Today’s customers expect self-service, speed, and transparency across the board, and insurance claims experiences are no exception. But outdated workflows and manual processes can lead to delays and a frustrating customer experience.

Artificial intelligence (AI), robotic process automation (RPA), machine learning, and analytics have all helped insurers begin optimizing their claims experience. However, agentic systems may be the key to fully transforming the claims experience for customers and insurers alike.

In fact, according to Forbes, AI in insurance has led to up to a 99.99% enhancement in claims accuracy and a 95% improvement in customer experience. By automating repetitive, labor-intensive tasks, agentic systems free up adjusters to focus on high-value, complex claims, ultimately lowering operational costs and improving customer experience.

Why Insurers Need Agentic Systems for Claims Processing

From inconsistent decisions to rising fraud risks and mounting customer dissatisfaction, the cracks in traditional claims workflows are increasingly hard to ignore. Manual processes can significantly delay the claims process, especially during periods of catastrophic loss.

For example, the average time from FNOL to final payment for homeowners claims has surpassed 44 days, the longest since 2008. These delays can even lead customers to leave their insurer. Among customers dissatisfied with the claims process, 26% said they changed providers, and 48% considered switching.

Adjusters often rely on subjective judgment, leading to variability in claim outcomes. And if insurers aren’t effectively leveraging analytics, their fraud approach may be more reactive than proactive, which lets preventable losses occur. To overcome these challenges, insurers are increasingly turning to agentic systems, AI-powered agents that go beyond traditional automation by working autonomously within orchestrated workflows to carry out specific tasks.

Agentic systems can help streamline every stage of the claims process. By automating data extraction, document classification, and workflow coordination, claims can be processed significantly faster, turning weeks-long timelines into hours or even minutes. Machine learning models within agentic systems can apply consistent, data-driven logic to each claim, eliminating human error and bias.

Real-time fraud detection agents can continuously evaluate claim data against behavioral patterns, historical records, and third-party sources to escalate suspicious claims for review while letting low-risk claims through.

Agentic Systems in Action: A Claims Processing Workflow

Combining these agentic systems with modern platforms can enable insurers to reimagine the claims journey, transforming it from a reactive function into a proactive, value-generating process. But what does this transformation look like in practice?

Step 1: FNOL

A policyholder reports a loss via phone, portal, or mobile app. An agent powered by natural language processing (NLP) extracts key information: date of loss, policyholder identity, incident description, and location. The system verifies policy status and triages the claim in real time, eliminating delays.

Step 2: Coverage Assessment and Policy Validation

An underwriting agent cross-checks FNOL inputs with policy terms. AI evaluates deductibles, exclusions, and limits in real time, surfacing only edge cases or inconsistencies for human review. This minimizes delays caused by back-and-forth between departments.

Step 3: Damage Assessment and Loss Estimation

For property and auto claims, computer vision agents analyze submitted photos and videos to assess visible damage. These agents draw on historical claims data and repair benchmarks to estimate costs quickly and accurately. If the damage qualifies under predefined thresholds, the system may even trigger an instant settlement offer — no adjuster needed.

Step 4: Fraud Detection and Risk Scoring

While the claim is moving forward, a fraud detection agent runs in parallel, analyzing behavioral patterns, data inconsistencies, and third-party databases. Claims are scored for fraud risk, with high-risk cases flagged for investigation and low-risk ones cleared for expedited processing.

Step 5: Settlement Recommendation and Payout Processing

An agentic negotiation model recommends fair settlement amounts based on industry benchmarks and precedent. Upon claimant approval, the system initiates payment automatically. Disputed cases may be routed to a mediation agent for resolution.

Step 6: Claims Closure and Customer Communication

Once settled, AI automatically generates documentation for internal compliance and regulatory reporting. Meanwhile, a conversational AI assistant updates the claimant via SMS, chatbot, or email, ensuring transparency throughout. A satisfaction survey is triggered, feeding feedback into system models to inform future interactions.

This modern, agentic workflow highlights how insurers can shift from fragmented, manual processes to an intelligent, end-to-end system that adapts in real time. By embedding AI agents at every step, insurers can reduce cycle times, improve accuracy, and deliver the kind of transparent, responsive service policyholders now expect.

The Road Ahead: Future Innovations in Agentic Claims Systems

The adoption of agentic AI in claims processing is just beginning. As the technology matures, insurers can expect even more advanced capabilities that go beyond today’s automation. Innovations on the horizon include:

• Hyper-Personalized Claims Handling: Behavioral analytics will tailor claims journeys to individual policyholders, adapting messaging, channels, and pacing.
• AI-Driven Litigation Support: Agentic systems will support complex cases by assessing legal exposure, summarizing case files, and recommending strategies.
• Blockchain-Enabled Smart Contracts: Smart contracts will automate claims payouts by executing agreements instantly once defined conditions are met.
• Multi-Agent Collaboration: AI agents, adjusters, and third-party vendors will work together in real time, improving coordination and reducing delays.
• Intelligent Adjuster Copilots: AI agents can run in the background to help adjusters account for every manual step in their workflow, thereby reducing cost and cycle time and improving claim handling accuracy.

As these technologies evolve, insurers that invest early will transform claims from a back-office burden into a strategic differentiator — reducing fraud, accelerating outcomes, and building long-term policyholder trust. Agentic systems aren’t just enhancing claims processing; they’re redefining what it means to deliver value in insurance.

Interested in other ways AI can improve experiences across the insurance life cycle? Read our whitepaper “The Insurer’s Generative AI Handbook.”