AI Fraud Detection for Roof Claims: A Playbook

North Carolina Farm Bureau didn’t wait for another spike in roof claims to “prove” a pattern everyone in claims already recognized. They helped set a trap.

In December 2025, investigators used a “bait house” sting to capture video of a roofer allegedly bending shingles and striking roof surfaces to make damage look like wind or hail—just enough to trigger an insurance claim. It’s a rare example of an insurer and a state department of insurance partnering to get the kind of evidence prosecutors actually need.

Here’s the problem: most carriers can’t run sting operations at scale. And most fraud doesn’t show up as a single obvious incident—it shows up as repeated, small distortions across claims, vendors, geographies, and time. That’s where AI in insurance earns its keep. Not by replacing adjusters, but by spotting patterns early, triaging smarter, and reducing the number of questionable roof claims that get paid “because it’s cheaper than fighting.”

What the NC “bait house” sting teaches insurers

The lesson is simple: fraud becomes prosecutable when you can prove intent and method. The sting worked because it combined three things that most day-to-day roof claims lack:

1. A controlled baseline (engineering experts inspected the roof before and after)
2. Direct behavioral evidence (video of alleged manipulation)
3. A repeatable narrative investigators can present in court

That trio matters because roof claims are uniquely hard to litigate. Shingle creases, granule loss, and small impacts can be ambiguous. When the dispute becomes “my engineer vs. your engineer,” carriers often settle or pay—especially if the policy language creates gray areas (for example, when vandalism coverage could apply).

The sting also highlights a second reality: policyholders are often victims, not accomplices. Door-to-door roof solicitation works because it offers a clean story:

“You can get a new roof with little or no out-of-pocket cost.”

When the homeowner believes the contractor is simply “finding storm damage,” they can’t serve as strong witnesses about what happened on the roof. That makes the case hinge on data and documentation, not recollection.

Why roof fraud scales so fast (and why winter matters)

Roofing fraud doesn’t need a catastrophe to spread, but catastrophe seasons make it easier. After major events—like the surge of wind-related roof claims many carriers saw in 2024 after Hurricane Helene—communities have:

• High legitimate demand (real repairs needed)
• Busy adjusters (triage pressure)
• Long repair cycles (delays create frustration)
• More contractor churn (new vendors flood the area)

By mid-December 2025, many carriers are planning for 2026 storm capacity and trying to reduce “avoidable” loss cost before the next season. Roof fraud is one of the most direct levers: it drives severity (full replacements instead of repairs), loss adjustment expense, and litigation/complaints.

There’s also a migration effect. When one state tightens rules—like Florida’s multi-year crackdown on assignment of benefits (AOB) practices—some aggressive operators simply look for friendlier terrain. North Carolina’s restrictions on AOB without carrier consent reduce one pathway for abuse, but they don’t stop door-to-door pressure or manufactured damage.

So, what does?

How AI detects roof claim fraud earlier than a sting can

A sting catches one incident. AI fraud detection catches the pattern that led to the incident—and helps you intervene before the claim is paid.

The most effective approach is not a single model. It’s a fraud analytics stack that combines claim signals, vendor signals, property signals, and human feedback.

1) Behavioral pattern detection across vendors and neighborhoods

The fastest way to identify “door knocker” behavior is to look for clusters:

• Unusual density of roof claims in a tight radius (same subdivision, same street)
• Claims that appear soon after a contractor begins advertising locally
• Repeated mentions of the same company name, phone number, or sales pitch in notes
• A sharp change in roof claim frequency after a storm that didn’t materially affect that area

AI models can flag these anomalies as network patterns, not single-claim red flags. That matters because each claim might look reasonable on its own.

2) Document intelligence that spots “too-perfect” estimates

Manufactured or exaggerated roof claims often come with documentation that reads like it was built to win a dispute:

• Exact claim totals stated early, before inspection
• Repeated line items across unrelated properties
• Templates that mirror public adjuster narratives (even when no adjuster is involved)
• Photo sets that are inconsistent in lighting/metadata or don’t match roof geometry

Natural language processing (NLP) can score estimates and communications for adjusting-like language and consistency issues. This supports a practical angle the NC Farm Bureau executive referenced: when contractors effectively “adjust” claims without a license, it creates a compliance and enforcement lever.

