Agentic AI in Insurance: Claims, Underwriting, and Fraud

Allianz built Project Nemo, seven coordinated AI agents handling food spoilage claims end to end, in under 100 days, and cut processing time by 80%, according to Allianz's own announcement.

A human claims professional still makes every final payout decision. That single design choice is the clearest signal yet of how production insurance AI is actually being built: fast, narrow, and never fully unsupervised.

This guide is part of Tecla's Agentic AI Use Cases series, covering agentic AI for insurance specifically: the claims workflows, underwriting patterns, and fraud processes that have moved past the pilot stage.

What Is Agentic AI?

Agentic AI refers to goal-oriented systems that plan, execute, and adapt multi-step tasks with minimal human oversight, distinct from traditional automation or generative AI built to answer a single prompt.

Three capabilities define it: autonomy, deciding what actions to take and carrying them out; adaptability, adjusting the plan as new information arrives; and coordination, working across tools and systems to finish what no single tool could handle alone.

In insurance, that difference shows up in how a claim gets handled. A fixed rule auto-approves anything under a set dollar amount, regardless of context.

An agentic system reads the coverage terms, the evidence, and the claim history together, and adjusts its handling to the specific case.

How Agentic Systems Work in Insurance

Insurance automated in stages, first through rules engines applying fixed underwriting criteria, then through straight-through processing for the simplest, most predictable claims.

That rules-based approach follows a fixed formula: if the claim amount is under this threshold and this box is checked, approve automatically. It works for the cleanest cases and breaks the moment a claim has any real complexity.

Agentic AI operates differently: it reasons across coverage terms, evidence, and history instead of a fixed checklist. It reads what's actually in front of it and adjusts its handling case by case, the way an experienced adjuster would.

The trade-off is real and worth naming directly: rules-based processing is more predictable and cheaper to audit; agentic systems are more adaptive and more expensive to run per case.

Most production deployments in 2026 are not one or the other.

They use straight-through processing for the simplest, most predictable claims, agentic reasoning for everything with real judgment involved, and a human gate in front of any actual payout or underwriting decision.

Agentic AI Use Cases in Insurance

Insurers don't adopt agentic AI as one system. They adopt it function by function, starting wherever the volume problem or the surge risk is worst.

Nine of those starting points are documented below, each at working depth. The architecture above stays abstract until it's tied to an actual trigger and an actual system.

What follows is a working set of agentic AI insurance use cases that have moved past a pilot and into daily use, not just what's technically possible on paper.

Claims Triage and First Notice of Loss

The first hour after a claim is filed sets the tone for everything after it: whether the right information gets captured, and whether a simple case gets routed as simply as it deserves.

Underwriting Support and Risk Assessment

An underwriter evaluating a new application pulls context from several places: property records, prior claims, third-party risk data, all before making a single pricing decision.

An agent that assembles that context automatically and flags the specific risk factors driving a recommendation gives an underwriter a documented starting point instead of a research task before the actual underwriting even begins.

Fraud Detection

Fraud rarely announces itself in a single claim. It shows up as a pattern across claims, an overlap in details, a history that doesn't quite add up when checked against other cases.

An agent correlating a claim against a policyholder's history and known fraud patterns can flag the specific inconsistency worth investigating, rather than a blanket suspicion score with no explanation behind it.

Damage Assessment and Estimation

Estimating repair cost from a description alone leaves too much room for a lowball or an inflated number, and scheduling an in-person inspection for every claim doesn't scale.

An agent reasoning over submitted photos and documentation can produce a reasonable estimate directly, reserving in-person inspection for the claims where the numbers genuinely don't line up.

Policy Servicing and Endorsements

Adding a driver, updating an address, adjusting coverage limits: these are routine changes that shouldn't require a person to manually re-key the same policy record every time.

An agent that processes routine endorsements directly and flags anything that changes the actual risk profile keeps simple updates simple, without treating every request as identical.

Subrogation and Recovery

Recovering costs from an at-fault third party requires piecing together liability evidence across multiple claims and parties, work that's genuinely time-consuming to do by hand.

An agent that identifies subrogation opportunities early and assembles the supporting evidence can materially improve recovery rates, simply by catching cases before the window to pursue them closes.

Catastrophe Response and Claims Surge Management

A single storm can generate more claims in a week than a normal month, exactly the kind of surge that overwhelms a fixed adjuster headcount no matter how well-staffed it is.

Allianz built Project Nemo specifically for this pressure: clearing simple, low-value claims fast during a catastrophe event so staff can focus on the complex cases the surge also brings.

Renewal and Retention

A policyholder who's about to churn usually shows signs before the renewal notice goes out: a rate shock, a service complaint, a competitor's ad they clicked on.

An agent correlating those signals can flag an at-risk renewal to a retention team while there's still time to act, instead of finding out only when the policy actually lapses.

Agent Identity and Authentication for Insurance Systems

An agent with standing access to policy, claims, and payment systems is a different risk than an adjuster with the same access, because it acts continuously and at a volume no one would notice drifting until an audit catches it.

Implementation: Guardrails and Governance

More than 25 states and the District of Columbia had adopted the NAIC's Model Bulletin on insurers' use of AI systems by mid-2026, with a formalized AI Systems Program now expected as standard practice for AI in insurance industry operations.

That program requires board and senior-management accountability, documented risk controls, model testing for bias, and oversight that extends to third-party AI vendors, not just systems built in-house.

The Team Behind Production Agentic AI

Allianz shipped Project Nemo in under 100 days by scoping it narrowly on purpose: one claim type, one clear rule for when a human has to step in. That discipline, not the model, is what made it production-ready.

Tecla's Agentic AI services design, build, and operate these workflows directly, the same claims, underwriting, and fraud detection systems above, running in your stack with the evals and guardrails production requires.

Or bring the expertise in-house: AI engineers who've worked on live insurance systems, past the demo stage.

Tecla runs a network of senior engineers across the US and Latin America, built over more than a decade, with a top 3% acceptance rate and first candidates in 3 to 5 business days.

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