserves to keep up with the impact of AI on reserving practices.

As agentic AI continues to develop and roll out, even the most refined models are susceptible to hallucinations and glitches. However, because it is easy to access and works quickly, agentic AI has the potential to be a go-to assistant for multiple customer insurance tasks that were previously considered time-consuming and tedious. Agentic AI in insurance works by gathering extremely personal information about its user, which might turn away some customers from ever adopting the technology. Regardless, with the pros heavily outweighing the cons and the low cost of the technology (at least initially), both pricing and reserving actuaries might have to learn to adapt to a world where their customers have more information, incentives to sue, and jury backing. Agentic AI is here to stay, and the sooner our community of actuaries realizes its potential and adapts, the better prepared we will be for its long-term ramifications.

Bracken II, Lawrence J., and Joseph T. Niczky. “Court Allows Discovery Into Insurer’s Use of AI to Deny Claims.” Hunton, 23 March 2026, https://www.hunton.com/hunton-insurance-recovery-blog/court-allows-discovery-into-insurers-use-of-ai-to-deny-claims. Accessed 15 June 2026.

Bruno, Tamara D., and Shareen Sarwar. “AI and Insurance Policy Interpretation After Snell v. United Specialty: What Policyholders Need to Know.” Policyholder Pulse, 25 February 2026, https://www.policyholderpulse.com/ai-insurance-policy-snell-united-specialty-policyholders-need-know/. Accessed 15 June 2026.

Google. “Google AI Plans.” Google One, https://one.google.com/intl/en/about/google-ai-plans/. Accessed 15 June 2026.

Kansky, Jessica, and Juliana Manrique. “From Headlines to the Jury Box: Assessing the Impact of Nuclear Verdicts on Litigation.” US Law, 2025, pp. 8-9. US Law, https://www.uslaw.org/wp-content/uploads/2025/09/From-Headlines-to-the-Jury-Box-Assessing-the-Impact-of-Nuclear-Verdicts-on-Litigation_Verdict-Insight-Partners_Fall2025-USLAW-Magazine.pdf. Accessed 15 June 2026.

LawPay. “AI for Law Firms: What the 8am Legal Industry Report Tells Us About AI Use.” American Bar Association, 1 April 2026, https://www.americanbar.org/groups/law_practice/resources/law-practice-magazine/2026/march-april-2026/8am-legal-industry-report/. Accessed 15 June 2026.

Loktionova, Margarita. “How AI Tools Influence the Modern Buyer Journey: A Survey of 1,000+ US Consumers.” Semrush, 5 March 2026, https://www.semrush.com/blog/ai-tools-the-modern-buyer-journey-study/. Accessed 12 May 2026.

Lynch, Jim. “Financing Justice: The Rise and Risks of TPLF.” Actuarial Review, 16 July 2025, https://ar.casact.org/financing-justice-the-rise-and-risks-of-tplf/. Accessed 16 June 2026.

Reid, Elizabeth. “Search’s I/O 2026 updates: AI agents and more.” Google Blog, 19 May 2026, https://blog.google/products-and-platforms/products/search/search-io-2026/#agentic-coding. Accessed 15 June 2026.

Stryker, Cole. “What is Agentic AI?” IBM, June 2026, https://www.ibm.com/think/topics/agentic-ai. Accessed 15 June 2026.

Tabriz, Parisa. “The New Era of Browsing: Putting Gemini to Work in Chrome.” Google Blog, 2026, https://blog.google/products-and-platforms/products/chrome/gemini-3-auto-browse/. Accessed 12 May 2026.

u/NecessaryBear98. “I Tried Gemini Chrome Auto Browse And Chrome Got Scary Useful.” Reddit, May 2026, https://www.reddit.com/r/AISEOInsider/comments/1tfzjjh/i_tried_gemini_chrome_auto_browse_and_chrome_got/. Accessed 15 June 2026.