AI Fraud Detection: Stop Insider Claims Scams Early

A seven-month run of fake insurance claims shouldn’t be able to slip through any modern carrier. Yet a recent Georgia case alleges exactly that: a former loss representative created false claims, routed payments through a fake towing business, and issued $146,167 in checks, producing an alleged $141,000 loss to the insurer. Investigators say more than 100 checks were cashed or deposited, and policyholders reportedly didn’t even know claims were filed in their names.

This isn’t “just fraud.” It’s process fraud—the kind that exploits routine workflows and trust. And it’s the exact reason fraud teams are shifting from after-the-fact investigations to AI-driven fraud detection that watches patterns in real time.

For insurers heading into 2026, the lesson is blunt: you can’t audit your way out of high-velocity claims fraud—especially when the risk is internal. You need systems that spot the signals as the scheme is unfolding.

What this case reveals about modern claims fraud

The core insight: the alleged scheme didn’t require sophisticated hacking. It required familiarity with claims operations and enough access to move money.

In the reported scenario, an adjuster (or loss representative) allegedly:

• Created claims that weren’t real
• Used a fake towing company as part of the narrative
• Issued a high volume of checks over time
• Benefited through deposits/cashing activity that appeared to connect to personal accounts

That’s not a one-off anomaly. It’s a repeatable playbook.

Why insider fraud is uniquely hard to catch

Insider claims fraud is painful because it looks “valid” at a glance. The employee knows:

• Which documentation normally gets accepted
• What payout thresholds avoid extra review
• How to phrase loss notes to reduce questions
• How to time activity so it blends into normal workload

Traditional controls help—segregation of duties, periodic audits, managerial review—but they’re usually sampling-based and slow. High-frequency schemes can do real damage before anyone pulls the thread.

The cost isn’t just the dollars

A $141,000 loss is tangible. The hidden costs are what usually sting more:

• Claim leakage that distorts loss ratios
• Operational drag from retroactive investigations
• Regulatory risk once patterns are identified
• Customer trust damage when policyholders learn a claim was filed in their name

That last one matters. If your customers think your claims operation can be used without their knowledge, your brand takes a hit no marketing campaign can easily fix.

The four red flags AI can catch faster than humans

AI isn’t magic. It’s pattern recognition at scale with consistent memory. That’s exactly what claims departments need when fraud is repetitive, distributed, and paperwork-heavy.

Here are four red flags in fake claims that AI fraud analytics can detect early—often within days, not months.

1) Unnatural payment velocity

Signal: unusually frequent payments from one handler, team, office, or vendor relationship.

Humans can miss velocity because each file “looks fine.” AI models don’t get tired and don’t rely on intuition—they measure baselines.

Practical triggers include:

• Sudden increase in checks issued per week by a specific employee
• Atypical “burst” patterns (many payments clustered near Fridays, month-end, or after hours)
• Payments that repeatedly land just under escalation thresholds

2) Vendor anomalies (including towing and storage)

Signal: vendors that behave unlike legitimate peers.

Towing and storage are common in auto claims and are frequently abused because:

• Charges vary widely
• Supporting docs can be thin
• There’s urgency to release vehicles

AI can flag vendors with:

• Repeated invoicing patterns that mirror one another too closely
• Unusual geography (vendor far from loss location)
• Lack of digital footprint consistency (addresses, phone numbers, tax IDs that change)

Even simple entity resolution—matching slightly different spellings of the same vendor—catches more than many teams expect.

3) Policyholder-behavior mismatch

Signal: the “customer” attached to the claim doesn’t behave like an involved claimant.

In the reported case, policyholders allegedly didn’t know claims existed. That creates detectable gaps:

• No inbound calls, portal logins, or email responses
• Contact attempts routed to unfamiliar numbers
• Repeated use of the same mailing address or banking destination across unrelated claims

When you combine claim file activity with customer interaction data, AI can spot claims that are operationally active but customer-silent.

4) Employee-linked networks and circular relationships

Signal: hidden relationships between employees, claimants, and vendors.

Modern fraud isn’t isolated; it’s networked. Graph analytics can reveal:

• Multiple claims tied to the same bank account or deposit destination
• Shared phone numbers or addresses across “unrelated” claimants
• Vendors consistently associated with one employee’s files

A simple but powerful rule of thumb:

If the same small cluster of entities keeps appearing together, it’s not randomness—it’s a pattern.

