$300B +
Estimated annual cost of insurance fraud in the United States, across all lines (Source: Coalition Against Insurance Fraud, 2022 estimate)
Agentic fraud intelligence for insurers
AI Insurance Fraud Detection for Claims Teams
AI agents check identity, behavior, documents, images, and networks on the same claim and return a single explainable fraud score.
How much does insurance fraud cost, and how fast are suspicious claims rising?
+16%
More questionable claims were referred for review in 2025 than in 2024. (Source: NICB, via NAIC Antifraud Task Force, March 2026)
~35%
More suspected staged accidents over the same period.(Source: NICB, March 2026)
Insurers already detect fraud. What do adjusters lack?
Carriers we work with have bought the tools and trained the people. What they describe is not a detection issue. Instead, it's four systems producing four opinions, a queue that nobody has ranked, and a fraud view that arrives after the payment has already gone out.
"We bought the tools, but we still don't get one answer."
Rules engine, predictive model, text mining, and a separate media checker. Four scores, four opinions, and the adjuster is left to reconcile them.
"We rely on people knowing what to look for."
A large US carrier told us that fraud is handled by claims staff, who apply what they have been trained to spot, on a case-by-case basis.
"By the time we're confident, we've already paid."
The fraud view arrives after the payment, turning every case into a recovery exercise.
"A lot of what reaches my team isn't fraud."
The rules flag anything unusual rather than anything suspicious, and nothing ranks the queue, so every referral gets the same work-up.
One fraud risk assessment per claim, with supporting evidence
Xebia Agentic Fraud Intelligence is built for claims fraud first and extends to underwriting.
Every score arrives with the needed information
Rules engine, predictive model, text mining, and a separate media checker. Four scores, four opinions, and the adjuster is left to reconcile them.
One score instead of four opinions
Identity, behavior, documents, images, and networks are assessed on the same claim and aggregated into a single score.
Find organized fraud before payout
Claimants, devices, and vendors are connected across the book, so fraud rings surface while the claim is still open.
SIU receives a case, not an alert
SIU receives a built case: a summary, an explanation of red flags, the attached evidence, and the recommended next steps.
How does it work?
How do AI agents detect fraud in insurance claims?
| Phase | Input | What the agent does | Output |
|---|---|---|---|
| 01 · IntakeIdentity verification | First notice of loss, policy record, device and location | Validates the policyholder, checks device and geography, flags duplicate identities. | Verified claimant identity, with a trust rating |
| 02 · IntakeData collection and enrichment | Policy, claims, payment, CRM and watchlist records | Pulls those records through API and data connectors. | One unified claim profile |
| 03 · DetectionBehavior and narrative analysis | Claim narrative, call transcript, chat | Analyzes the narrative, reviews the interaction and detects conflicts between them. | Narrative inconsistency flags |
| 04 · DetectionDocument and image forensics | IDs, invoices, photos, file metadata | Validates the evidence, detects mismatches and detects manipulation. | Evidence integrity rating |
| 05 · DetectionNetwork and link analysis | Claimants, vendors, accounts, prior claims | Maps entities and links, checks shared addresses and IPs, identifies fraud rings. | Network risk score, with ring alerts |
| 06 · DispositionClaim assessment | Signals from phases 01 to 05, and your risk thresholds | Aggregates every signal, scores the claim and writes the reason codes. A person decides. | Composite score with ranked reason codes |
The Same Claim, With and Without Agents
| Stage | Insurers Today | With Xebia Agentic Fraud Intelligence |
|---|---|---|
| Detection | Four tools raise four separate flags. The adjuster reconciles four opinions. | Simplified. One score, with the reasons ranked and the evidence attached. |
| Fraud Rings | Link analysis runs only on claims someone already found suspicious. | Network checks across every claim. Look across all systems to understand the big picture across every claim. |
| The SIU Handoff | Investigators assemble the case before they can judge whether there’s one. | Cases ready to investigate. The case pack arrives built: red flags are explained, evidence attached, next step recommended. |
What ROI can AI fraud detection deliver for insurers?
Standfirst: The figures below are modeled, not measured. They show what the solution is designed to deliver, and the basis for each is published underneath.
~30%
Less fraud review effort per claim. (NICB, 2022)
$1.5M+
Value of fraud stopped before payment, per 1,000 claims. (Deloitte Center for FS, 2025)
2x
More confirmed fraud per case opened. (Verisk, State of Insurance Fraud, 2024)
Who benefits from Xebia Agentic Fraud Intelligence?
Property and casualty, including motor, health, commercial, and personal lines.
P&C claims carriers
High claim volumes across property and casualty, where leakage accumulates quietly across thousands of small decisions.
Motor and auto insurers
Staged accidents, repair and vendor collusion, and injury claims that need document and image forensics as standard.
Health and life insurers
Document-heavy claims, provider networks, and genuine claims held behind blanket checks.
Digital and direct insurers
Fast, automated intake with little human contact, where identity and device checks have to carry more weight.
SIU-heavy insurers
Established investigation functions that need ranked referrals and built case packs rather than more alerts.
Highlighted Case Study
Stopping a high-value claim before a referral was ever advanced
The challenge: A large property claim was notified long after the event, with a third-party payee and a bank account changed shortly before submission. Documents and photos were checked by eye, or in a tool that does not feed the scoring model. Link analysis sat behind the referral gate, so evidence gaps appeared late.
The approach: The Identity, device,
and session were verified at intake. Documents, invoices, and damage photos were tested for forgery, reuse ,and staging. The network check ran as a scoring input, mapping payee, bank account , and phone number across the book.
Book a demo
Bring a claim type that causes you trouble. We will show how the signals are assessed, how the score is built, and what the adjuster and SIU each receive.
Frequently Asked Questions
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Let’s discuss how we can support your journey.