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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteAgentic claims intelligence is a set of coordinated software agents, each given a bounded task and limited access to policy and claim data, that move a claim through steps such as intake, coverage checking, fraud screening, payout calculation and audit summary. In the best-documented public example, Allianz’s Project Nemo in Australia, the agents assemble an audit summary in less than five minutes and a human makes the final payment decision. That example is narrow: small food-spoilage claims, typically below AUD 500, arising from weather-related power outages. It does not show that insurers have generally automated end-to-end claims handling. Most performance figures in circulation are reported by the insurer or vendor describing its own system, and they have not been independently validated.
What separates agentic claims workflows from chatbots and simple automation
Three kinds of software often get confused in claims operations. A chatbot answers questions or drafts text inside a conversation. Conventional automation runs one fixed rule or one model against one input. An agentic workflow coordinates several specialized steps on the same claim, and each step can use policy, claim and external data within permissions the insurer sets. Allianz defines agentic AI as systems of specialized, task-oriented agents that can independently plan, decide and collaborate across multi-step workflows.
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| Dimension | Chatbot or assistant | Single-task automation or model | Agentic claims workflow |
|---|---|---|---|
| Typical job | Answers questions or drafts text | Performs one task, such as reading a document or estimating a settlement | Links several task steps on one claim, such as coverage, evidence, fraud and payout checks |
| Context it uses | Whatever the user supplies in the conversation | Its own input data | Policy, claim, weather and payment context through integrations, within set permissions |
| Output | A reply | A score, extraction or label | An assembled audit summary and recommendation for a person, or routing to a queue |
| Who decides | The user | A claims handler or rule-based process acts on the result | A named human keeps final authority in the public examples |
The distinction matters because agentic orchestration does not replace the models underneath it. The NAIC notes that traditional machine learning already supports claims through image analysis, settlement estimation and fraud detection. Agentic orchestration adds linked actions and workflow context, but the underlying predictions and decisions still need to be validated.
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The seven steps of an agentic claims workflow
The sequence below combines Allianz’s Project Nemo description with Duck Creek’s account of intake. Products divide the work differently, so read the step names as functions rather than a fixed architecture. Allianz’s list of seven agents also includes planning and cybersecurity oversight. Those are supervisory tasks rather than claim decisions, and Allianz’s public description says little about how they work.
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1. Intake and validation
Intake is where a claim enters the system through digital forms, voice calls or mobile submissions. Duck Creek says its Agentic FNOL application captures, validates and routes claims across digital, voice and mobile channels. Validation checks that required fields are complete and consistent with policy data. Allianz’s German pet-insurance process uses the same pattern, with OCR extraction from uploaded documents and field validation against policy data. A mismatched policy number or an unreadable document should be caught here, before any other agent acts on the claim.
2. Coverage checking
The coverage step asks whether the policy was in force on the date of loss and whether the type of loss is covered. Allianz lists coverage verification as one of the Project Nemo agent tasks. Duck Creek says its application can verify policy and coverage at intake. This agent needs read access to policy terms. A sound design does not let it modify them.
3. Weather and external evidence confirmation
In Project Nemo, an agent confirms the weather context that supports a food-spoilage claim after a power outage. This step depends on data from outside the insurer, so the quality of that feed matters. A delayed or incorrect weather record can send a valid claim down the wrong path or let an invalid one through.
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4. Fraud screening
Project Nemo’s agents screen for fraud signals, and Duck Creek says its application can identify potential fraud at intake. The output of this step should be a flag that routes a claim for review. It should not be a finding. A model-generated signal is a reason to look more closely, not evidence that a claim is fraudulent.
5. Calculation support
Project Nemo includes a payout calculation agent that works out the payout amount. The figure is support for a decision, not the decision. It appears in the audit summary, where a person can compare it with the policy terms before anything is paid.
6. Audit summary
The audit summary is the handoff document. Allianz says the human reviewer receives it before deciding whether to pay. A useful summary records what each agent checked, what evidence it used and where it was uncertain. Allianz also says its dashboards compare AI outputs with claim outcomes, which gives the insurer a way to see whether the agents’ recommendations hold up over time.
7. Routing and escalation
Routing sends each claim to the right place. A claim that appears eligible goes to a human payment decision. A potential rejection goes to an experienced handler, and an uncertain or complex case goes to a qualified expert. Allianz says potential rejections in the Australian example are escalated to experienced handlers. Routing is where much of the design risk sits, because a claim sent down the wrong path can be paid incorrectly or delayed.
Which claims suit agentic handling, and where people decide
Good first candidates
- High-volume, repetitive claims with low complexity. Project Nemo was designed for that profile, which Allianz describes as high-volume, low-complexity claims.
- Low claim values, where the cost of an error is limited and the number of claims is high.
- Evidence that can be checked against a structured source, such as a weather record or policy data.
- Coverage rules clear enough that an agent can apply them without judging the insured’s conduct.
Decisions that stay with people
- The final payment decision. In Project Nemo, a human receives the audit summary and decides whether to pay.
- Potential rejections. Allianz escalates these to experienced handlers.
- Uncertain or inconsistent evidence. Allianz’s German pet-insurance process routes uncertain cases to human experts.
- Complex losses and any adverse action. The public examples describe agents handling low-complexity claims. A sound design treats complex losses and unfavourable decisions as human decisions.
Project Nemo: what Allianz has said and reported
Allianz says Project Nemo launched in Australia in July 2025 to handle food-spoilage claims after weather-related power outages, typically below AUD 500. Its November 2025 announcement says the workflow was designed for high-volume, low-complexity claims and uses seven task-specific agents.
Why Allianz built it
Thomas Baach, Managing Director, Core Insurance Platforms at Allianz Technology, described the problem this way: “From a customer’s perspective, it’s a simple claim,” but it “could take four days or more to process as the focus of the claims teams was on more complex claims happening during the NatCat event.” The design aims to keep simple claims from waiting behind complex ones during a catastrophe surge.
Reported outcomes
Maria Janssen, Chief Transformation Officer at Allianz Services, said: “With ‘Project Nemo’ as our first integrated agentic AI solution, we’re achieving an impressive 80% reduction in claim processing and settlement time.” That is Allianz’s reported result. Allianz’s two public accounts describe the baseline differently, so the figures below should not be averaged or read as one trend.
| Figure | What it measures | Source | Status |
|---|---|---|---|
| 80% reduction | Claim processing and settlement time for Project Nemo | Allianz Services, 2025, as quoted by Maria Janssen | Company-reported result |
| From several days to one day or hours | Processing time for eligible claims | Allianz, November 2025 announcement | Company-reported; baseline described as “several days” |
| From around seven days to less than one day | Claims under AUD 500 | Allianz, March 18, 2026 responsible-AI account | Company-reported; baseline described as “around seven days” |
| Less than five minutes | Automated sequence up to final human review | Allianz, November 2025 | Description of one workflow, not a benchmark |
| 95% | Share of relevant Australian food-spoilage claims requesting AUD 500 or less | Allianz, March 18, 2026 | Describes claim mix, not performance |
The published figures measure speed and claim mix. They do not report accuracy, error rates or customer satisfaction for the workflow. Anyone assessing Project Nemo should ask for those measures before drawing conclusions about quality.
Other public examples: German pet claims and Duck Creek’s Agentic FNOL
Allianz German pet-insurance claims
Allianz reports that fully automated processing accounted for 49.7% of its German pet-insurance claims in 2025, and that simple everyday claims are paid within a few hours. The process reads uploaded documents with OCR, checks the extracted fields against policy data, and routes uncertain cases to human experts. Allianz describes this as AI claims processing, not as an agentic deployment, so it should not be counted as an example of agentic claims intelligence.
Duck Creek Agentic FNOL
On April 28, 2026, Duck Creek announced an insurance-native agentic AI platform with an Agentic FNOL application for first notice of loss. Its architecture description lists orchestration, guardrails, traceability, observability, compliance controls, cybersecurity and integration with core system data. Hardeep Gulati, Chief Executive Officer at Duck Creek, said: “Agentic AI will redefine how insurance operates—enabling carriers to move from manual, fragmented processes to orchestrated end-to-end decisioning and support for all personas to drive better outcomes and continuously improve.” That is a promotional statement. The announcement does not report independent results or broad customer deployment, so it describes what the product is designed to do rather than how it performs in use.
