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 occurs in about 10% of property-casualty insurance losses in the United States, and a 2022 study for the Coalition put total US insurance fraud at US$308.6 billion a year across all lines.

In Australia, Insurance Council of Australia members detected A$560 million of opportunistic fraud in motor and property claims in 2023, and undetected fraud is estimated at around A$400 million a year. In the UK, insurers detected £1.16 billion of fraudulent general insurance claims in 2024, and the Association of British Insurers estimates that a similar amount goes undetected.

None of these figures yet separates out claims supported by AI-generated evidence. Sources and more detail are on our insurance fraud statistics page.

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 2,137% 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.

Sumsub’s 2024 Identity Fraud Report found that the global identity-fraud rate rose from 1.10% of verifications in 2021 to 2.50% in 2024, and that deepfakes detected worldwide rose fourfold from 2023 to 2024. Sumsub also found that cheap fraud tooling has lowered the barrier to entry: with as little as US$1,000, a fraud group can cause losses of up to US$2.5 million a month.

Regula’s Deepfake Trends 2024 survey found that 92% of businesses experienced identity fraud in the previous 12 months and about half had faced audio or video deepfakes. Financial-services firms lost a little over US$600,000 each to deepfakes on average (a per-business figure, not per incident).

The clearest insurance-specific signal so far is in voice: Pindrop recorded a 475% rise in synthetic-voice attacks on insurers in 2024.

What this means for insurance: If deepfake-enabled fraud follows the trajectory seen in banking and identity verification, and there is no structural reason it wouldn’t, the share of insurance fraud involving AI-generated evidence is growing quickly from a small 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, Veo, Runway, Pika) produce short clips of realistic scenes, some with synchronized audio. 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: Research systems such as Microsoft’s VALL-E can synthesize a new speaker’s voice from a three-second recording, and commercial cloning tools are widely available. Pindrop’s 2025 Voice Intelligence and Security Report found that in 2024 deepfake fraud attempts rose more than 1,300% and synthetic-voice attacks on insurers rose 475%.

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: A 2019 Federal Reserve white paper described synthetic identity fraud — combining real and fabricated identity elements to create fictitious persons — as a fast-growing problem and cited a widely reported estimate of US$6 billion in US lender losses in 2016.

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. A large University of Florida study found that people identified audio deepfakes with about 73% accuracy: better than chance, but wrong on more than one judgment in four. For faces, Nightingale and Farid (PNAS, 2022) found participants scored 48.2% at telling AI-generated faces from real ones, close to chance.

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.

Lab benchmarks overstate field performance. As we detail in our analysis of the lab-to-production accuracy gap, detectors evaluated on curated lab datasets can perform markedly worse on real-world claims media, which is compressed, variably lit, taken on diverse devices and processed through several platforms before it reaches the insurer. Evaluate any detector on media like your own.

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 favors 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 analyzes 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 approach that directly addresses AI-generated evidence, and it works alongside claims analytics rather than replacing it.

What Regulators Are Signaling

United States:

43 states plus DC mandate fraud reporting to state fraud bureaus
The NAIC’s Model Bulletin on the Use of AI Systems by Insurers (December 2023) had been adopted by 25 states and the District of Columbia as of August 31, 2026. It governs insurers’ own use of AI; the NAIC has issued no guidance specific to deepfake fraud
FinCEN issued an alert on fraud schemes that use deepfake media to target financial institutions (FIN-2024-Alert004, November 13, 2024)

Australia:

APRA prudential standards (CPS 220 Risk Management, CPS 234 Information Security, and CPS 230 Operational Risk Management, in force since July 1, 2025) require insurers to identify and manage material risks, including emerging technology-enabled ones
ASIC expects fair and timely claims handling
The Insurance Council of Australia’s counter-fraud function, the Insurance Crime Intelligence Network of Australia (ICINA), coordinates industry fraud intelligence. In November 2025 the ICA announced a national fraud detection and investigations platform with Shift Technology and EXL

Europe:

EU AI Act Article 50 requires disclosure of deepfakes and other AI-generated content from August 2, 2026. The 2026 Digital Omnibus moved the Act’s main high-risk obligations to December 2, 2027
EIOPA (European Insurance and Occupational Pensions Authority) oversees how insurers govern their own use of AI

United Kingdom:

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

Projections

Now (2026)

Deepfake-enabled insurance fraud is growing in volume but remains a minority of total fraud
Most incidents are likely to involve editing genuine photos (exaggerating damage) rather than sophisticated multimodal forgery
Media verification is still concentrated among early adopters
Insurers and industry bodies don’t yet publish AI-specific fraud figures, so publicly reported insurance cases remain rare

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 managing AI-enabled fraud will become more explicit (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

Verify media at claims intake. Analyzing photos, video, audio and documents before payment is the point where a fabricated claim is cheapest to stop, and it routes suspicious evidence to a human reviewer before money moves.
Audit your digital claims pipeline. Understand what proportion of claims include photos, videos, and documents. Quantify the attack surface.
Update risk frameworks. Record AI-generated fraud as an emerging risk in your risk management documentation (CPS 220 and CPS 230 for Australian insurers; the relevant risk framework requirements elsewhere).
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 ICINA (Australia), NICB (US) and the 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 is updated as significant new data is published. deetech™ helps insurers and claims teams verify the photos, video, audio and documents submitted with claims. Request a demo to discuss your claims media.

Sources cited in this report: