Hidden Chemical Risks: A Wake-Up Call for Insurers

A single set of firefighter turnout gear can cost thousands of dollars—and it’s built to keep people alive in the worst conditions imaginable. Yet a new U.S. study found that some gear marketed as PFAS-free still contains brominated flame retardants across multiple layers, sometimes at higher “extractable” levels than the PFAS chemicals they were meant to replace.

That’s not just a public safety story. It’s a risk management story.

Because the pattern is familiar: you remove one known hazard, swap in a new material, and later discover the replacement brought a different exposure. Insurance organizations see the same thing when a “fix” in underwriting, claims, or compliance quietly introduces a new failure mode—often buried in data, vendor processes, or model assumptions.

This post sits in our AI in Government & Public Sector series, where the real question isn’t “Should we modernize?” It’s “How do we spot hidden risk early—before it becomes a safety issue, a regulatory issue, or a loss issue?” Firefighter gear gives us a sharp, timely metaphor for how AI risk detection can (and should) work in insurance.

What the firefighter gear study actually found—and why it matters

The clearest takeaway: “PFAS-free” doesn’t automatically mean “chemical-risk-free.” Duke University researchers tested multiple layers of turnout gear and found brominated flame retardants were present in every set evaluated, including newer gear marketed as non-PFAS-treated.

The study design (the part risk leaders should care about)

The team analyzed 12 sets of used turnout gear:

• Nine sets manufactured between 2013 and 2020
• Three sets manufactured in 2024, marketed as non-PFAS-treated

They tested the three main layers of turnout gear:

1. Outer shell (flame-resistant)
2. Moisture barrier (blocks liquids/pathogens while allowing airflow)
3. Thermal liner (helps regulate heat)

Most risk discussions stop at “is it present?” This study went further by measuring extractable levels—the portion more likely to transfer during use, which is a practical proxy for exposure potential.

The surprise: higher extractable brominated flame retardants in PFAS-free gear

For the older gear (2013–2020), PFAS was detected across the board. For 2024 gear, extractable PFAS was low or non-detectable, consistent with manufacturer claims.

But the headline is what replaced it: extractable brominated flame retardants were generally higher than PFAS, and the highest extractable concentrations showed up in PFAS-free gear, particularly in the moisture barrier. The chemical DBDPE (decabromodiphenyl ethane) appeared at the highest extractable levels.

A practical risk lesson: when you force a substitution—by regulation, procurement rules, or public pressure—you often get “regrettable replacement” risk unless transparency and testing keep pace.

For public sector leaders (fire departments, procurement teams, regulators) and for insurers covering municipal risk, workers’ comp, liability, and occupational disease exposures, this is the kind of emerging issue that can turn into multi-year claims patterns.

Hidden exposures are the norm—insurance just pretends they’re rare

Insurance processes are full of “turnout gear layers”—places risk can hide even when the surface looks compliant.

Here’s the parallel I see most often: a process gets labeled safe because it satisfies a single requirement, while the real exposure shifts to a layer nobody is testing.

Regrettable substitutions show up in insurance operations too

Examples that mirror the PFAS → brominated flame retardant story:

• Fraud controls that reduce one fraud type but increase false positives, driving complaints, bad faith risk, and regulator scrutiny.
• Faster claims automation that improves cycle time but quietly increases leakage on complex injuries because edge cases are misrouted.
• New vendor data sources that improve underwriting lift but introduce consent gaps, prohibited attributes, or documentation shortfalls.
• Model governance “checkboxing” that meets policy language while leaving drift, bias, and stability unmonitored in production.

The reality? Most companies don’t struggle to identify obvious risk. They struggle to identify substituted risk—the risk that appears because a change was made for the right reasons.

That’s where AI can help, provided it’s deployed like a safety program, not like a productivity toy.

How AI can detect “chemical-like” hidden risk in underwriting and claims

The answer: AI is good at surfacing patterns humans don’t see across time, vendors, and portfolios—especially when the signal is weak at first. But you have to aim it at the right questions.

1) Underwriting: find exposure where disclosures are incomplete

Firefighter gear treatments aren’t fully disclosed; insurers face similar opacity with supply chains, subcontractors, and public entity operations.

AI can support underwriting by:

• Entity resolution to connect vendors, facilities, subsidiaries, and prior loss records that sit in different systems.
• Document intelligence to extract exclusions, endorsements, and safety controls from submissions, inspection reports, and contracts.
• Portfolio anomaly detection to flag municipal or occupational segments where loss ratios shift after a policy change, procurement shift, or vendor change.

