The State of Deepfake Fraud in Insurance: 2026 Report

This report consolidates the available data on AI-generated fraud affecting the insurance industry. It draws from published research, regulatory filings, and industry reporting to establish the current state of the threat and project its trajectory.

No single authoritative source tracks deepfake fraud in insurance specifically. This report aggregates data from multiple domains — identity fraud, financial fraud, media manipulation, and insurance industry reporting — to construct the most complete picture currently possible.

The Scale of Insurance Fraud

Baseline: Total Insurance Fraud

The Coalition Against Insurance Fraud estimates that fraud constitutes approximately 10% of property-casualty insurance losses in the United States, with total losses estimated at over US$308.6 billion annually across all lines.

In Australia, the Insurance Council of Australia reported A$280 million in detected fraudulent claims in 2017 across all insurance classes (excluding health and personal injury). The ICA acknowledges this figure represents detected fraud only — the undetected portion remains unquantified.

Globally, the International Association of Insurance Supervisors (IAIS) has identified insurance fraud as a systemic concern, with estimates ranging from 5% to 15% of total claims expenditure depending on the market and insurance line.

The AI Amplification Factor

The question is no longer whether fraud occurs at scale — that’s established. The question is how AI tools are changing the economics and execution of that fraud.

Key data points:

Signicat’s research found deepfake-based fraud attempts increased by 2,100% over three years, with deepfakes now representing 6.5% of all fraud attacks. While this data covers financial services broadly, insurance claims processes share the same vulnerability surface: identity verification, document submission, and evidence assessment.

The Sumsub 2024 Identity Fraud Report documented that identity fraud rates rose from 1.1% to 2.5% of all verifications across industries. The report highlighted that AI-powered fraud tools have lowered the barrier to entry, enabling less sophisticated actors to execute more convincing attacks.

Regula’s research found that 92% of businesses worldwide experienced identity fraud in the past 12 months, with average losses from deepfakes in the financial industry reaching US$600,000 per incident.

What this means for insurance: If deepfake-enabled fraud follows the trajectory observed in banking and identity verification — and there is no structural reason it wouldn’t — then the proportion of insurance fraud involving AI-generated evidence is growing exponentially from a small but rapidly expanding base.

Attack Vectors in Insurance

1. Fabricated Claims Evidence — Images

The threat: AI image generation and manipulation tools can produce or alter photos of property damage, vehicle damage, personal injury, and environmental conditions. A genuine photo of minor damage can be edited to show catastrophic damage. Damage can be generated entirely from text prompts.

Current capability: Tools like Stable Diffusion, FLUX, and Midjourney produce photorealistic images. Adobe Firefly and open-source inpainting tools allow targeted manipulation of specific regions within genuine photos. The output quality is sufficient to pass casual human inspection.

Insurance relevance: Photo evidence is submitted with the majority of property and auto claims. Most adjusters review photos on screens at normal zoom — conditions where current-generation AI output is difficult to distinguish from genuine captures.

2. Fabricated Claims Evidence — Documents

The threat: Large language models produce text with appropriate formatting, terminology, and structure for any document type. Combined with image generation for logos, letterheads, signatures, and stamps, complete forged documents can be produced in minutes.

Insurance relevance: Claims processing relies on supporting documents — police reports, medical records, repair estimates, invoices, and correspondence. A fabricated police report supporting a staged accident claim, or a manipulated medical record inflating injury severity, directly impacts claim assessment.

3. Fabricated Claims Evidence — Video

The threat: Video generation tools (Sora, Runway, Pika) produce short clips of realistic scenes. Quality is improving rapidly with each model generation.

Insurance relevance: Dashcam footage, security camera recordings, property walkthrough videos, and video statements are increasingly submitted as claims evidence. As video generation quality approaches photorealism, this evidence category becomes increasingly suspect.

The threat: Voice cloning requires as little as 3 seconds of sample audio to produce convincing replicas. Pindrop’s 2025 Voice Intelligence and Security Report documented US$12.5 billion in contact center fraud losses in 2024.

Insurance relevance: Insurers operate call centers for claims reporting, policy servicing, and customer support. Voice cloning enables impersonation of policyholders (to file fraudulent claims or change account details), impersonation of claims adjusters (to extract information from claimants), and impersonation of service providers (to redirect payments).

5. Synthetic Identity for Policy Fraud

The threat: The Federal Reserve has identified synthetic identity fraud — combining real and fabricated identity elements to create fictitious persons — as the fastest-growing financial crime in the United States, with estimated losses of US$6 billion annually.

Insurance relevance: Synthetic identities can be used to take out insurance policies (life insurance, health insurance), build claims histories with small legitimate claims before submitting large fraudulent ones, and create networks of fictitious policyholders for coordinated fraud schemes.

6. Manipulated Telehealth Consultations

The threat: Real-time deepfake technology enables impersonation during live video calls. The Hong Kong deepfake CFO case — where fraudsters used real-time deepfakes to impersonate multiple executives on a video call, resulting in a US$25.6 million transfer — demonstrated that this capability is already production-ready for fraud.

Insurance relevance: Telehealth consultations are standard in health insurance claims and workers’ compensation assessments. If the consultation can be deepfaked — either the patient impersonating someone else, or a fabricated consultation that never occurred — the downstream claim is fraudulent from its foundation.

The Detection Gap

Current State of Detection

Human detection is unreliable. Research from the University of Florida found that humans detect audio deepfakes with only 73% accuracy — barely better than chance. For visual deepfakes, the gap is narrowing as generation quality improves. Reality Defender’s own CEO noted that “in internal tests, our own PhDs incorrectly labeled at least one manipulated sample as real.”