How widely AI is used in insurance operations
The NAIC summarizes survey results on its AI topic page, drawn from surveys released between 2022 and 2025. The figures below cover AI and machine learning in general. They do not measure agentic claims systems.
| Insurer type | Responding companies | Share using, planning to use or exploring AI/ML models in operations |
|---|---|---|
| Auto insurers | 193 | 88% |
| Home insurers | 194 | 70% |
| Life companies | 161 | 58% |
| Health insurers | 93 | 92% |
These numbers show broad exploration of AI in operations. They do not show how many insurers run agentic claims workflows.
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Governance: what regulators and insurers expect
NAIC model bulletin and evaluation tool
The NAIC says its Model Bulletin on the Use of Artificial Intelligence by Insurance Companies was adopted in December 2023. It states that decisions or actions made or supported by AI must comply with applicable insurance laws and regulations, and it describes governance expectations and the information regulators may request during examinations. A model bulletin is a template that individual state regulators decide whether to adopt, so the operative requirements depend on the state.
The NAIC AI Systems Evaluation Tool was being piloted by 12 states as of March 2026. The NAIC page anticipates adoption at the 2026 Fall National Meeting. That adoption is an expectation stated on the page rather than a completed step, so check the NAIC AI topic page for current status.
Insurer governance in practice
Allianz lists its AI governance principles as transparency, accountability and accuracy, security and resilience, non-discrimination, data privacy, data governance and human oversight. It says it registers AI use cases and assesses compliance, privacy, data quality, IT security and operational risks across their lifecycle. Philipp Raether, Chief Privacy & AI Trust Officer at Allianz, put the stakes this way: “AI will only deliver its promise if it strengthens that trust.”
What to test before adopting an agentic claims system
Cycle time is the number vendors and insurers lead with, but the public sources point to a longer test list. Use the items below as a procurement and go-live checklist.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- Integration. Confirm that the agents can read from and write to policy administration, claims, payment and external evidence systems, tested on live data flows rather than a demonstration dataset.
- Data quality and provenance. Establish where each input comes from, how current it is, and how an error in a weather feed or policy record would surface.
- Permission boundaries. Define what each agent can read, recommend, change or trigger. Decide explicitly whether any agent can initiate a payment and, if so, under what limits.
- Confidence thresholds and escalation. Specify which cases go to a handler, who can override an agent, and whether each override is logged.
- Audit trail and explainability. Every agent action should be traceable so that a reviewer or regulator can reconstruct how a claim was handled.
- Monitoring and incident response. Compare AI outputs with claim outcomes over time, as Allianz describes for its dashboards, and document how the team responds when outputs go wrong.
- Fairness, privacy and security. Test outcomes across customer groups, review data handling and vendor subprocessors, and confirm cybersecurity controls.
- Outcome measurement. Measure accuracy, cycle time, customer experience and total cost by claim complexity, against a baseline recorded before go-live and using a method that someone outside the vendor can review.
Duck Creek’s announced guardrails, traceability and observability features map onto several of these items. Each should be verified in a live deployment rather than accepted from the announcement.
Quick Recap
Sources
- Allianz SE, “When the storm clears, so should the claim queue,” November 3, 2025: https://www.allianz.com/en/mediacenter/news/articles/251103-when-the-storm-clears-so-should-the-claim-queue.html
- Allianz SE, “For People. For Pets. For Trust: Responsible Use of AI at Allianz,” March 18, 2026: https://www.allianz.com/en/mediacenter/news/articles/260318-responsible-use-of-ai-at-allianz.html
- National Association of Insurance Commissioners, “Insurance Topics: Artificial Intelligence”: https://content.naic.org/insurance-topics/artificial-intelligence
- Duck Creek Technologies, “Duck Creek Launches Insurance-Native Agentic AI Platform and Unveils New Applications to Transform Underwriting and Claims,” April 28, 2026: https://www.duckcreek.com/resource/press-releases/launch-agentic-ai-platform-underwriting-claims/
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