Fraud Detection Signals with AI
Insurance fraud is a multibillion-dollar problem. The Coalition Against Insurance Fraud estimates US fraud losses at over $300 billion per year across all lines. SIU (Special Investigations Unit) referrals from front-line claims work are the carrier's first line of defense. AI is becoming a meaningful tool for spotting patterns and inconsistencies that warrant a closer look.
What You'll Learn
- How AI can flag fraud signals in claim narratives, statements, and documents
- The most common fraud indicators by line of business
- How to build a fraud-screening prompt for claims intake
- The legal and ethical limits of AI in fraud determinations
Important Up Front
AI does not detect fraud. It detects signals that warrant human investigation. A "high signal" claim is not a fraudulent claim. The decision to refer to SIU, deny coverage, or pursue prosecution always requires a licensed claims professional and, often, legal counsel.
This is critical because misuse of AI in fraud accusations can trigger bad-faith claims, defamation suits, and DOI complaints. Always treat AI fraud screening as a triage signal, not a verdict.
Common Fraud Signals by Line of Business
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- Inconsistent accounts of the accident across statements
- Reported injuries disproportionate to reported damage
- Multiple prior claims with similar fact patterns
- Recently increased coverage just before the loss
- Claimants represented by attorneys at FNOL
- Repair shops, medical providers, or attorneys with prior SIU history
- Staged-collision indicators (low-speed, multiple occupants, all-claimant injuries)
Property
- Loss timing aligned with financial distress
- Recent increase in coverage limits
- Inventory loss without supporting receipts
- Repeated similar losses at the same location
- Lack of forced entry on theft claims
- Discrepancy between reported damage and photographic evidence
Workers' Comp
- Monday morning back injuries with no witness
- Claimant's job is at risk (recently disciplined, layoff pending)
- Inconsistent injury history across treating providers
- Subjective complaints disproportionate to objective findings
- Prior comp claims at unrelated employers
Health and Disability
- Provider billing patterns inconsistent with diagnosis
- Identical narratives across multiple unrelated claims
- Multiple high-intensity treatments without clinical progression
- Claimant working while collecting disability
A Fraud-Signal Screening Prompt
You are a senior claims investigator. Below is a claim
narrative. Identify potential fraud SIGNALS only — not
conclusions. For each signal you flag:
- Brief description
- Severity (Low / Medium / High)
- The single follow-up question that would either confirm
or rule out the signal
Categories to consider (not exhaustive):
- Inconsistencies within the narrative
- Inconsistencies with known facts
- Timing red flags (recent coverage changes, financial
distress)
- Pattern red flags (similar prior claims)
- Documentation gaps
- Service provider concerns
Output a numbered list. End with one summary line:
"OVERALL SIGNAL LEVEL: Low / Medium / High."
Constraint: This is a SIGNAL screening, NOT a fraud
determination. Do not accuse anyone of fraud. Use neutral,
factual language. If no signals are present, say so.
Narrative and known facts: [paste]
Comparing Statements for Inconsistencies
One of the strongest fraud signals is inconsistency across versions of the story.
You are a claims investigator. Below are two narratives of
the same incident:
NARRATIVE A: claimant's recorded statement on [DATE]
NARRATIVE B: claimant's deposition on [DATE]
List:
- Factual inconsistencies (specific facts that differ)
- Timeline inconsistencies
- Inconsistencies in described injuries or damages
- Inconsistencies in named witnesses or parties
For each, quote the specific lines from each narrative
that conflict.
Constraint: Only list items where the narratives actually
conflict on specific facts. Do not flag stylistic
differences or minor word choice variations.
Narrative A: [paste]
Narrative B: [paste]
This is a substantial time saver in litigated bodily injury claims, where the deposition might be 80 pages.
Documentation Pattern Analysis
For health and disability claims, AI can spot patterns across documents.
You are a workers' compensation claims investigator. Below
are progress notes from three different visits with the
same claimant. Identify:
- Inconsistencies in subjective complaints between visits
- Inconsistencies in claimed work restrictions
- Inconsistencies between subjective complaints and
objective findings
- Treatment escalations not supported by clinical findings
Quote the specific text supporting each observation.
Notes: [paste]
Provider Pattern Analysis
When you have a narrative from a known SIU-flagged provider, AI can spot the patterns the provider tends to use.
You are an SIU analyst. Below is a treatment narrative from
a provider. Compared to typical [INJURY TYPE] treatment,
identify:
- Treatments that are unusually frequent
- Treatments that escalate without clinical justification
- Diagnostic codes that do not match the described injury
- Billing patterns that deviate from typical care
Use general clinical knowledge. Do not invent specific
billing codes. Label any specific clinical claim as
"approximate" or "warrants verification by medical
director".
Narrative: [paste]
Building a Daily Fraud-Screening Workflow
For a claims adjuster handling 8-15 new losses per day, a daily AI-assisted fraud screen looks like:
- After morning triage, run the fraud-signal screening prompt on each FNOL narrative
- For any "Medium" or "High" overall signal level, save the AI output to the file as a triage memo
- Discuss high-signal claims with your supervisor
- Refer confirmed signals to SIU per carrier protocol
- Document the AI output, your review, and the disposition decision
This gives every claim a quick screen without slowing down throughput.
The Hard Limits
There are several places where AI must NOT be used in fraud work:
- Final fraud determinations. Always made by a licensed claims professional, often with legal counsel.
- Customer-facing fraud accusations. AI output is internal only. Never quote AI in a denial or referral letter.
- Adverse action without human review. Coverage rescissions and denials based on alleged fraud trigger heightened state-law procedures.
- Protected-class proxies. Never let an AI fraud screen rely on ZIP code, name, or other variables that proxy for protected class.
- Reporting to NICB or law enforcement. This is a human decision based on a human investigation.
Your carrier's SIU and legal teams should sign off on any AI fraud-screening tool before it touches live claims.
Key Takeaways
- AI is excellent at flagging fraud signals — inconsistencies, timing patterns, documentation gaps. It is NOT a fraud determination.
- The fraud-signal screening prompt produces a triage list of items to investigate, not a verdict.
- Comparing statements, progress notes, and provider patterns across documents is one of the highest-ROI AI workflows in litigated and SIU-referred claims.
- Final fraud determinations are made by licensed humans with legal counsel. AI never speaks to the customer.