Bridging the Gap: Collaborating with AI-Driven Carriers in Professional Liability

Artificial intelligence (AI) is no longer simply a futuristic concept in the insurance industry; it has become an operational reality that directly impacts carriers, brokers, and insureds alike. Across the professional liability marketplace, insurance carriers are increasingly leveraging AI to streamline underwriting, enhance risk assessments, and drive operational efficiency. While no one can fault carriers for adopting technology that affords them more speed, consistency, and data-driven insights, reliance on AI in decision-making is reshaping how brokers engage with underwriters and can advocate for their insureds. Understanding how to navigate AI-led coverage decisions is critical for brokers who strive to guide their clients, ensure fair placement, and reduce coverage gaps. Professional liability brokers must also consider how to protect their own professional credibility in the ever-changing landscape. As AI takes on a bigger role in professional liability insurance, brokers are navigating new challenges and new ways to support their clients. In this post, we take a closer look at how AI-driven underwriting is changing the way risk is assessed, priced, and communicated between brokers and carriers.

The Rise of AI in Professional Liability Insurance

When we first think of how insurance carriers are implementing AI, we likely anticipate its uses in slashing turnaround time for deliverables. For professional liability carriers, this is certainly true—AI tools already generate quotes, automate document processing, and cut down on response times—but these applications are just the tip of the iceberg. Machine learning models are also being used to analyze vast amounts of data to help assess risk, determine coverage eligibility, and set pricing. Some carriers are also applying AI to initial claims handling, where algorithms can assess severity, flag anomalies, or assign claims professionals. As claims develop, AI tools also identify patterns in litigation trends and industry benchmarks both at-scale and at the carrier level, informing claims-handling decisions. Medical malpractice carriers are also using provider databases complete with practice profile characteristics (procedure counts, prescribing habits, hospital and group affiliations) to selectively target their preferred risks. Armed with this data, carriers will actively seek out prospective insureds who match their appetite and for whom they anticipate their pricing would be competitive or policy features beneficial.

What Brokers Risk Losing in an AI-Led Process

These advancements and the shift toward technology in an industry so accustomed to the human touch can turn traditional interactions between brokers and underwriters on their heads. AI has accelerated decision-making, so brokers may even soon find themselves interacting as much with algorithms as they have with underwriters. This will be particularly true during the early stages of underwriting where processes like application review for completeness are automated more readily. While this increased efficiency undoubtedly benefits the market, it does introduce a new dynamic.

Some carriers may push underwriters to prioritize data submissions over relationship-based underwriting conversations, reducing or eliminating any influence brokers may have on risk placement. Brokers may receive AI-generated outcomes without accompanying explanations, complicating their ability to advocate for clients or communicate the reasoning behind the outcome to the clients themselves. Automated decisions may reduce opportunities for brokers to present unique risk factors or contest unfavorable outcomes. The chance to tell your clients’ stories and why they’re a good fit for the carrier may no longer be a given.

Strategies for Brokers Navigating AI in Underwriting

Navigating these shifts requires brokers to lean on their adaptability and strategic communication skills, while demanding they build a deeper understanding of AI’s role in carrier operations. Helping your insureds understand how AI may influence coverage availability, pricing, and decision timelines means setting realistic expectations early, even if the answer is telling your clients you’ll get back to them as you get more familiar with individual carrier processes and idiosyncrasies.

However, you can’t always expect a carrier to be forthcoming about what exactly makes their system unique and viable. AI models often operate as “black boxes”; carriers are naturally unwilling to share proprietary methodologies and data, making it difficult for brokers and insureds to understand how specific underwriting decisions are reached. AI models are only as reliable as the data they are trained on. Incomplete, outdated, or biased data can lead to flawed underwriting decisions. Some of the data used to determine risk, like the number of fast-food restaurants within a certain distance of a physician’s practice location, may surprise you. You’ll want to be able to explain these data points and their influence should they come up in conversation with your clients.

While brokers can and should encourage carriers to provide clear explanations of AI-driven coverage decisions, especially in complex or high-value placements, transparency is a two-way street. Ensuring that carriers have all the information they need to understand the risk being presented is essential. Reducing coverage gaps for insureds and maintaining professional credibility are key. Pay special attention to elements of your client’s story that you know likely aren’t reflected by current versions of the data carriers have in-house, such as fee-for-service procedures and practice locations to be included or excluded. Newer carrier entrants to the marketplace are untested and have yet to prove their commitment to handling claims related to exposures that may not have been understood when initially writing the account, so a prescription for extra caution is called for. Brokers may also face E&O liability if they fail to challenge AI-generated outputs that disadvantage clients by creating gaps in coverage, for example, particularly if human review was warranted.

Push for Human Review When It Matters Most

Knowing how and where AI is used in underwriting and pricing decisions and understanding when human review can and should be requested is crucial. For example, unexpected pricing swings with limited explanation, declinations based on outdated or incomplete data, AI risk scores that conflict with known improvements in client operations, or coverage terms that appear inconsistent with the client’s risk profile are all circumstances worthy of pushing back for a manual review. As AI influences more and more processes, maintaining already-established rapport with underwriters and carrier teams will be critical for nuanced negotiations.

The rise of AI is reshaping more than underwriting. It’s redefining how risk is measured, priced, and managed across the industry. Underwriters, claims professionals, and risk managers must collaborate with brokers to ensure AI delivers efficiency without compromising fairness, accuracy, or ethical standards. As guidelines for AI use are refined, professional liability practitioners must remain agile, continuously updating their knowledge and processes to align with evolving standards.

AI is transforming how professional liability carriers operate, but human expertise, judgment, and advocacy remain indispensable. For brokers, successfully collaborating with AI-driven carriers means embracing technology while continuing to safeguard client interests. By asking the right questions, staying informed, and fostering strong relationships, brokers can bridge the gap between algorithmic decision-making and the personalized service clients expect. In doing so, brokers reinforce their value as trusted advisors in an increasingly digital marketplace.

Meet the Author

Faith Karson

Custom Med Mal Insurance Creator, L&J Insurance Services, Inc.

Faith brings 4 years’ experience in the professional liability insurance industry to the team at L&J Insurance Services, Inc. L&J is a boutique independent insurance brokerage that has specialized in hard-to-place medical malpractice insurance for individual providers and small groups for over 40 years. Faith thrives on the challenge of finding tailored coverage solutions that protect L&J’s clients against potential exposure. She takes pride in working closely with healthcare providers throughout their careers, offering personalized support, and helping them navigate career transitions while ensuring their evolving insurance needs are continuously met. She values the trust her clients place in her and works diligently to earn it by providing clear, honest advice and responsive service. Committed to continuous learning, Faith stays current with industry trends and enjoys the dynamic nature of her role. Faith has a BA from NYU, is a Six Sigma Green Belt, and earned the Certificate in Medical Professional Liability from PLUS.