AI Fraud Detection for Insider Claims Scams

$146,167 in checks. More than 100 deposits. Seven months. And the policyholders whose names were used allegedly didn’t even know claims had been filed.

That’s the part of the recent Georgia case that should make every claims leader sit up straight. According to investigators, a former loss representative at National General allegedly created fake claims, routed payments through a fake towing company, and worked with his mother to cash or deposit the proceeds. The insurer’s reported loss: about $141,000.

This post is part of our AI in Insurance series, and I’m going to be blunt: most conversations about AI in claims focus on external fraud (staged accidents, inflated invoices, synthetic identities). But this story is a reminder that insider fraud—the kind that comes from someone with system access and process knowledge—can be even more expensive because it hides in plain sight.

What this case reveals about insider threats in claims

Insider claims fraud usually isn’t “one big theft.” It’s many small actions that look normal in isolation. That’s exactly why traditional controls miss it.

In the reported scheme, the alleged behavior wasn’t a single, dramatic system override. It was operational work that resembles legitimate claims handling:

• Creating claims records
• Issuing claim payments
• Using a vendor (here, allegedly a towing company)
• Processing many payments over time

Why insider fraud is hard to catch with manual reviews

Manual audits are periodic. Fraud is continuous.

Most carriers still rely on a combination of supervisor reviews, random audits, payment authority thresholds, and tip-based investigations. Those controls matter, but they’re built around a flawed assumption: if something is wrong, it will look obviously wrong.

The reality is the opposite. Insider fraud often looks like “just another claim” because the insider knows:

• Which claim types get paid quickly
• Which vendors are commonly used
• Which documentation is rarely requested
• How to keep amounts under escalation thresholds

A person can “thread the needle” for months. A machine can watch every needle.

The operational cost nobody budgets for

Even if the direct loss is $141,000, the total cost is larger:

• Investigation time (SIU, legal, HR)
• Regulatory reporting and reputational exposure
• Process slowdowns and retraining
• New controls that add friction to good adjusters

That last point is where many insurers get stuck: you tighten controls and your best people feel punished.

How AI-powered fraud detection flags the patterns humans miss

AI fraud detection is strongest when it focuses on patterns across time, people, and payments—not just individual claims.

A human reviewer can look at one claim and decide whether it’s suspicious. AI can look at every claim, every payment, every adjuster action, and every vendor relationship, then score what’s statistically abnormal.

Anomaly detection that’s tailor-made for insider skimming

The most practical approach for insider risk is anomaly detection. You don’t need a perfect definition of fraud upfront; you need a reliable way to surface behavior that doesn’t match the norm.

In a scenario like the reported Georgia case, an AI model could flag anomalies such as:

• Unusual check volume tied to a single handler over a short period
• Repeated payments to the same vendor (or vendor address/bank account) across unrelated policies
• Claims filed on behalf of policyholders with no prior loss history, especially clustered
• Payment patterns that avoid thresholds (e.g., many payments just under a review limit)
• Timing anomalies (claims created and paid faster than peer averages)

One “weird” event is noise. Ten correlated weird events is a signal.

Entity resolution: the quiet superpower for vendor and bank fraud

A common failure point in claims systems is messy identity data:

• Vendor names entered slightly differently
• Multiple addresses for the same entity
• Shared phone numbers or bank accounts across “different” businesses

AI techniques like entity resolution connect the dots by estimating whether two records represent the same real-world entity.

That matters for fake vendor schemes because the “vendor” is often just a thin wrapper around a real bank account. When you can link:

• Vendor → mailing address → phone number → bank account → depositor

…you stop treating each payment as independent.

Behavioral analytics: watching the workflow, not just the outcome

Insider fraud isn’t only about where money went; it’s about how the work was performed.

Behavioral analytics can look at system interaction patterns such as:

• High frequency of claim creation + payment issuance by the same user
• Repeated use of override functions
• Editing loss details after approvals
• Creating claims outside normal working hours or from unusual locations

This isn’t about “spying.” It’s about the same principle banks use for account takeover detection: workflow behavior predicts risk.

Practical controls: where AI fits (and where humans still matter)

AI shouldn’t be the judge and jury. It should be the early warning system.

