Agentic AI in Insurance Isn’t Coming – It’s Here

While Generative AI has been making waves across a variety of industries, the use of AI in the insurance industry - where precision, risk control and expert judgment are crucial - demands a more advanced solution. It requires smart systems that can think ahead, plan moves, and handle tough stuff like underwriting, claims, or spotting risks on their own.

That's where agentic AI steps in.

Instead of rigid bots following scripts, these agents reason like pros, learn from what happens, and team up with humans when needed. Already making waves in insurers' daily ops, they're slashing wait times and boosting accuracy.

In this article, you’ll learn how agentic AI is transforming the insurance landscape. We’ll cover its fundamentals, real-world use cases and business examples of how actual insurers are leveraging it.

Basics first: Why enterprises need to invest in Agentic AI in 2026

What does “agentic AI in insurance” mean?

Agentic AI refers to autonomous systems that can understand goals, plan actions and execute them with minimal human input. In the instance of the insurance industry, this means AI agents that can independently manage common workflows end-to-end - including claims processing, underwriting, risk analysis and much more.

Unlike chatbots or rule-based systems that follow predefined scripts, agentic AI can reason, adapt and make context-aware decisions; skills that align closely with how insurance professionals work.

Key elements that define agentic AI in insurance:

• Goal-oriented reasoning: It understands an objective, such as validating a claim and determines the best path to achieve it.
• Autonomous execution: Once trained, it can perform complex tasks, such as risk scoring or claims triage, without requiring constant human guidance.
• Continuous learning: It refines its performance using past outcomes and new data, improving accuracy over time.
• Collaborative intelligence: It integrates seamlessly with human experts, enhancing decisions when judgment or empathy is required.

Take Allianz, the financial services company, for example. They launched Project Nemo, an agentic AI system with seven specialized agents: planner, coverage, weather, fraud, payout, audit and cyber. These specialized agents collaborate to automate low-complexity claims such as food spoilage, cutting processing time from days to hours while keeping a human in the loop for final approval.

Deployed in Australia in July 2025, Nemo was built in under 100 days and achieved up to an 80% reduction in claim processing and settlement time, all while maintaining full auditability and compliance.

This new generation of AI is moving insurance from reactive claim handling to predictive, experience-led engagement. Insurers can now anticipate risks, personalize coverage and deliver faster, more transparent interactions that build long-term customer trust.

Preliminary read: 5 real-world Agentic AI use cases for enterprises

5 compelling use-cases of agentic AI in insurance

Insurance is a complex ecosystem of policies, claims and compliance, all of which can be a nightmare when trying to set up automated workflows. However, for the same reasons, it offers multiple entry points for delivering value through agentic AI. The following use cases demonstrate how insurers can leverage agentic AI across their operations to achieve faster, more intelligent and more reliable outcomes:

1. Claims resolution and fraud triage

Insurers handle thousands of claims daily, each requiring validation, document review and fraud checks before approval. The process is often slow, manual and prone to errors, leading to delayed settlements, higher operational costs and dissatisfied policyholders. Her's how agentic AI helps resolve claims and prevent fraud:

1. Context understanding and triage

Agentic AI reviews the claim’s intent, policy terms and contextual details from structured and unstructured data. It automatically categorizes claims by complexity, routing straightforward cases, such as minor property damage, to self-resolution pipelines while flagging those that require expert review.

2. Automated evidence validation

The AI agents analyze claim documents, images and repair estimates. Using computer vision and natural language processing (NLP) models, they cross-check submitted evidence with policy databases and past claims. Insurers no longer need to manually match photos or documents; the AI validates them in seconds.

3. Dynamic fraud detection

The system runs multi-agent checks across transaction history, claimant behavior and telematics data. One agent identifies anomalies, while another compares the claim against external fraud databases to verify its accuracy. This reduces false positives and helps investigators focus only on genuine fraud cases.

4. Collaborative decision-making

Once verified, the AI agents generate a confidence score and recommended action. If the score exceeds the threshold, the claim is automatically approved; otherwise, it’s passed to a human adjuster with full context. The human-in-the-loop model ensures both speed and compliance.

5. Continuous learning and audit trail

Every decision is recorded, explained and used for retraining the model. Over time, the AI improves accuracy while maintaining a complete audit trail, a feature that legacy automation systems could never guarantee.

Through this approach, agentic AI transforms claims management from a reactive, manual task into a proactive, intelligent operation, reducing resolution time from days to hours while cutting fraud losses and improving customer confidence.