Where claims automation helps (and where it can backfire)

Automation is the amplifier. It speeds up good claims handling—and bad claims handling. That means automation without fraud intelligence increases exposure.

Automation should increase controls, not remove them

A healthy approach looks like this:

• Straight-through processing for low-risk claims
• Risk-based routing for anything outside norms
• Escalation rules that are dynamic (based on data), not static (based on a single dollar amount)

The reality? Many carriers still rely heavily on threshold-based review: “Over $X requires approval.” Fraudsters adapt in a week.

The 2026 stance I recommend: “trust, but score”

If you’re modernizing claims, build it on one principle:

Every claim gets a fraud score. Every payment gets a risk score.

Not every score triggers an investigation. But every score creates:

• Prioritized queues
• Consistent documentation of why a claim moved quickly
• Measurable oversight for regulators and auditors

This is especially relevant now, as regulators and lawmakers scrutinize claims decisions (including requirements for human involvement in denials). Insurers need explainable workflows that show how risk was evaluated, not just that it was.

A practical AI fraud detection blueprint for insurers

You don’t need a moonshot AI program to reduce exposure to insider skimming and fake claims. You need a tight feedback loop between data, models, and investigators.

Step 1: Instrument the claims lifecycle

Start capturing (and standardizing) events such as:

• Claim creation time, edits, reopenings
• Payment issuance events (amount, method, approver)
• Vendor additions and invoice uploads
• Contact attempts and customer responses

If you can’t measure it, you can’t detect drift.

Step 2: Combine rules + machine learning (don’t pick one)

Rules catch obvious abuse fast. Machine learning catches the “looks normal” fraud.

A workable stack:

1. Rules engine for known patterns (duplicate bank account, repeated vendor tax ID, etc.)
2. Anomaly detection to surface unusual velocity and behavior
3. Supervised models trained on confirmed fraud and non-fraud outcomes
4. Graph analytics for network connections

Step 3: Focus on three high-yield use cases first

If you’re choosing where to start, pick what typically pays back quickly:

• Payment integrity (duplicate payments, split payments, threshold gaming)
• Vendor risk scoring (towing, glass, remediation, medical, body shops)
• Insider risk monitoring (claims handled, override frequency, payment velocity)

Step 4: Close the loop with investigators

Models get better when investigation outcomes come back as structured data.

I’ve found teams struggle most with this part. The fix is operational, not technical:

• Require investigators to tag outcomes (fraud, not fraud, inconclusive)
• Capture the reason codes (vendor, identity, documents, collusion)
• Feed results back into model training and rule tuning monthly

Step 5: Make it explainable enough to defend

Fraud flags affect customers and employees. Your program must be defensible.

Good practice includes:

• Reason codes that are human-readable (“unusual payment frequency”)
• Model monitoring to prevent bias and drift
• Audit trails that show who reviewed what and when

Explainability isn’t a nice-to-have. It’s how you keep AI in claims from becoming a compliance headache.

People also ask: AI fraud detection in insurance

Can AI detect insurance fraud in real time?

Yes—if your claims platform emits events (payments, edits, vendor changes) and your fraud engine scores them continuously. Real time is mostly an integration and workflow problem.

What’s the difference between claims fraud and insider fraud?

Claims fraud usually involves external actors (claimants, vendors). Insider fraud involves employees abusing access to initiate, steer, or pay claims. Insider fraud often requires network analytics and behavior baselines to detect.

Will AI increase false positives and slow claims?

It can if it’s implemented as a blunt “stop everything” gate. The better approach is risk-based triage: most claims move fast, while a small percentage gets extra verification.

What to do next if you’re responsible for claims or SIU in 2026

The Georgia case is a reminder that the fraud you don’t see is the fraud that hurts you most—quiet, repeatable, and buried inside daily operations.

If you’re evaluating AI in insurance, start here:

1. Map your highest-risk payment flows (especially checks and manual overrides)
2. Baseline employee and vendor behavior so “unusual” has a clear definition
3. Implement fraud scoring at the payment level, not only the claim level
4. Add network analysis to uncover repeat relationships across claims

Fraudsters scale when insurers don’t. AI fraud detection is how you scale the other way—by putting consistent scrutiny on every claim event without slowing honest customers.

Where do you think your organization is most exposed right now: vendors, identity, or insiders?