Every insurer faces the same paradox: Your claims teams are highly qualified, yet your Combined Operating Ratio (COR) keeps taking hits from avoidable errors.

We just got back from London, and the mood has shifted. Two years ago, the conversation was about "disruption." This year, it was about mechanics.

A special thanks to ITC London 2026 for bringing together insurers, technologists, and operators who are focused on solving real workflow failures, not just showcasing tools. The event reinforced why practical, governed AI is now a board-level priority for claims leaders.

During our tech talk on Built-In Intelligence, we addressed a hard truth: Insurers aren’t bleeding money because their people are incompetent. They are bleeding money because their people are trapped in workflows that actively work against them.

This conclusion reflects patterns we see repeatedly when reviewing FNOL data, adjuster rework rates, and claim file audits across carriers. It starts with "poor-quality inputs" at FNOL, and it ends with cognitive overload on the adjuster’s desk.

Here is exactly where that value leaks and why exactly Built-In Intelligence is the only way to plug it.

Part 1: Leakage Begins at the Source (The Broken FNOL)

Before a claim even reaches an adjuster, the damage is often already done.

In our presentation, we shared some stark industry realities about the state of First Notice of Loss (FNOL). If your intake process is purely a "digital form" without intelligence, you are letting chaos into your system.

Across internal FNOL audits and carrier reviews we’ve participated in, intake quality remains one of the strongest predictors of downstream leakage.

The Stats of Broken Intake:

• 10% of claims arrive with missing or incomplete information.

• 10% contain the wrong documents entirely.

• 5% involve fundamental coverage misunderstandings by the customer.

This isn't just an administrative headache; it is a leakage multiplier.

When a claim arrives with "gaps” in the initial information, your expensive senior adjusters stop being risk experts and become data chasers. They spend their time emailing customers for missing photos or explaining basic coverage terms. This rework creates fatigue and excessive back-and-forth communication, which can cause them to overlook more significant issues later.

The Solution: The Claims LLM Agent 

We fix this by moving intelligence to the front door. Instead of a static form, an “AI Claim Agent” handles intake.

• Real-time Validation: Validates fields and documents instantly.

• Coverage-aware guidance: It flags that a document is blurry or incorrect before the submission.

• Smart Prompts: It notices if a field is vague and asks the customer to clarify immediately.

• Result: The claim arrives on the adjuster's desk clean, complete, and ready for decision.

Part 2: The "Ctrl+F" Problem (The Adjuster's Trap)

Once the claim lands on the desk, the second wave of leakage happens.

Even with better intake, the sheer volume of unstructured data (medical reports, police records, email chains) creates a "Knowledge Trap."

The Data Hunt

Your handlers are drowning in 50-page PDFs. To find a subrogation opportunity, a handler often has to manually Ctrl+F through a document stack.

• The Reality: If they have 15 files to review before lunch, they will miss that one sentence on page 42 that mentions a third party was liable.

• The Cost: You pay a claim that another carrier should have covered.

The Inconsistency Tax

When knowledge lives in people’s heads, expenditure becomes variable.

• Scenario: Two senior adjusters look at the same complex claim.

• Result: One catches the exclusion clause; the other misses it because they were rushing.

• The Issue: This variability is invisible on a dashboard until your quarterly results show a spike in COR.

(Note: If you are looking to fix an inconsistency in risk selection rather than claims, read our breakdown of Smarter Underwriting Strategies).

How Built-In Intelligence Stops the Bleeding

This is the shift we discussed at ITC London: Large Language Models (LLMs) have solved what traditional automation couldn’t, understanding context.

We’re not talking about “Robo-Adjusters” making final payout decisions. We’re talking about an orchestration layer that supports human experts.

The New Workflow

1. At Intake: The Ai Claim Agent ensures the data is clean before it enters the system.

2. At Ingestion: The system reads every document (handwritten notes, PDFs, emails) instantly.

3. At Review: It flags the subrogation opportunity or the exclusion clause and presents it to the human handler.

The result? The human stops hunting for data and starts making decisions based on all the facts. Human reviewers remain accountable for all final determinations.

tigerlab: The Orchestration Layer

Implementing this requires more than just an API key. It requires governance. This is where tigerlab fits.

We act as the Orchestration Layer for insurance operations.

• We Guardrail: We ensure the AI checks against your specific guidelines, not the general internet.

• We Verify: Our "tiger Suite" ensures humans remain in the loop to validate the AI's findings.

• We Integrate: We sit between your legacy PAS and the new intelligence, preventing the "black box" problem.

The Bottom Line

You don't need "smarter" employees. You need to stop forcing your expensive experts to fix broken intake forms and do cheap administrative hunting.

When you fix the intake (FNOL) and remove the friction of finding information, you restore consistency to your claims process. And when you restore consistency, you plug the leak.

See how tigerlab connects legacy claims systems with intelligent workflows to reduce leakage and improve consistency. Learn more about our platform here.