The best claims organizations treat AI fraud detection as a triage layer:

1. AI scores risk based on claims, payments, vendor data, and user behavior
2. Rules handle the obvious hard stops (duplicate bank accounts, sanctioned entities, etc.)
3. SIU/claims leadership reviews a short list of the highest-risk cases
4. Confirmed outcomes feed back into model improvement

A “three lines of defense” model for claims fraud

Here’s a simple structure that works in the real world:

• Line 1: Claims operations

  • Uses AI risk scores embedded in their daily tools
  • Gets prompts like “Vendor account matches 3 other vendors” or “Handler’s payment velocity is 4× peer median”

• Line 2: SIU and analytics

  • Investigates the top slice of alerts
  • Tunes thresholds so you don’t drown adjusters in false positives

• Line 3: Audit/compliance

  • Tests whether controls are working
  • Validates fairness, privacy, and governance practices

That structure keeps AI useful without turning it into a distraction.

What “good” looks like: fewer alerts, better investigations

If your AI initiative produces thousands of alerts a week, it’s not helping. It’s outsourcing the hard part to humans.

A strong program aims for:

• A manageable alert volume (think dozens per investigator per week, not hundreds per adjuster per day)
• High-quality explanations (“why this was flagged”) so people can act quickly
• Clear outcomes tracking (confirmed fraud, false positive, monitoring only)

A fraud model that can’t explain itself doesn’t reduce losses—it just creates meetings.

Implementation blueprint: getting value in 90 days

You don’t need a multi-year core replacement to reduce insider claims fraud. You need a focused data pipeline and a clear operating model.

Here’s a practical 90-day path I’ve seen work.

Days 1–30: Start with the data that already exists

Prioritize these data sources:

• Claims header data (policy, loss type, dates, adjuster)
• Payments ledger (amounts, dates, method, payee, approvals)
• Vendor master (addresses, tax IDs where available, bank info)
• User activity logs (key actions: create claim, change payee, approve payment)

Also define what counts as an “event” worth scoring (e.g., issuing a payment, adding a vendor, changing bank details).

Days 31–60: Build risk signals before fancy models

Many carriers get fast wins with engineered signals like:

• Payment velocity per handler (daily/weekly)
• Concentration risk (top vendors by handler)
• Peer group comparison (same team, same region, same claim type)
• Reuse of bank accounts across vendors
• Claims filed without typical corroboration steps

These signals can feed anomaly detection models or even an initial scoring system.

Days 61–90: Operationalize—alerts, routing, and feedback

This is where most AI projects fail. The model is fine; the workflow is messy.

Set up:

• A single queue for fraud-risk alerts
• Clear ownership (SIU vs. claims leadership)
• A short list of actions per alert:
  1. monitor
  2. request documentation
  3. pause payment
  4. open investigation

And crucially: record outcomes so the system learns.

Common objections (and the straight answers)

“Won’t this slow down claims?”

If you apply it to every claim, yes. Don’t do that.

Use AI to narrow the surface area: score everything quietly, but only intervene on the riskiest slice. Most customers never notice. Your cycle time stays intact.

“What about false positives?”

False positives are a tuning problem, not a reason to avoid AI.

The fix is to:

• compare people to true peer groups (same role, same claim types)
• require multiple signals before escalating
• give investigators a fast way to close an alert with a reason

“Isn’t insider fraud mostly a HR issue?”

It becomes HR after the money leaves.

Before that, it’s a controls and monitoring issue. AI helps you spot patterns early enough to prevent losses rather than document them.

The stance I’m taking: AI is now basic infrastructure for claims integrity

This Georgia case—fake claims, a fake towing company, and a long trail of checks—reads like a story from an older era of claims controls. But it happened recently, and that’s the point.

Insurers are heading into 2026 with pressure on loss costs, tighter underwriting, and rising expectations for fast digital claims. That combination creates a risky incentive: speed without oversight. AI-powered fraud detection is one of the few tools that can increase oversight without grinding the whole operation to a halt.

If you’re responsible for claims performance, SIU results, or compliance, the next step is practical: map the payment workflow, identify the highest-risk points (vendor creation, payee changes, check issuance), and decide where AI scoring should trigger review.

The forward-looking question I’d ask your team before Q1 planning is simple: If an insider ran a seven-month scheme in your claims operation, what signal would catch it first—and how quickly would you act?