AI Fraud Detection for Insider Claims Scams

Insider fraud doesn’t look like fraud—until you add up the checks.

A December 2025 case out of Georgia shows how quickly things can spiral when someone inside the claims operation knows the system well enough to fake reality: investigators allege a former loss representative created bogus claims and issued $146,167 in checks over seven months, tied to a fake towing company, with more than 100 checks cashed or deposited. Policyholders reportedly didn’t even know claims were filed in their names.

This matters because most fraud programs are built to catch the “classic” external fraudster. But insider abuse is a different animal: the paperwork is clean, the claim notes sound plausible, and approvals can happen faster because the person pushing the claim knows the rules.

In our AI in Insurance series, we’ve talked about automation, faster cycle times, and better customer experience. This post takes a harder stance: if you’re using AI to speed up claims but not to monitor for insider risk, you’re leaving the vault open while upgrading the door lock.

What this alleged scam tells us about modern claims fraud

The clearest lesson is that process knowledge is a force multiplier for fraud.

According to investigators, the alleged scheme depended on three simple ingredients:

• Synthetic claim creation (claims that appear valid in the system)
• A plausible vendor story (a towing company is an easy, familiar line item)
• Rapid, repeated payments (many checks, not one massive payout)

That combo is hard to spot with traditional controls because nothing is “technically” wrong in a single transaction. The risk emerges in the pattern.

Why “lots of small checks” can be worse than one big one

A single $50,000 payment triggers attention. One hundred smaller checks often won’t—especially in high-volume auto claims environments.

Fraudsters (external and internal) understand thresholds:

• Manual review thresholds
• Authority levels and approval limits
• Exception rules for “customer care” scenarios

In nonstandard auto lines and towing/roadside contexts, payments can be frequent and time-sensitive. That creates cover: “We needed to move fast.”

The hidden damage: customer trust and regulatory heat

When policyholders aren’t aware claims were filed in their names, the insurer inherits downstream fallout:

• Confused customers disputing claim history
• Potential premium impacts or underwriting flags
• Complaints that escalate quickly because the customer did nothing wrong
• Regulatory scrutiny around controls, not just restitution

Financial loss is painful. Reputational loss is expensive and slow to repair.

Why insider fraud is uniquely hard to catch (and why AI helps)

Insider claims fraud is hard to detect because the actor can make the file look “normal.” AI helps because it doesn’t rely on one red flag—it scores risk across many weak signals.

Traditional anti-fraud approaches do three things reasonably well:

1. Catch known fraud rings (repeat participants, known bad actors)
2. Flag obvious anomalies (impossible dates, duplicate invoices)
3. Support SIU after suspicion exists (investigation tooling)

But insider abuse often sits in the blind spot between “normal” and “provably wrong.”

The real gap: monitoring the behavior of claims handling

Most carriers analyze claimants and vendors. Fewer analyze employee behavior patterns with the same rigor.

AI models can monitor adjuster or loss rep activity in ways that are both measurable and auditable:

• Claims created per day/week vs. peer group
• Payment frequency and timing patterns
• Vendor selection concentration (same vendors repeatedly)
• Override and exception rates
• Claim note similarity across files (templated narratives)
• After-hours activity (creation, edits, approvals)

The goal isn’t to accuse people. It’s to surface outliers early so leaders can review.

What “AI fraud detection” actually means in claims ops

Here’s a plain-English definition you can share internally:

AI fraud detection in insurance claims is a system that assigns risk scores by learning patterns from historical outcomes and spotting anomalies across people, vendors, and transactions—not just single files.

In practice, strong programs combine:

• Rules (clear policy and compliance requirements)
• Machine learning (patterns too complex for rules)
• Network analytics (connections among claimants, vendors, addresses, phones)
• Human review (SIU/claims leadership decisions)

AI doesn’t replace SIU. It prioritizes SIU’s attention.

AI signals that could flag a “fake towing + fake claim” pattern

If you’re evaluating fraud analytics vendors or building internally, you want signals that match the way this alleged scheme operated.

Transaction-level signals (the “what”)

These indicators focus on payments and line items:

• Unusual volume of towing payments tied to a small set of vendors
• Multiple checks issued with similar amounts, descriptions, or timing
• Payments issued soon after claim creation with minimal documentation
• High ratio of “expense payments” (like towing/storage) compared to claim severity

Entity-level signals (the “who”)

These indicators focus on people and organizations involved:

• A vendor that is “new” but suddenly receives frequent payments
• Vendor bank/account attributes that resemble personal accounts (where legally observable)
• Shared addresses/phone numbers across vendor and claimant records
• Reuse of the same policyholders without their typical claim behavior

Workflow signals (the “how”)

This is where insider detection gets real:

• One employee repeatedly touching the same vendor and payment path
• Unusual edit patterns (e.g., frequent claim reopenings right before payments)
• Low documentation density combined with high payment velocity
• A spike in exceptions: overrides, manual approvals, bypassed steps

A strong system doesn’t need every signal. It needs enough weak signals together to raise a review flag.