Also Read: Agentic AI in finance: Reduce fraud losses effectively

2. Underwriting and risk assessment

Underwriting sits at the core of insurance operations, yet it remains one of the most resource-intensive and time-consuming functions within the industry.

Traditional underwriting relies on static models and historical data, requiring underwriters to manually review forms, assess risks and determine policy prices. This approach slows policy issuance and increases the chance of inconsistent or biased evaluations. Agentic AI can transform underwriting and risk assessment, with :

1. Automated data gathering and prefill Agentic AI agents extract applicant data from multiple sources, customer forms, credit history, IoT devices and third-party databases. This prefill process saves underwriters hours of manual entry and ensures each application starts with accurate, verified data.

2. Contextual risk reasoning Once the data is aggregated, the AI evaluates risk across various dimensions, including asset type, geography and claim history. Instead of static rules, it applies reasoning to detect outliers and correlations that traditional scoring systems overlook. Underwriters get dynamic risk profiles built on live intelligence.

3. Decision recommendations and straight-through processing For low- to medium-risk applications, the AI autonomously issues recommendations or approvals based on predefined confidence thresholds. Complex cases are escalated to human experts with detailed justifications and scenario comparisons, helping them make faster, more informed decisions.

4. Continuous model refinement Every underwriting decision feeds back into the learning loop. The system tracks outcomes, claims filed, policy renewals, customer satisfaction and adjusts models to reflect real-world risk behavior. This ensures that underwriting accuracy improves with every cycle.

5. Portfolio-level insights Agentic AI also provides portfolio analytics, helping insurers identify concentration risks, regional exposure or emerging risk patterns. These insights enable proactive adjustments to pricing strategies and risk appetite.

With agentic AI, underwriting shifts from a manual, document-driven task to an adaptive, intelligence-led process. Insurers can now issue accurate quotes in minutes, identify risks earlier and maintain a balanced, data-driven portfolio that evolves with market conditions.

3. Policy management and billing continuity

Managing policies and ensuring timely billing are ongoing challenges for insurers. Manual processes often result in missed renewals, payment delays and policy lapses, which frustrate customers and increase administrative workload.

Maintaining compliance in your contact center across regional laws and compliance requirements adds another layer of complexity, making it challenging to deliver a consistent experience. Agentic AI can strengthen policy management and billing continuity by:

1. Intelligent policy lifecycle tracking Agentic AI monitors each policy across its full term, tracking due dates, coverage changes and renewal milestones. When a renewal approaches, it automatically generates reminders and recalculates premiums based on updated customer data or risk exposure, keeping policies active without human intervention.

2. Automated premium calculation and validation AI agents retrieve real-time market data, customer history and regulatory parameters to calculate premiums with precision and accuracy. They cross-verify values to avoid mispricing or noncompliance. Insurers no longer rely on batch updates or manual spreadsheets; every quote reflects the latest information.

3. Secure and compliant payments The system integrates with payment gateways and banking APIs to initiate, confirm and reconcile payments. It recognizes failed transactions instantly and re-triggers payment requests with compliant notices, avoiding lapses and ensuring regulatory consistency.

4. Adaptive renewal and lapse prevention If a payment fails, agentic AI doesn’t just alert the user; it analyzes the cause, whether technical or behavioral and takes corrective action. It may retry payment through another channel or notify the customer with contextual suggestions, such as switching to an alternate payment date.

Did you know: Accenture’s billing & payments engine notes that AI-driven insurance billing can automate premium calculations and secure payments, enabling accurate and timely billing while reducing lapses.

Agentic AI extends this capability by orchestrating the entire policy lifecycle, from issuance to renewal, through reasoning-based automation and adaptive workflows.

5. Audit-ready records and transparency Every transaction and policy change is logged with reason codes, timestamps and approval trails. This not only simplifies audits but also gives insurers real-time visibility into revenue flow and customer behavior.

Through agentic AI, insurers transition from reactive policy administration to proactive policy assurance. Renewals occur seamlessly, billing remains accurate and customers enjoy uninterrupted coverage, all while maintaining compliance and transparency.

4. Customer service and engagement

Customer service has become a major differentiator in the insurance industry, yet most insurers still rely on fragmented systems and labor-intensive processes to handle claims, queries and policy updates.

This results in prolonged response times, inconsistent service quality and increasing operational costs. Customers expect instant, personalized support across all channels but legacy tools often struggle to keep up. You can improve customer service and engagement with Agentic AI, with:

1. Unified customer understanding Agentic AI consolidates customer data from claims, policies and prior interactions to build a live context profile. When a customer reaches out through chat, voice or email, the system instantly retrieves relevant details, so the conversation starts with understanding, not repetition.

2. Proactive support through reasoning Instead of waiting for tickets, AI agents predict customer needs. For example, if a policyholder reports a vehicle issue, the AI checks coverage, schedules an inspection and automatically shares the next steps. It acts like a digital service advisor working in the background.

3. Real-time guidance for human agents During live interactions, the AI assists service representatives by suggesting accurate responses, verifying compliance language and summarizing case histories to enhance the overall customer experience. This reduces the average handling time (AHT) and improves consistency without replacing the human touch.

4. Intelligent virtual assistants Unlike traditional chatbots that follow fixed scripts, agentic AI agents can reason through multi-turn conversations. They can resolve complex queries, such as claim rejections or premium adjustments, by interacting with multiple backend systems in real-time.

5. Continuous improvement and empathy calibration The system tracks engagement outcomes and customer sentiment to refine its responses. Over time, the AI learns which tone, timing and resolution paths yield better satisfaction scores, resulting in more empathetic and practical service experiences.

With agentic AI, insurers evolve from reactive support desks to intelligent engagement ecosystems. Customers receive faster, more personalized responses, while service teams handle complex cases with context, accuracy and confidence.

5. Property and catastrophe operations

Large-scale catastrophe (CAT) events test every part of an insurer’s operation. When floods, hurricanes or earthquakes strike, teams face a sudden surge in claims, limited field resources and fragmented data sources. Traditional manual workflows slow down assessments and delay payouts when customers need help the most. Here are 5 ways agentic AI transforms property and catastrophe operations:

1. Real-time asset monitoring and alerts Agentic AI connects with IoT sensors, weather feeds and satellite imagery to track insured assets in real-time. When a potential event, such as rising water levels or wind surges, is detected, the AI alerts both policyholders and internal teams, enabling proactive damage mitigation before claims even arise.

2. Automated damage assessment After a catastrophe, AI agents analyze satellite and drone images to estimate the impact on properties. They classify damage severity, match it with policy data and generate preliminary loss reports. This saves insurers from deploying large manual field teams and accelerates the First Notice of Loss (FNOL) process.

3. Prioritized claims handling The system ranks incoming claims by urgency, coverage type and customer vulnerability. It automatically assigns field inspectors and digital adjusters, ensuring that critical cases, such as total property loss, receive immediate attention.

4. Vendor and resource coordination Multiple AI agents manage logistics, contractor dispatch and supply availability. If repair crews are limited, the system dynamically reallocates resources across affected zones to maximize efficiency.

5. Post-event analytics and prevention insights Once the event concludes, the AI compiles incident-level reports, comparing loss ratios, regional exposure and model performance. It identifies patterns that help underwriters and risk teams improve preparedness for future CAT events.

With agentic AI, property and catastrophe management becomes predictive and organized. Insurers can assess, process and settle claims in a fraction of the time, reducing customer distress, maintaining trust and improving operational control even during large-scale crises.

Get Inspired: Global insurer scales engagement with Unified-CXM

A leading financial services and insurance provider struggled with manual engagement, disconnected data and poor routing that slowed response times and limited visibility. During volume spikes, service teams struggled to prioritize and act quickly enough, resulting in missed opportunities and inconsistent customer experiences.

The company implemented Sprinklr Unified-CXM, featuring AI-powered social listening, to uncover engagement and sales opportunities from unstructured online conversations. Using Sprinklr Service automation, messages were automatically classified as engageable or non-engageable and routed to the right teams for faster action.

When inbound messages surged to 7,714 in a single week, a 110% increase, the system’s Intuition Moderation capability automatically closed 3,248 messages (45%), filtering out non-actionable traffic. The result: higher agent efficiency, faster responses and consistent customer experiences even during peak interaction periods.

Let Sprinklr AI agents guide your insurance teams into this new era

Having worked with global insurers, we’ve realized that insurance enterprises need technology that can think, reason and improve on its own. Sprinklr AI Agents are built for this shift. Insurers can customize them to match specific workflows, underwriting, claims or customer servicing, while staying fully compliant with data regulations.

These agents learn from client interactions, process data autonomously and report progress in real-time, freeing your human agents to focus on what matters most: strengthening client relationships and driving strategic growth. It’s already happening across the industry. Book a free demo and see how Sprinklr AI Agents can transform how your enterprise operates.