Agentic AI Use Cases in the Insurance Industry

Agentic AI, defined as artificial intelligence (AI) systems that are capable of autonomous decision-making and then taking proactive actions, holds the promise of transforming numerous strategic and operational aspects of the insurance industry. 

Agentic AI in insurance is in its infancy, and its promise across the industry has barely been tapped. When fully implemented, Agentic AI can bring carriers expanded revenue opportunities, reduced expenses, improved productivity, and enhanced customer engagement and satisfaction across the CX lifecycle.

In this blog post, we will look at just some of the myriad possibilities for using Agentic AI in insurance across multiple functional areas. We’ll explore various agentic AI use cases in the insurance industry, and the business benefits which can accrue from addressing those use cases. 

What is Agentic AI in Insurance? 

AI as a whole continues to rapidly evolve and revolutionize the insurance industry. There is perhaps no other technology that has so deeply and quickly changed the face of the insurance vertical to date. Its proven capabilities and measurable gains can be astonishing, and yet there is a sense that we are merely at the beginning of its transformative possibilities.

As a subset of the AI revolution, agentic AI is even earlier in its adoption. Gartner, for example, reports that agentic AI accounted for less than 1% of all enterprise software applications in 2024. But Gartner predicts that by 2028, 33% of enterprise software applications will include agentic AI, and that agentic AI will thus enable 15% of day-to-day work decisions to be made autonomously.

Like almost any technology, especially one in its early stages, there are many definitions of just what agentic AI is and what it does. But all definitions typically contain these key elements: 

• Agentic AI incorporates advanced artificial intelligence systems capable of autonomous decision-making and action

• They are aimed at achieving specific goals with minimal human intervention

• Unlike traditional AI, which typically responds to direct human-initiated prompts, agentic AI can gather inputs from its environment to then plan, adapt, and execute tasks independently, often coordinating with other software agents or systems

• Of particular concern to a highly regulated industry such as insurance, these agents are designed to operate within defined boundaries, ensuring human oversight and ethical considerations remain integral to their function. 

Agentic AI is gaining traction in multiple industries and across multiple business functions. It has already shown promise in such application areas as customer service and support, risk assessment and management, patient monitoring and scheduling in healthcare, cybersecurity, supply chain and inventory management, people management, and even in software development. 

How Agentic AI Works 

Agentic AI is conceptually simple, and involves these key steps in the process: 

• Perception: The AI collects data from its environment using sensors, APIs, or other inputs.

• Reasoning and Planning: The system processes the data to determine the best course of action. It evaluates possible solutions and plans steps to achieve the desired outcome(s).

• Decision-Making: Using algorithms and past experiences, the AI selects the most appropriate course of action. Reinforcement learning helps it improve decisions over time, by learning from successes and failures.

• Execution: The AI carries out the chosen action, such as sending a response, initiating a process, or controlling a device.

• Feedback and Learning: After executing the task, the AI assesses the results. It uses this feedback to refine future actions, enhancing its performance over time. 

The key to the performance of agentic AI models is that this process is recursive (it repeats as many times and as often as needed), enabling agentic AI to function effectively in dynamic environments, continuously improving its capabilities, and yielding better and better results with time. 

Agentic AI Benefits Insurers and Consumers 

With their ability to autonomously make decisions, adapt to new information, and execute tasks with minimal human intervention, agentic AI systems can accelerate the digital transformations of insurers.

This transformation offers insurers numerous benefits across multiple operational areas: 

• More Accurate Risk Assessment and Underwriting 

  Traditional underwriting has relied on manual analysis of historical data, which can lead to outdated or inaccurate risk assessments and long lead times in connecting with prospects and customers, and delays in new product development and introduction.  

  AI has improved this situation immensely. Agentic AI can go further by integrating ever more diverse data sources — such as telematics, social media activity, behavioral insights, and open banking data — to further improve dynamic risk assessment in real-time.  

  This capability also benefits consumers, as it allows insurers to craft personalized policies that are more accurately and attractively crafted, and to offer options in near real time, at the point of decision-making. 

• More Personalized and Competitive Pricing 

  Even before agentic AI, among the benefits of improved risk assessment and underwriting is the ability for carriers to develop and implement more accurate and competitive pricing, and to deploy new pricing models more quickly than with their antiquated and manually-driven processes. With the boost provided by agentic AI, these benefits to insurers and customers are further enhanced. 