Most insurers lack AI detection capability. Signicat’s research found that only 22% of organizations have implemented specific measures to combat AI-driven fraud. In the insurance industry specifically, where digital claims submission is standard but AI media verification is not, the gap is likely wider.

Generic tools underperform on insurance media. As we’ve detailed in our analysis of the lab-to-production accuracy gap, deepfake detection tools trained on curated lab datasets typically achieve 95%+ accuracy, but this collapses to 50-65% on real-world insurance claims media — which is compressed, variably lit, taken on diverse devices, and processed through multiple platforms before reaching the insurer.

The Asymmetry

The current situation represents a classic asymmetry:

Factor	Attack	Defense
Cost	Low (free/cheap AI tools)	High (enterprise detection systems)
Speed	Minutes to generate	Days/weeks to investigate
Scale	One person can generate thousands of fraudulent items	Investigation is manual and per-claim
Skill required	Decreasing rapidly	Increasing (needs AI + insurance expertise)
Detection	Attacker knows what detection looks for	Defender doesn’t know what generation tool was used

This asymmetry favours the attacker and will continue to do so until automated detection is deployed at scale across the claims intake pipeline.

Industry Preparedness

What Insurers Are Doing

Based on available reporting:

Most: Relying on traditional fraud detection — rules-based triggers (claim timing, value thresholds, claimant history), adjuster suspicion, and SIU investigation after escalation. None of these methods analyze the media evidence itself.

Some: Implementing AI-assisted claims triage that analyses claims data patterns but not the media content. This catches data-pattern fraud (e.g., claims that match known fraud patterns) but misses evidence-fabrication fraud (where the data patterns are normal but the evidence is fake).

Few: Deploying AI-powered media analysis that examines submitted photos, videos, and documents for manipulation indicators. This is the only approach that directly addresses AI-generated evidence.

What Regulators Are Signalling

United States:

43 states plus DC mandate fraud reporting to state fraud bureaus
The NAIC has formed working groups on AI in insurance, though specific guidance on deepfake fraud hasn’t been issued
FinCEN issued an alert on deepfake fraud targeting financial institutions (November 2024)

Australia:

APRA (CPS 220, CPS 230, CPS 234) requires identification and management of emerging risks including technology-enabled fraud
ASIC expects fair claims handling, which includes effective fraud prevention
The ICA’s IFBA coordinates industry fraud intelligence

Europe:

The EU AI Act includes provisions on deepfake disclosure and detection
EIOPA (European Insurance and Occupational Pensions Authority) has flagged AI-enabled fraud as an emerging supervisory concern

United Kingdom:

The Insurance Fraud Bureau (IFB) coordinates intelligence sharing
The FCA expects firms to have effective systems and controls for fraud prevention

Projections

Short Term (2026)

Deepfake-enabled insurance fraud will increase in volume but remain a minority of total fraud
Most incidents will involve image manipulation (exaggerating damage in photos) rather than sophisticated multi-modal forgery
Detection capability will remain concentrated among early adopters; most insurers will be unprotected
The first widely publicised insurance-specific deepfake fraud cases will emerge, driving industry awareness

Medium Term (2027-2028)

Video-based claims evidence will become increasingly unreliable without AI verification
Coordinated fraud rings will use AI tools to generate evidence packages at scale
Regulatory expectations for AI fraud detection will formalise (APRA, NAIC, EIOPA)
The cost gap between AI-generated fraud and human investigation will widen further
Insurers without detection will face measurably higher fraud losses than those with it

Long Term (2029+)

AI media verification will be standard infrastructure in claims processing — as fundamental as identity verification is today
Detection and generation will be in continuous arms race, requiring ongoing model updates
The industry will develop shared intelligence databases for AI-generated fraud patterns
Insurers that invested early will have mature, tuned systems; late adopters will be deploying into an embedded crisis

Recommendations

For Insurers

Deploy AI media detection at claims intake. This is the single highest-ROI action. Every fraudulent claim caught before payment is the full claim value saved.
Audit your digital claims pipeline. Understand what proportion of claims include photos, videos, and documents. Quantify the attack surface.
Update risk frameworks. Ensure AI-generated fraud is identified as an emerging risk in your risk management documentation (CPS 220/230 for Australian insurers, risk framework requirements for US/EU insurers).
Build forensic capability. Ensure your SIU has access to AI detection tools and training to interpret results.
Contribute to industry intelligence. Share fraud patterns with IFBA (Australia), NICB (US), IFB (UK). Collective intelligence benefits everyone.

For Regulators

Issue specific guidance on AI-generated fraud in insurance. Current frameworks address technology risk generically; insurance-specific guidance would accelerate adoption.
Require AI media verification for digital claims above a threshold. Just as KYC is required for financial transactions, media verification should be required for claims evidence.
Establish reporting mechanisms for AI-generated fraud. Current fraud reporting doesn’t distinguish between AI-enabled and traditional fraud, limiting intelligence.

For the Industry

Develop shared standards for AI media verification in insurance. Interoperability between detection systems and claims platforms accelerates adoption.
Create industry benchmarks for detection accuracy on insurance media. Current benchmarks use curated datasets that don’t reflect real claims conditions.
Fund research on insurance-specific AI fraud patterns. The academic research focuses on face-swap detection; insurance fraud involves property damage, document forgery, and other categories that receive less attention.

This report will be updated annually. deetech is committed to providing the insurance industry with transparent, data-driven intelligence on the evolving deepfake threat. Request a demo to discuss how deetech can help your organization.

Sources cited in this report: