Agentic AI – autonomous, self-directed artificial intelligence agents – is poised to revolutionize insurance by automating complex workflows in claims handling and fraud detection. By learning from experience and adapting in real time, agentic AI can perform tasks traditionally done by humans. In claims management, AI agents can handle First Notice of Loss (FNOL) intake, document review, damage assessment, and even straight-through processing of simple claims. In fraud management, they can analyze vast datasets to identify anomalies, link seemingly unrelated claims, and flag suspicious cases more quickly than manual systems. Insurers are already seeing dramatic benefits: for example, Lemonade’s AI, “Jim,” settled a claim in 2 seconds, and Aviva cut claims-processing time by 23 days and saved over £60 million after deploying AI models in claims. Leading consultancies note that agentic AI adds “unprecedented levels of automation to complex workflows”, promising faster cycle times, lower costs, and higher accuracy. However, realizing these gains requires robust data, integration with legacy systems, and careful oversight to manage risks such as bias, regulatory compliance, and AI “hallucinations.” This white paper reviews the technology, use cases, benefits, challenges, and real-world examples of agentic AI in global insurance claims and fraud management, striking a balance between visionary potential and practical considerations.

Agentic AI refers to AI systems that operate as semi-autonomous “agents” with their own reasoning and decision-making capabilities. Unlike traditional AI or RPA (Robotic Process Automation) that follow fixed rules, agentic AI can perceive its environment, learn from new data, and adapt its behavior. It gathers inputs, reasons about goals, and executes actions without human prompts. In insurance, this means AI agents can conduct end-to-end tasks: for example, an agent might intake a claim, evaluate damage from photos using computer vision, verify policy details, and even initiate payment – all without human intervention.

According to industry analysts, agentic AI is still early in adoption but growing rapidly. Gartner estimates that less than 1% of enterprise applications had agentic AI in 2024, but this could grow to 33% by 2028. Surveys report that 80% of organizations in India (and many globally) are already exploring autonomous AI agents. McKinsey projects that by 2030, over half of insurance claims tasks will be fully automated through AI. Insurance is particularly well-suited for this shift: insurers have vast data reserves and complex, data-driven workflows, and AI can handle both structured data (policies, claims history) and unstructured data (photos, reports). Agentic AI brings “unprecedented levels of automation” to these workflows, potentially transforming claims processing and fraud detection.

Key attributes of agentic AI include:

• Autonomy: Agents make goal-driven decisions (e.g., “settle this claim”) with minimal oversight.
• Continuous learning: Unlike static rules, agentic systems adapt from new cases and feedback, improving over time.
• Context awareness: Agents maintain context across steps, enabling them to handle complex scenarios (e.g. escalating exceptions) rather than just single tasks.
• Orchestration: Multiple agents can collaborate as a network (e.g., one agent gathers data, another assesses risk, a third oversees compliance).

These features make agentic AI a powerful upgrade over earlier tools. Where RPA or basic AI handled one step at a time, agents chain together multi-step processes autonomously. As a Newgen case study notes, traditional automation was task-focused, but agentic AI “operates as a network of intelligent agents that can assist, decide, act, and learn continuously” through processes like underwriting and claims management. In short, agentic AI can transform insurance from reactive, manual processing to proactive, end-to-end automation.

Streamlining the Claims Lifecycle

Claims management is a natural fit for agentic AI due to its structured yet varied workflow. Key phases of claims – intake, validation, adjudication, and settlement – involve repetitive tasks ripe for automation, but they also encounter many exceptions (missing documents, ambiguities, etc.) that slow down traditional systems. Agentic AI can handle both routine and exceptional cases by learning from prior experiences. As one Cognizant study explains, agentic systems “continuously learn and then leverage that intelligence to make decisions,” automating many of the exceptions that once required human intervention. For instance, if a submitted claim is missing a document, a traditional AI might stall, but an agentic AI can recall how past cases were resolved and either retrieve the needed info or route the claim appropriately, effectively turning an exception into a routine process.

Agentic AI can be deployed at each step of claims handling. Examples include:

• First Notice of Loss (FNOL) Intake: AI agents can process multi-channel inputs (phone, email, app) and extract all relevant details in real time using NLP and vision. They can classify the claim type and severity and trigger appropriate workflows (assign adjuster, request more info, flag for fraud review). By automating data capture and classification, agents can cut FNOL delays “from hours to minutes”.


• Claims Triage and Routing: Agents analyze the claim context (coverage, complexity, workload) to determine next steps autonomously. For example, one agent might escalate a high-value loss to a senior adjuster, while another handles straightforward cases automatically. This dynamic assignment can optimize resources and speed up processing. BCG notes that insurers are experimenting with AI agents for such routing and are seeing benefits: Aviva, for instance, deployed over 80 AI models in claims and improved routing accuracy by 30% while slashing backlog.


• Document and Evidence Processing: Claims often require processing of varied documents (photos, reports, receipts). Agentic AI can ingest diverse data (structured and unstructured) and extract insights. It can compare images (e.g., accident photos) against expected norms, read handwritten notes, and even handle novel formats (video, bodycam footage) that traditional systems might reject. Over time, the agent learns new file types; for instance, if a claimant uploads a video instead of an image, an agentic system will quickly adapt to analyze it rather than fail. This reduces human rework in document review and speeds adjudication.


• Damage Assessment and Settlement: In property and auto lines, AI vision can immediately assess damage from images or video. Insurers like Allstate and Geico already use smartphone apps and computer vision for this purpose. With agentic AI, this step can be fully automated: an agent can score damage severity, compare it to historical claims, and even calculate fair settlement amounts based on policy rules. In simple cases, the agent can execute straight-through processing, approving payment in moments. For example, Lemonade’s AI Jim famously scanned a claim, validated coverage, ran fraud checks, and settled a genuine claim in just two seconds. The policyholder immediately received notification and payment instructions without human delay.


• Customer Communication and Support: Agents can interact with claimants directly. By analyzing a customer’s tone, intent, and emotional state, an agentic chatbot can provide real-time updates, answer questions, or even schedule field appointments. If frustration is detected (e.g., a delayed claim), the AI can proactively escalate the issue or reassure the customer. This leads to higher satisfaction: as Celent observes, agentic systems deliver transparent, real-time updates and personalized guidance throughout the claim, making a stressful process smooth.

These use cases highlight a common theme: agentic AI makes claims handling faster, more accurate, and more customer-friendly. By internalizing past decisions, agents eliminate many manual steps. For instance, Cognizant notes that instead of humans repeatedly handling the same exceptions, a self-learning agentic system can contextualize tasks, recognize patterns, and apply learned fixes automatically. Over time, this drives significant efficiency gains. Industry data suggests insurers can achieve a 20–40% reduction in onboarding and claims costs, with cycle times cut dramatically.

• Speed: Claims that once took days can be closed in minutes or seconds. Straight-through processing of straightforward claims improves cycle times by up to 40% or more. Aviva’s AI overhaul trimmed complex-case processing by 23 days.
• Accuracy and Consistency: AI agents apply consistent rules. McKinsey reports a 3–5 percentage-point improvement in claims accuracy when AI is fully integrated.
• Scale: AI agents work 24/7 without fatigue, enabling insurers to handle surges (e.g., natural disasters) without hiring staff.
• Customer Experience: Faster payouts and clear updates boost satisfaction. Celent notes agentic AI can turn a “lengthy, overwhelming” claims process into a “smooth, low-stress journey”.

• Lemonade (InsurTech): Now an AI-driven insurer, Lemonade reports that nearly half of its claims are handled by AI. Its chatbot “AI Jim” can evaluate, approve, and pay out a valid claim in two seconds, setting a world record. This dramatic acceleration comes from AI agents reading policy details, running fraud filters, and initiating payments instantly.


• Aviva (U.K.): Aviva deployed 80+ AI models within its claims department. According to McKinsey, this project cut liability assessment time by 23 days, improved correct routing by 30%, reduced customer complaints by 65%, and saved over £60 million in one year. Much of this success stemmed from utilizing AI agents to automate routine decisions and identify cases that required human attention.


• Eastern Alliance (U.S.): Eastern Alliance implemented AI “agents” (named Phyllis) to index and process claims documents. The results: a 100x speed improvement in document processing, saving 2,700+ human hours in a few months. The agents autonomously read and categorize incoming documents (photos, forms), drastically reducing back-office labor and errors.


• Inaza (Ireland): A usage-based insurance startup, Inaza uses AI agents to detect fraud and automate claims across inputs like telematics and text forms. Its platform “analyzes inputs, detects fraud, and automates claims processing,” illustrating how agents can serve specialized insurers.

These cases underscore that agentic AI is not just theoretical: insurers and insurtechs globally are piloting and scaling agentic solutions in claims with measurable ROI.

Insurance fraud – false or exaggerated claims – is a perennial challenge. Industry data show fraud costs hundreds of billions annually (e.g. $122 B in U.S. P&C losses[34], £1.1B in the UK alone). Agentic AI brings new power to fight fraud by rapidly analyzing complex patterns across claims data, enabling proactive detection rather than after-the-fact review.

Traditional fraud checks rely on rule-based flags (e.g., suspicious claim amounts) or manual audits, which are slow and often miss novel schemes. In contrast, AI agents can process both structured and unstructured data – claims history, text notes, images, voice transcripts, even social media – to uncover subtle anomalies. As Datagrid notes, machine learning can now “see patterns invisible to even the most experienced analysts,” learning from each new claim to adapt to fraudsters’ evolving tactics.

Key capabilities of agentic AI in fraud detection include:

• Pattern Recognition: Agents use predictive analytics to identify red flags. For example, if a healthcare provider submits multiple reimbursements for overlapping procedures, an AI agent can instantly identify the inconsistency and flag the case, often faster than human investigators. Agentic systems continuously learn from confirmed fraud cases, sharpening their detection rules.
• Cross-Claim Linking: AI excels at connecting dots across different claims. Fraud rings often spread small claims across policies to avoid detection. AI agents, by contrast, can analyze network graphs and find “telltale fingerprints” linking those claims, uncovering organized schemes that humans might miss.
• Real-Time Screening: Instead of batch reviews, agents can assess fraud risk as soon as a claim is filed. For legitimate customers, this means faster processing; for suspicious ones, immediate escalation. XCubeLabs explains that agentic AI can run fraud checks in the background to “allow genuine customers to receive fast approvals while flagging risky cases for deeper review.”
• Continuous Improvement: Agentic AI adapts to new fraud methods. Kellton notes agents “learn from emerging fraud tactics, ensuring long-term effectiveness.” Analysts can A/B test different models via agents to optimize detection rates without raising false positives.
• Multimodal Analysis: Modern fraud schemes may involve text, images, and even voice. Advanced agents integrate these modalities – e.g., matching a photo of damage against the reported story or using speech sentiment analysis to detect deception in phone claims.

The quantitative impact of AI in fraud detection is substantial. Machine learning systems typically catch far more fraud than manual processes, greatly reducing leakage. For example, Datagrid reports that AI-enabled detection significantly lowers loss ratios by identifying hidden fraud. Deloitte predicts that if P&C insurers implement AI across claims, they could prevent $80–160 billion of fraudulent payouts by 2032. In practice, insurers see both tighter security and business benefits: better fraud screening “lowers loss ratios” and allows faster payouts for genuine claims, improving trust.

Major insurers are already moving: Zurich Insurance has built 200+ AI use cases, including fraud detection, while AXA uses AI to spot patterns missed by humans. These efforts pay off directly. For instance, policyholders in competitive markets ultimately benefit through lower premiums as fraud losses shrink. And operationally, automated fraud screening means investigators can focus on complex cases rather than sifting through the obvious ones.

• High-Quality Training Data: Agents need rich data (including past fraud cases) to learn effectively. Cognizant advises investing in realistic training datasets; otherwise, AI decisions can be erroneous.
• Human-AI Collaboration: Maintain analysts in the loop. AI should flag suspects, but humans should verify critical cases. Deloitte emphasizes combining AI with human oversight to maintain fairness and accuracy.
• Balanced Thresholds: Tune agentic decisions to minimize false positives. Extensive A/B testing (as Kellton notes) helps find the sweet spot where fraud is caught without spamming legitimate claims.
• Regulatory Alignment: Since fraud detection is sensitive, systems must comply with laws (e.g., data privacy). Agents should be designed to respect these boundaries even as they analyze data.

• Healthcare Provider Fraud: An AI agent might analyze billing patterns across all claims and notice a clinic repeatedly billing for overlapping procedures. That pattern triggers an alert much faster than random audits.
• Auto Accident Rings: In the U.S., agents have linked staged accidents by correlating small differences in claim details across policies – an analysis impractical for humans, but routine for AI.
• Internal Commission Schemes: Agents can monitor commission payments and spot irregular structures indicative of kickbacks, spotting schemes often invisible to legacy systems.

In summary, agentic AI transforms fraud management from reactive rule-checking to proactive prevention. By continuously sifting through data, AI agents help insurers stay ahead of fraudsters, protecting revenues and honest policyholders alike.

Successfully deploying agentic AI requires more than technology alone. Insurers must integrate AI agents into their broader processes and technology stacks. Key considerations include:

• Modern Data and Technology Infrastructure: Agents thrive on data. Insurers need clean, high-quality data lakes and modern APIs so agents can access all relevant information (policy details, claims history, external data). Legacy systems often require refactoring to interface with AI platforms.
• Reusable AI Components: McKinsey suggests building reusable AI modules. For example, a generative AI model trained to answer customer queries can be repurposed for claims document analysis. This modular approach speeds development of new agents.
• Cross-Functional Change Management: Adopting agentic AI is as much organizational as technical. McKinsey stresses aligning leadership around an AI vision and transforming end-to-end workflows. Employee training and cultural buy-in are crucial since staff must trust and work with AI co-pilots.
• Governance and Oversight: Even autonomous agents need guardrails. Implement AI governance frameworks for monitoring agent decisions, managing model drift, and ensuring ethical compliance. For example, agents can log every decision in an audit trail, simplifying reviews.
• Data Privacy and Security: Agents will access sensitive customer data, so insurers must enforce strict security (encryption, access controls) and comply with regulations like the EU AI Act or HIPAA (for health claims). As Celent cautions, robust data protection is essential to realize AI’s benefits.
• Continuous Learning Loop: Agentic AI systems should be designed for ongoing improvement. Human-in-the-loop processes (as in the Eastern Alliance example) allow experts to correct agent mistakes and feed that feedback back into the model. This ensures agents adapt accurately over time.

Overall, insurers often start with pilot projects (e.g., automating FNOL or specific fraud checks) and scale gradually. By focusing first on high-impact workflows, insurers can demonstrate quick wins (as Aviva did with claims) and build momentum for wider agentic AI adoption.

Agentic AI offers big rewards, but not without challenges. Companies must address:

• Data Quality and Bias: AI agents are only as good as their data. Incomplete or biased training data can lead to wrong decisions. For example, if historical claims data is skewed, the agent may unfairly flag certain customer segments. Insurers must proactively audit and cleanse data, and potentially adjust models to correct biases.


• Model Errors and Hallucinations: Generative and agentic AI models can sometimes produce unexpected outputs (“hallucinations”). In insurance, a hallucinated response could misclassify a claim or wrongly approve a payment. To mitigate this, companies need robust testing regimes. Celent and Deloitte both highlight the importance of human review for edge cases and setting up triggers when an agent’s confidence is low.


• Regulatory Uncertainty: The AI landscape is still evolving legally. Regulations like the upcoming EU AI Act introduce requirements (transparency, fairness) that agentic systems must satisfy. Insurers must design AI with explainability in mind, even though agents operate autonomously. This may involve keeping logs of how agents reached decisions and providing understandable rationales when questioned.


• Security and Fraudulent Use: Just as AI can fight fraud, adversaries might try to trick AI agents (e.g., poisoning claims data or reverse-engineering agent patterns). Insurers must guard against this via secure design and anomaly monitoring of the AI itself.


• Legacy Culture and Talent Gaps: Many insurers struggle with legacy mindsets and a shortage of AI talent. A Bain/BCG study notes that while insurers lead in adoption enthusiasm, only ~7% scale pilots successfully. Effective transformation requires training staff to partner with AI and possibly hiring or partnering for AI expertise.


• Return on Investment: Deploying agentic AI can require significant upfront investment in tools, data infrastructure, and change management. While the long-term payback (cost savings, growth) can be large, insurers must plan carefully. Deloitte’s survey found that many companies prefer buying AI solutions rather than building in-house, but then must ensure the tools can be adapted to their unique needs.

Despite these risks, experts agree that early adoption has long-term payoff. As one Deloitte leader notes, organizations that embrace agentic AI and scale it responsibly will redefine the next era of insurance. The key is balancing “rapid adoption with sustainable strategies” through strong governance and incremental deployment.

Agentic AI represents the next frontier in insurance innovation. By granting AI agents autonomy and learning ability, insurers can automate entire claim lifecycles and tighten fraud defenses in ways previously impossible. Early examples – from Lemonade’s 2-second claim settlement to Aviva’s £60M savings – demonstrate that the technology works and delivers real value. With estimates that AI could manage half of all claims tasks by 2030, the industry is clearly on the cusp of a transformation.

For insurers and insurtechs, the message is clear: building agentic AI capabilities is no longer optional. As McKinsey warns, merely piloting AI will not suffice; companies must retool processes end-to-end to be AI-native. Leaders in the space are already doing this by integrating AI agents as “virtual coworkers” – handling everything from customer onboarding to underwriting support and, of course, claims and fraud. Insurers that invest in robust data platforms, modular AI components, and change management will be well-positioned to harness these agents for competitive advantage.

In balance, agentic AI is not a panacea. It brings new challenges in governance, ethics, and implementation. But when designed responsibly, the business benefits are immense: faster claims processing, fewer fraudulent payouts, higher customer satisfaction, and lower operating costs. As one industry analyst summarized, “AI systems that can act, learn, and improve on their own enable insurers to handle tasks at unprecedented speed and scale”. For insurance and insurtech enthusiasts, the rise of agentic AI heralds a pivotal shift – one that will define how claims and fraud are managed globally in the years ahead.