• Accelerated Claims Processing 

  Agentic AI streamlines the claims process by automating tasks such as document verification, damage assessment, and claim approval. Insurers reap the benefits through reduced expenses and lower rates of manual intervention, while consumers see their claims paid more promptly and with fewer interactions with insurer staff. 

• Proactive Fraud Detection 

  Insurance fraud is a persistent and costly issue for carriers around the world. Agentic AI enhances fraud detection and prevention by analyzing patterns and anomalies in large datasets to identify potential fraud more quickly. This proactive approach, vs. reacting to fraudulent activity after the fact, enables insurers to flag suspicious claims early, and even prevent fraud at the application stage, minimizing financial losses and enhancing compliance with regulatory standards.

  Consumers benefit through reduced premium escalation, as fraud drives up the cost of policies and can hit them through issues such as identity theft and online impersonation. 

• Improved Customer Engagement and Loyalty 

  Agentic AI enhances customer experiences through personalized interactions and 24/7 support. AI-powered digital assistants can guide customers through policy selection, provide real-time updates on claims, and offer tailored recommendations based on individual circumstances. This level of personalization fosters deeper customer relationships and increases satisfaction. 

• Operational Cost Reductions 

  By automating routine tasks such as data entry and claims processing, agentic AI reduces operational costs and minimizes human errors. This efficiency allows human agents to focus on more complex value-adding tasks, improving overall productivity. In turn, when insurers are able to hold costs down and keep inflation to a minimum, customers can expect more competitive pricing and additional policy options. 

• Competitive Advantage 

  Adding all these effects up, insurers adopting agentic AI can respond more swiftly to market changes and customer needs. They can offer faster, more accurate services, personalized products, and better pricing. This technological advancement results in a competitive edge, and early adopters will be able to position themselves as industry leaders. 

Agentic AI Applications in Insurance 

Agentic AI use cases in the insurance industry today are many and varied. Ultimately, they are limited only by the imaginations of their implementors and by the focus of the insurer, in terms of the business problems they’re trying to solve.

Here is just a sampling of insurance use cases for agentic AI, organized by carrier business function. (One other benefit of agentic AI is that it can facilitate cross-function cooperation and efficiency, but this can serve as a template for use cases per current insurer organizations.) 

• Underwriting: 

  • Automated Risk Assessment: Agentic AI can autonomously gather and analyze data (e.g., property images, geographic data, building permits) to assess risk and suggest or make underwriting decisions without human intervention. 

  • Dynamic Pricing: AI agents can be used to continuously adjust premiums in near real-time, based on new data sources such as inputs from IoT devices or climatic or weather changes. 

  • Portfolio Optimization: With the help of these software agents, carriers can find new ways to rebalance their risk portfolios on an ongoing basis, by identifying underperforming or overly exposed segments. 

• Claims Processing: 

  • FNOL (First Notice of Loss) Automation: Agentic AI can automatically trigger and handle claim intake functions as they are posted from various sources such as IoT devices, telematics, or mobile apps. 

  • Autonomous Claims Handling: From investigation to settlement, agentic AI can verify coverage, assess damage (e.g., from uploaded images or drone footage), and settle straightforward claims without human intervention. Those that require further investigation are then flagged for human adjusters, but are already partially completed. 

  • Fraud Detection & Prevention: AI agents can detect and block suspicious claims patterns in real time and launch deeper investigations autonomously and/or alert human claims adjusters that action is needed on their part. 

• Customer Service & Engagement:

  • Proactive Customer Communication: Carriers’ AI agents can engage policyholders proactively about renewals, risks (e.g., upcoming storms), or gaps in coverage. 

  • Virtual Agents/Concierges: These agentic AI functions can handle complex customer interactions across multiple communications channels — email, phone, chat — with goal-oriented behavior (e.g., resolving complaints or upselling). This can be particularly effective when wedded with the latest generation of customer interaction systems, which are also increasingly migrating from “standard” AI to agentic implementations. 

  • Personalized Policy Recommendations: Agent software applications can suggest policy adjustments or new products based on changes in the customer's life situation (marriage, e.g.) or changes in property (a new home or car, a total loss incurred in either, etc.). 
    

• Risk Engineering & Loss Prevention: 

  • Internet of Things (IoT) Monitoring & Alerts: Using sensors and connected devices (e.g., in homes, vehicles) to detect potential hazards and trigger mitigation actions (e.g., shutting off water valves) are perfect opportunities for employing agentic AI. 