How to add AI monitoring without turning claims into a surveillance culture

The most common failure mode I see is swinging from “we trust everyone” to “we trust no one.” That hurts morale and slows claims.

You can build effective internal monitoring while staying fair and transparent.

1) Start with governance: what you will and won’t use

Write it down. Socialize it. Enforce it.

Good governance policies include:

• Purpose limitation: fraud prevention, compliance, and customer protection
• Role-based access: who can see what, and why
• Auditability: every alert and action is logged
• Retention rules: how long you keep model outputs and investigation notes

This is also where you align with legal and HR. You want controls that stand up in court and in employee relations.

2) Use peer-group baselines, not raw counts

A catastrophe week can spike claim counts. A specialized desk will have different patterns.

Better models compare employees to their true peer group:

• Same line of business
• Similar geography
• Similar tenure and authority levels
• Similar claim mix (severity, coverage types)

This reduces false alarms and makes alerts easier to defend.

3) Design alerts for action, not curiosity

If your fraud dashboard is interesting but not operational, it becomes shelfware.

Every alert should answer:

• What behavior triggered it?
• How unusual is it (percentile vs peers)?
• What files/entities are involved?
• What’s the recommended next step (review docs, verify vendor, call insured)?

A practical standard: an analyst should be able to disposition an alert in under 15 minutes unless it escalates.

4) Build “verify the customer” into suspicious workflows

The Georgia case highlights a painful truth: customers may not even know a claim exists.

For certain risk thresholds, add a lightweight verification step:

• Outbound confirmation to the policyholder for first notice details
• Verification of vendor relationship (did you request this tow?)
• Confirmation of loss date/location

Done well, this protects customers and deters insider schemes.

What to ask when buying an AI fraud detection solution

If your goal is leads (and results), skip the glossy demos and ask questions that expose real capability.

Model performance and false positives

• How do you measure precision/recall in production, not just pilots?
• What’s the typical false positive rate by line (auto physical damage vs injury)?
• How often are models retrained, and what drift monitoring exists?

Insider risk coverage

• Do you explicitly model employee behavior signals and workflow events?
• Can you set up peer groups and authority-tier baselines?
• Can the system detect vendor concentration and adjuster–vendor coupling?

Data and integration reality

• What data is required (claims, payments, notes, vendor master, user logs)?
• How long does implementation take end-to-end (not best case)?
• Can it work with partial data while integrations mature?

Investigation workflow

• Can alerts be routed to SIU, compliance, or claims leadership separately?
• Does it support case management with evidence capture?
• Can it explain “why” an alert fired in plain language?

My opinion: if a vendor can’t explain alerts clearly, it’s not “advanced.” It’s risky.

A practical 30-day plan to reduce insider claims fraud risk

If you want momentum before the next budget cycle, here’s a realistic approach.

Days 1–10: Find your exposure

• Identify the top 10 expense categories prone to abuse (towing/storage is usually on the list)
• Pull 12 months of payment data and rank vendors by spend and by number of transactions
• Baseline employee payment activity (counts, timing, exception rates)

Days 11–20: Add controls where fraud hides

• Tighten vendor onboarding and validation (even basic steps help)
• Require documentation minimums for repeat vendors and repeat payees
• Add policyholder verification for high-risk patterns (not for every claim)

Days 21–30: Pilot AI-style detection quickly

Even without a full machine learning platform, you can start with “AI-shaped” monitoring:

• Outlier detection on payment frequency and vendor concentration
• Text similarity checks on claim notes (to catch templating)
• Network graphs to spot shared phones/addresses across entities

Then decide: build, buy, or partner.

Where this fits in the AI in Insurance roadmap

Fraud detection is the most defensible, near-term ROI use case for AI in insurance because it protects both loss ratio and customer trust.

The alleged Georgia scheme is a reminder that AI can’t just sit on the perimeter looking for shady claimants. It has to watch the process itself: who creates claims, who routes payments, which vendors appear, and how quickly money moves.

If you’re modernizing claims in 2026, here’s the stance I’d take: speed without monitoring is just faster leakage.

If you’re assessing AI fraud detection for auto claims—or you’re worried your internal controls won’t catch insider skimming—what part of your workflow feels most “trust-based” right now: vendor setup, payment approvals, or claim creation?