AI Fraud Detection: Stop Insider Claims Scams Fast

A seven-month run of fake claims, more than 100 checks, and roughly $141,000 in alleged insurer losses—all tied to an insider in claims—reads like a fraud “how-to” that nobody asked for. Yet that’s exactly why it’s useful. Insider misconduct exposes the weak spots in claims operations: trust-based workflows, fragmented oversight, and monitoring that’s great at catching external fraud but slow to spot fraud coming from behind the firewall.

For insurers, TPAs, and claims leaders, this isn’t a niche risk. It’s a reminder that fraud prevention isn’t just about suspicious policyholders. It’s also about what happens when an employee (or contractor) can create claims, route payments, and count on the fact that exceptions won’t be reviewed until the money’s gone.

This post is part of our AI in Insurance series, and I’m taking a clear stance: insider claims fraud is one of the most practical, high-ROI places to apply AI in insurance—right now—because the signals are already in your data.

What this alleged scam reveals about claims blind spots

Answer first: The alleged scheme worked because claims systems often prioritize speed and customer experience over robust internal anomaly detection.

According to reporting, investigators allege a former loss representative created fake insurance claims and issued checks totaling about $146,167 over seven months, tied to a fake towing company and deposited/cashed across 100+ checks. Policyholders reportedly weren’t aware claims had been filed in their names.

This pattern highlights three uncomfortable realities about many claims environments:

1. “Trusted user” risk is real. If a user’s role allows claim creation, vendor involvement, and payment release (even with nominal approvals), you’ve got a separation-of-duties problem.
2. Controls tend to be point-in-time, not continuous. Many carriers rely on post-payment audits, periodic sampling, or manual QA—great for compliance, bad for stopping fast-moving fraud.
3. Vendor fraud and insider fraud often blend together. Fake service providers (towing, glass, remediation, medical) are common in claims fraud. When an employee is feeding that vendor pipeline, the scheme becomes harder to spot with traditional “policyholder-centric” detection.

If your fraud program is mostly tuned for claimants exaggerating damage, you’re fighting the last war.

Why “more approvals” won’t solve it

Answer first: Adding manual approvals usually shifts fraud, it doesn’t stop it—and it slows legitimate claims.

The knee-jerk response to insider skimming is extra sign-offs. I’ve seen this backfire: adjusters learn what gets escalated, avoid those triggers, and keep amounts just under thresholds.

The better approach is behavioral monitoring and network-level anomaly detection—the kind of work AI is good at—so you can spot unusual patterns even when each individual transaction looks “reasonable.”

How AI catches fraud that humans miss (especially from insiders)

Answer first: AI detects insider claims fraud by correlating weak signals across systems—payments, vendors, user behavior, and claim text—then flagging patterns that don’t match normal operations.

Traditional fraud rules tend to be “if-then” checks:

• If payment > X, require approval
• If claim filed within Y days of policy start, review
• If vendor appears on watchlist, hold

Rules still matter. But insider fraud often succeeds because it stays inside the rules.

Here’s what modern AI fraud detection in insurance can add:

1) Anomaly detection on payments and check activity

Answer first: AI can flag payment behavior that’s statistically abnormal for a specific adjuster, team, region, or vendor type.

In a case involving 100+ checks, an AI model can look for patterns like:

• Unusual volume of checks per week per adjuster
• Repeated payments clustered near “no-review” thresholds
• High rate of reissued checks, manual overrides, or expedited payments
• Payments to new vendors without prior history

This isn’t magic. It’s math plus context—comparing someone’s activity to their peers and to their own baseline.

2) Entity resolution: connecting “different” vendors that are really one

Answer first: AI can link vendors and payees that appear unrelated by matching addresses, phone numbers, bank accounts, and naming patterns.

Fraudsters rarely reuse exact identities forever. They rotate:

• Slightly different company names
• New PO boxes
• Different deposit accounts

AI-supported entity resolution can detect that “QuickTow Solutions LLC” and “Quick Tow Services Inc.” share the same phone number, mailing address, or bank routing patterns—then raise risk scores automatically.

3) Network analysis: spotting collusion through relationship graphs

Answer first: Graph analytics identify suspicious clusters—adjusters, claimants, vendors, and payment methods that are unusually interconnected.

A fake towing company isn’t just a vendor record; it’s a node in a network. When a single adjuster repeatedly routes payments to a small vendor cluster (especially new vendors), the network shape changes.

Graph signals that often correlate with organized or insider-driven fraud:

• One employee linked to a high share of payments to a small set of vendors
• Multiple “unrelated” claimants linked to the same vendor + same payment method
• Vendor appearing across geographies that don’t make operational sense

Humans can’t eyeball these graphs at scale. AI can.

4) Behavioral analytics on internal users (with guardrails)

Answer first: User behavior analytics can detect suspicious internal activity patterns while still respecting privacy and compliance boundaries.

Examples of risk signals:

• Logging in outside normal hours followed by payment activity
• High rate of edits to payee details right before payment
• Frequent access to claims that later become high-loss or reversed
• Unusual patterns of “note writing” or copy-paste narratives

This is where insurers need maturity: the goal isn’t workplace surveillance. It’s risk monitoring tied to financial controls, with clear governance.

A practical AI playbook to prevent insider claims fraud

Answer first: The best results come from combining AI scoring with targeted controls—hold-and-review workflows, vendor onboarding gates, and airtight audit trails.

If you’re trying to turn this into an internal action plan for Q1 2026, here’s what works.

Step 1: Define “high-risk claim events” and score them

Start with events that are easy to log and hard to justify when they spike:

• New vendor created + first payment within 7 days
• Payment method changes (check to ACH, payee updates)
• Manual overrides of standard estimating rules
• Multiple payments on the same claim within short windows
• Claim filed + processed unusually fast by a specific user

Then apply an AI risk score that blends:

• Payment anomalies
• Vendor anomalies
• Text/narrative anomalies (basic NLP)
• Network/graph risk

Step 2: Put AI in the workflow, not in a dashboard

A dashboard is nice; it doesn’t stop fraud.

Operationalize with tiered friction:

• Low risk: straight-through processing
• Medium risk: additional documentation requirement
• High risk: automatic hold + SIU queue + manager attestation

Done well, you keep cycle time fast for legitimate claims and slow down the “quiet fraud” patterns.

Step 3: Lock down separation of duties (and prove it)

AI can flag risk, but access design still matters.

Minimum controls I’d insist on:

• No single user can create a vendor and approve the first payment
• High-risk payments require dual approval from different reporting lines
• Immutable audit logs for key actions (payee edits, overrides, check reissues)

AI is strongest when it’s paired with control points that matter.

Step 4: Build a feedback loop with SIU outcomes

Answer first: AI fraud detection improves when investigation outcomes flow back into the model.

Every closed referral should feed structured outcomes:

• Confirmed fraud
• Not fraud (and why)
• Process issue (training, system gap)

This creates a learning system rather than a one-off project.

Snippet-worthy truth: A fraud model without investigator feedback is just an expensive opinion.

“Will AI create more false positives?” Here’s how to keep it sane

Answer first: You control false positives by tuning for precision at the top of the queue, using human review for edge cases, and limiting holds to high-confidence risk patterns.

Claims teams resist fraud tools when they flood queues. The fix is design, not wishful thinking:

• Start with a narrow target. For example: towing and roadside assistance payments, or check issuance patterns.
• Use risk bands, not binary flags. Most claims should never see extra friction.
• Measure operational impact weekly. Track cycle time, SIU acceptance rate, and dollars prevented.

The reality? You don’t need a perfect model. You need a model that reliably surfaces the “we should look at this today” cases.

What insurers should do next (especially heading into 2026)

Answer first: If you want to prevent the next insider claims scam, prioritize data readiness, vendor controls, and AI-triggered investigations—not more manual review.

The end-of-year timing matters. December is when many teams are exhausted, backlog creeps up, and “just get it paid” becomes the culture. That’s exactly when weak controls get exploited.

If you’re building a 2026 roadmap for AI in insurance, I’d put these on the first page:

1. Centralize claims + payment + vendor data enough to score risk in near real time.
2. Deploy AI fraud detection on a high-leakage category (towing is a strong candidate) before expanding.
3. Add graph analytics to uncover collusion and repeated vendor/employee patterns.
4. Strengthen internal controls so risky actions require independent approval.

Insider fraud will never be “solved,” but it can be made dramatically harder and easier to catch early.

If you’re evaluating AI for claims, don’t start with flashy chatbots. Start where the money leaks. Fraud detection and prevention is the practical proving ground.

Where in your claims operation would a fraudster hide today: vendor onboarding, payment approvals, or adjuster overrides?