Insurers have relied on automation for years to improve efficiency, reduce costs, and streamline workflows. Robotic process automation (RPA) and rule-based decision engines have been instrumental in handling structured, repetitive tasks. But these systems have a fundamental limitation: They follow predefined rules without the ability to adapt, reason, or respond to complex scenarios in real time.
Agentic systems have changed the game. Unlike traditional automation, agentic systems continuously learn from new data, collaborate across functions, and optimize workflows based on outcomes. Agentic artificial intelligence (AI) is on the rise, too. According to Deloitte, 25% of organizations that already use generative AI (GenAI) will launch agentic AI pilots in 2025 — and that number will double by 2027.
Additionally, Forbes predicts that agentic AI will become a $100 billion industry by 2030, signaling that insurers who adopt it early will gain a competitive advantage. For the property and casualty (P&C) insurance industry, agentic AI could mean more accurate underwriting risk evaluation, proactive fraud detection, smarter claims triage, and dynamic policy adjustments.
What Are Agentic Systems?
Traditional forms of automation like RPA have played a key role in streamlining insurance operations, but their inherent rigidity often creates bottlenecks when faced with new or unstructured data or unexpected scenarios. Insurers must look beyond rule-based automation to solutions that incorporate real-time reasoning, contextual awareness, and the ability to learn from new data.
Unlike traditional automation, agentic systems leverage more advanced technologies such as machine learning, natural language processing (NLP), and reinforcement learning to operate autonomously, make complex decisions, and continuously improve over time. Not only can they independently execute tasks based on learned patterns and real-time data, such as automating an entire claims processing workflow, but they can also understand and interpret documents, conversations, and historical trends to provide more accurate risk assessments and personalized customer interactions.
Agentic AI can modify its actions based on new information, such as adjusting risk evaluations in real time by analyzing emerging data, leading to more accurate pricing models. On top of all of this, AI agents can work together, sharing insights across underwriting, claims, and fraud detection teams to ensure a seamless flow of information within the organization.
Use Cases of Agentic AI in P&C Insurance
By integrating agentic systems, insurers can transition from rigid, rule-based processes to dynamic, intelligent operations, leading to improved efficiency, reduced costs, and enhanced customer satisfaction.
Like GenAI before it, agentic AI is poised to revolutionize the P&C insurance industry by introducing advanced capabilities that transcend traditional automation. This technology can be leveraged across the value chain to improve insurers’ business outcomes. Here are four key use cases:
1. Underwriting Risk Evaluation
Traditional underwriting relies on static checklists and predefined risk models, limiting insurers’ ability to assess emerging risks dynamically. Agentic AI enhances underwriting by analyzing that may be unstructured or in a different format, external data sources, and historical claim patterns. For example, when processing a cyber insurance application, an AI agent can scan the submission, compare it against industry benchmarks, suggest possible coverages that might be additional needs, and flag inconsistencies for further review — all without manual intervention.
Additionally, these intelligent systems provide real-time risk recommendations by continuously adapting to new regulatory requirements and market conditions. If a government agency updates compliance requirements for flood insurance, an agentic AI system can immediately adjust underwriting criteria, ensuring compliance without human intervention.
Acting as AI underwriting assistants, these systems also support human underwriters by offering contextual insights, improving decision-making, and increasing efficiency.
2. Fraud Detection
Insurance fraud costs the industry over $308 billion annually, yet many fraud detection systems rely on rigid, rule-based triggers, based on pre-trained models, that generate false positives and require time-consuming investigations. Agentic AI systems improve fraud detection by continuously learning from evolving fraud patterns and correlating disparate data sources — including police reports and social media activity.
For example, if an auto insurance claim is submitted just days after a policy purchase, an AI fraud detection agent can cross-reference social media posts to identify potential red flags. If the policyholder recently posted about a staged accident or vehicle sale, the system can then automatically escalate the case for human review, reducing the burden on fraud investigators and improving response times.
By automating fraud detection and reducing manual workload, agentic AI enables insurers to identify suspicious activities faster, mitigate losses, and protect honest policyholders from premium increases caused by fraudulent claims.
3. Claims Triage and Processing
AI-driven claims processing can reduce loss-adjusting costs by 20%-25% and claims leakage by up to 50%. Claims processing has traditionally been a slow, manual workflow, often leading to delays and customer frustration. Agentic AI systems optimize this process by automating triage, assigning claims based on severity, complexity, and adjuster availability.
For instance, when a homeowner submits a hail damage claim, an AI-powered claims triage agent can analyze satellite imagery, compare it with historical weather reports, and verify the claim’s legitimacy in real time. If the damage aligns with a recent storm in the area, the system can automatically fast-track the claim for approval. However, if discrepancies arise — such as no reported hailstorm in the region — the AI can flag the claim for further investigation.
In addition to triage, agentic AI can analyze damage estimates from accident photos, reducing reliance on manual assessments. By dynamically adjusting workflows, these systems ensure straightforward claims are processed quickly while complex cases receive the scrutiny they require. This leads to and reduced operational costs.
4. Dynamic Policy Adjustments
Static insurance policies often fail to account for real-time changes in risk exposure. Agentic AI enables insurers to offer more flexible and personalized coverage by continuously monitoring external risk factors such as climate data, economic conditions, and individual policyholder behavior.
For example, if an AI agent detects an increased wildfire risk for a commercial property due to prolonged drought conditions and nearby wildfire activity, it can proactively recommend an adjusted coverage plan. This not only helps mitigate financial losses for the insurer but also ensures that policyholders have adequate protection before a disaster occurs.
Beyond risk monitoring, agentic AI supports dynamic pricing by adjusting premiums based on usage-based insurance (UBI) principles. In auto insurance, for example, AI-powered systems can analyze real-time driving behavior and adjust policy rates accordingly. A safe driver who consistently follows speed limits and avoids hard braking may receive lower premiums, while high-risk drivers may see rate increases based on their driving patterns.
Embracing Intelligent Workflows
As insurers adopt agentic AI, they should build these systems on a strong data foundation while working alongside human decision-makers and operating within regulatory constraints. A well-planned approach will help insurers maximize AI’s benefits while maintaining trust, accuracy, and compliance.
Agentic AI isn’t a replacement for humans; its intelligent workflows help your most valuable resources, your employees, focus on providing exceptional value to your customers.
Interested in learning more about how to use AI to create a better insurance experience? Read our whitepaper, “The Insurer’s Generative AI Handbook.”