3) Computer vision for photo and aerial imagery consistency checks

Modern roof claim workflows increasingly rely on:

• Customer-submitted photos n- Adjuster photos
• Aerial imagery
• Third-party property data

Computer vision models can identify mismatches between claimed damage type and visual indicators, such as:

• Patterns consistent with mechanical creasing rather than wind uplift
• Impact distributions that don’t look like hail fields
• Re-used photos across claims (duplicate detection)
• Evidence that the photographed roof is not the insured structure

No model should “deny” a claim. But models are excellent at answering: which claims deserve a senior adjuster, an engineer, or an SIU consult.

4) Triage scoring that protects cycle time (and customer experience)

Fraud programs fail when they slow down legitimate claims.

A practical design is a two-lane approach:

• Fast lane: low-risk roof claims with consistent imagery, clean vendor history, and normal loss patterns
• Review lane: claims with elevated risk scores or suspicious vendor/network signals

This reduces blanket friction while still increasing the hit rate for SIU referrals.

A practical AI playbook for roof-claim fraud detection

If you’re building an AI program in claims or SIU, here’s what works in the real world—especially for roof claims where ambiguity is common.

Step 1: Build a “roof claim fraud feature set” (not a generic fraud model)

Generic fraud models miss roofing nuances. A roof-specific feature set should include:

• Claim timing vs. storm reports and local weather intensity
• Roof age, material type, and prior loss history
• Replacement vs. repair recommendations by region
• Contractor and subcontractor identifiers (names, phone, email, license)
• Litigation/complaint outcomes tied to vendors
• Text signals from adjuster notes and call transcripts

Opinion: If your model doesn’t incorporate vendor network behavior, it’s not going to catch door-knocker operations early.

Step 2: Stand up vendor intelligence and credential verification

Insurers often know more about contractor performance than they realize—they just don’t centralize it.

Start with a vendor “truth table”:

• License status and complaint history
• Prior claim involvement and severity lift
• Reinspection frequency and engineer utilization rates
• Cycle time impact
• Geographic spread (rapid multi-county expansion is a flag)

Then automate alerts when a vendor’s profile shifts.

Step 3: Use AI to create better evidence, not just better scores

NC’s sting worked because it produced proof. You can move in that direction without undercover operations by using AI to standardize documentation:

• Auto-create photo checklists for field adjusters based on roof type
• Require geo/time capture and consistency checks for customer photos
• Generate inspection narratives that explain why damage is consistent (or inconsistent) with the reported peril
• Maintain an audit-friendly trail of model inputs and outputs

This matters because if a questionable claim escalates, you need the file to be defensible.

Step 4: Close the loop with outcomes (the part most teams skip)

Fraud models drift unless you feed them outcomes:

• SIU referral results
• Denial/withdrawal reasons
• Recovery/subrogation amounts
• Vendor removals
• Complaint and DOI inquiry outcomes

Treat this as product work, not a one-time analytics project.

“People also ask” about AI roof claim fraud detection

Can AI detect manufactured roof damage?

AI can’t read intent, but it can detect statistical and visual inconsistency—patterns that strongly correlate with manufactured damage. The value is triage: sending the right claims to deeper review.

Will AI increase false positives and upset customers?

It will if it’s used as a blunt instrument. The better approach is low-friction escalation: more documentation, better inspections, and faster routing to specialists—while keeping low-risk claims moving.

What’s the fastest AI win for roof claims?

Start with vendor network analytics and document intelligence. They’re often quicker to implement than full computer vision pipelines and can surface door-knocker patterns early.

Where this goes in 2026: more enforcement, more automation

North Carolina officials have said more bait houses are likely in 2026. That’s a strong signal to the market: roof manipulation isn’t “sales”—it’s fraud.

But enforcement alone won’t keep up with volume. Many carriers handle hundreds of thousands of claims annually, and roof claims remain one of the easiest places for severity inflation to hide.

If you’re leading claims, SIU, underwriting, or analytics, the next step is straightforward: build an AI fraud detection workflow that flags suspicious roof claims early, documents evidence consistently, and protects cycle time for legitimate customers.

If your roof-claim fraud strategy still depends on adjusters recognizing the same story for the hundredth time, you’re paying for fraud with payroll.

Where could AI help your organization most right now—vendor intelligence, photo consistency, or claim triage?