One concrete use case for public sector books: use AI to identify departments or municipalities transitioning gear or equipment categories (or other safety-critical supplies) and proactively adjust underwriting questions, loss control recommendations, and pricing assumptions.

2) Claims: detect emerging patterns before they become “known issues”

Occupational disease and exposure claims rarely arrive with neat labels. They show up as coded injuries, free-text notes, medical billing patterns, attorney involvement, and long tails.

AI can help by:

• Clustering claims narratives (adjuster notes, nurse case notes) to surface recurring exposure themes.
• Early-warning indicators that correlate with future severity (treatment types, comorbidity patterns, jurisdiction signals).
• Triage models that route potential exposure-related claims to senior adjusters earlier—before reserve errors and delayed care inflate costs.

This matters for firefighters specifically because exposure concerns (PFAS, flame retardants, combustion byproducts) sit alongside statutory presumptions and evolving standards—exactly the kind of environment where “lagging indicator” management fails.

3) Compliance and governance: watch the “moisture barrier” layers

The gear study measured extractable chemicals—what can transfer during use. In insurance, the equivalent is: what can transfer into decisions.

AI governance should explicitly track:

• Training data provenance (where it came from, what consent covers)
• Feature review for proxy variables (attributes that act like prohibited factors)
• Model drift and decision drift (the model might be stable while human workflows change)
• Vendor model oversight (the “chemical recipe” problem—black-box inputs with limited disclosure)

If you only test for the one risk everyone is talking about (today’s PFAS), you miss the substitution (tomorrow’s brominated flame retardant).

A practical playbook for public entities and insurers (next 60–90 days)

The fastest wins come from treating risk detection like a continuous inspection program.

For public sector risk leaders (fire departments, municipalities)

1. Procurement requirements: Ask for explicit disclosure of chemical treatments and testing summaries across all gear layers.
2. Inventory mapping: Track which stations and roles are using which manufacturing years and models of gear.
3. Cleaning and storage SOPs: Reduce contamination build-up from smoke/soot, because older gear may accumulate additional flame retardants from fire environments.
4. Exposure documentation: Standardize incident reporting fields that help future occupational health tracking.

For insurers (underwriting, claims, risk engineering)

1. Add “substitution risk” questions to public entity underwriting: when did the insured change gear/equipment vendors? what safety standard drove the change? what documentation exists?
2. Build an early-warning dashboard that monitors claim clusters for exposure keywords and medical billing patterns tied to thyroid, endocrine, and cancer-related workups.
3. Deploy AI to read submissions and loss control reports and flag missing documentation (the “ingredient disclosure” gap).
4. Strengthen model governance to prevent operational substitutions: new data feeds, new triage rules, or new automation steps should trigger monitoring, not just approvals.

If you’re trying to generate leads in the AI in insurance space, this is also the right message to bring to prospects: AI isn’t the product. Earlier detection is the product.

People also ask: what does this mean for insurance risk and coverage?

Does “PFAS-free” reduce liability risk?

It can reduce one category of concern, especially where regulation or procurement bans focus on PFAS. But the study’s implication is blunt: removing PFAS may increase reliance on other treatments, and liability risk depends on what replaces it and how exposure is managed.

Will this change claims patterns immediately?

Not immediately. Exposure-driven claims are often long-tailed. The operational impact shows up earlier in risk engineering, municipal procurement scrutiny, media attention, and underwriting diligence expectations.

What’s the AI opportunity that isn’t just “automation”?

The opportunity is horizon scanning inside your own book—finding weak signals (small clusters, odd shifts, new documentation gaps) before they become established loss drivers.

Where this fits in the “AI in Government & Public Sector” story

Government and public safety organizations are being asked to modernize while staying accountable: transparent procurement, measurable safety outcomes, and defensible decisions. That’s exactly why AI adoption in the public sector has to be tied to risk sensing and oversight, not just service speed.

Firefighter turnout gear is a vivid reminder that compliance labels can lull smart teams into overconfidence. Hidden exposures don’t announce themselves; they accumulate quietly in layers.

If you’re responsible for municipal risk, public safety coverage, or claims operations, here’s the next step worth taking: audit where your organization relies on “safe” labels—PFAS-free equivalents in data, vendors, or models—and put AI monitoring where substitutions are most likely to occur.

What risk in your process would you only discover five years from now—unless you build an early-warning system today?