  • “Digital Twins” for Property Analysis: An insurer can use agentic AI to simulate risks (e.g., flood, fire, earthquake) and recommend structural changes or adjustments in coverage based on simulations. 

  • Autonomous Inspections: AI agents can autonomously deploy drones or ground robots to inspect properties for underwriting or post-claim evaluation. 
     

• Product and Market Development: 

  • Market Gap Identification: Agentic AI solutions can autonomously analyze data from competitors, social media, and policyholder behavior to identify unmet needs and propose the development of new products, services, and pricing much faster than is typical of today’s methods. 

  • Micro-Product Development: In the quest to serve markets as small as individual consumers or small groups, agentic AI can assist in the design and launch of niche or usage-based insurance products “on the fly”, based on market signals or emerging risks (e.g., cyber threats for smart homes or automobiles).

• Regulatory & Compliance: 

  Although this might be one of the last functions entrusted to this new technology, due to its extremely delicate nature and exposure, both financially and to the insurer’s brand and reputation, this could certainly be feasible down the road.  

  • Autonomous Policy Auditing: Software agents have the capabilities to continuously audit underwriting and claims for regulatory compliance, flagging or correcting issues proactively or bringing them to the attention of internal audit staff before filings and other actions that might expose the insurer to fines or administrative actions from regulators. 

  • Real-Time Reporting: With the ever-changing nature of rules and regulations in the industry, agentic AI holds the promise of automatically generating and submitting regulatory filings or updates. 

• Other Aspects of Internal Operations: 

  • Workflow Optimization: In order to maximize organizational agility and optimize resources, agentic AI could conceivably reassign resources across functions such as underwriting, claims, and service on a dynamic basis, considering current staffing and workloads and reacting quickly to unexpected changes (e.g., reaction to a natural disaster and the increased volume of incoming claims that results). 

  • Knowledge Management: Agentic AI holds the promise of continuously learning from current and historical cases and then updating internal policies, playbooks, processes, and decision logic. This could then also accelerate learning for both current employees and new hires, making both potentially more productive in less time. 

Future Innovations That Can Improve Insurance 

With a technology as new and full of potential as agentic AI, and with today’s applications just emerging, prognostications stand a pretty good chance of being proven conservative or too rooted in the status quo. The crystal ball will need to be polished frequently, just as agentic AI technology itself will surely rapidly evolve.

Given those caveats, here are some thoughts on where this technology may go in the insurance realm. (Note that many of these innovations apply across industries, not just to the insurance vertical): 

• Transformation of the Software Development Profession and Lifecycle 

  In both internal software engineering and when applied by independent software development shops, agentic AI is expected to increasingly manage entire software development and maintenance lifecycles autonomously.  

  These systems could design entire architectures, write and debug code, and oversee quality assurance processes, significantly accelerating software production and transforming digital product development.  

  Of particular interest is how agentic AI could accelerate the development and refinement of software copilots, in a sort of symbiotic relationship that rapidly improves their ability to help humans complete tasks and anticipates and accelerates the evolution of the copilots and their usefulness. 

• Advancements in Cybersecurity 

  As an improvement over current technologies, AI-driven or not, agentic AI systems are anticipated to enhance cybersecurity by autonomously monitoring network traffic, detecting anomalies, and responding to threats in real time.  

  While agentic AI relies on many of the same, proven methodologies as current solutions (machine learning or ML, behavioral analytics, and anomaly detection, for example), AI agents are expected to take cybersecurity to the next level, both in sophistication and response times. This enhanced and more proactive approach aims to strengthen organizational security postures and allow human experts to focus on the most complex challenges.  

• Evolving the Human Resources Function 

  In the human resources realm, agentic AI could further automate processes such as candidate screening, interview scheduling, onboarding, and personalized employee training. By analyzing performance data, these systems can provide tailored career advice, streamline HR operations and give people managers new tools for working with and developing their employees. 

Get Started Now with Earnix 

To accelerate their growth and improve their bottom lines, insurers must improve their tools and techniques across the board.

Now is the time to deploy today’s leading-edge tools that leverage machine learning and AI, and to commit to working with a partner that will evolve those tools and lead the way with innovations such as agentic AI.

Explore what Earnix can do for your organization. Reach out to Earnix today, or schedule a demo to get started on your journey.