What Is a Deepfake? A Guide for Insurance Professionals

If you work in insurance — underwriting, claims, investigation, or compliance — you need to understand deepfakes. Not at a computer science level. At a practical level: what they are, what they look like, where they appear in your workflow, and what to do about them.

This guide is written for insurance professionals without a technical background. It covers the fundamentals and points you to deeper resources where relevant.

What Is a Deepfake?

A deepfake is any piece of media — image, video, audio, or document — that has been created or manipulated using artificial intelligence to misrepresent reality.

The term originated in 2017, combining “deep learning” (a type of AI) with “fake.” Originally it referred specifically to AI-generated face swaps in video. Today it encompasses a much broader range of AI-generated and AI-manipulated content:

Face swaps: Replacing one person’s face with another’s in photos or video
Voice clones: Synthesizing someone’s voice from a small sample of real audio
Synthetic images: Entirely AI-generated photographs of people, places, or objects that don’t exist
Manipulated documents: AI-altered or AI-generated text documents, including medical records, invoices, and official reports
Synthetic video: Fully AI-generated video content, including fabricated CCTV footage or incident recordings

The key point: deepfakes aren’t just face swaps on social media. In insurance, deepfakes encompass any AI-generated or AI-manipulated content submitted as part of a claim, application, or verification process.

How Deepfakes Are Made

You don’t need to understand the mathematics. You do need to understand the basic mechanisms, because they determine what’s possible and what detection looks like.

Generative Adversarial Networks (GANs)

GANs were the original deepfake technology. A GAN consists of two AI models working against each other:

The generator creates fake content (an image, for example)
The discriminator tries to determine whether the content is real or fake

Through thousands of iterations, the generator learns to produce increasingly convincing fakes, while the discriminator learns to detect increasingly subtle flaws. The result is a generator capable of producing content that’s extremely difficult to distinguish from reality.

Insurance relevance: GANs are used to generate synthetic faces for identity fraud, fake damage photographs, and synthetic medical images.

Diffusion Models

Diffusion models (the technology behind tools like Stable Diffusion, FLUX and Midjourney) work differently. They start with random noise and progressively refine it into a coherent image, guided by text descriptions or reference images.

Diffusion models have largely overtaken GANs for image generation because they produce higher-quality, more controllable output. Some newer generators, such as the image generation built into OpenAI’s GPT-4o, use different (autoregressive) techniques, which is one reason detectors have to keep up with more than one family of tools.

Insurance relevance: Diffusion models generate photorealistic images of vehicle damage, property damage, personal injuries, and document elements. They can be directed with precise text prompts: “photograph of hail damage to the hood of a red 2022 Toyota RAV4, taken in daylight.”

Large Language Models (LLMs)

LLMs (such as the GPT, Claude and Gemini model families) generate human-quality text. They can produce any type of written document: medical reports, legal statements, repair estimates, correspondence, and more.

Insurance relevance: LLMs generate the textual content of fake medical records, repair estimates, police reports, witness statements, and other claim documentation. They can match the tone, terminology, and formatting of legitimate documents with high accuracy.

Voice Cloning

Voice cloning AI can replicate a specific person’s voice from a short recording. Research systems such as Microsoft’s VALL-E (2023) demonstrated cloning from a three-second sample, and OpenAI’s Voice Engine preview needed 15 seconds. The cloned voice can then speak any text, with natural intonation, emotion, and speech patterns.

Insurance relevance: Voice clones can be used to impersonate policyholders in phone-based claim submissions, authorization calls, and recorded statements. They have fooled voice-based authentication: in 2023, a Guardian Australia journalist used an AI clone of his own voice to access his Centrelink account through the government’s voiceprint system.

Real-Time Deepfakes

The latest development: real-time deepfake technology that can alter appearance and voice during live video calls. A fraudster on a video call can appear to be someone else entirely — different face, different voice — in real time.

Insurance relevance: Real-time deepfakes threaten video-based verification processes, including telehealth assessments, video-based claims verification, and remote identity checks used in underwriting. In early 2024, a finance worker at the engineering firm Arup transferred about US$25 million after a video call in which the other participants were deepfakes.

Why Insurance Is a Target

Insurance is uniquely attractive to deepfake fraudsters for several reasons:

High-Value Payouts with Document-Based Verification

Insurance claims are fundamentally document-based processes. Submit the right documents, receive a payout. Historically, the primary barrier to fraud was the difficulty of creating convincing fake documents. AI has eliminated that barrier.

Trust in Third-Party Documents

Insurance processes place significant trust in documents from third parties: doctors, repair shops, police, employers. This trust creates attack vectors — it’s often easier to fabricate a third-party document than to manipulate the insurer’s own systems.

Volume Creates Cover

Large insurers process millions of claims annually. High-volume processing means each individual document receives limited scrutiny. A convincing fake in a routine claim has a high probability of passing through unchallenged.

Remote Processes

The shift to digital claims submission, remote verification, and online underwriting has reduced in-person checks that once caught fraud. Deepfakes are specifically designed to exploit digital channels.

Delayed Verification

Insurance verification often occurs after payment. By the time fraud is detected (if it’s detected at all), the money is gone. Fraud that surfaces months later, if ever, gives fraudsters a long runway; see our insurance fraud statistics for the wider picture.

Types of Deepfakes in Insurance Claims

Identity Deepfakes

What they look like: A synthetic photograph or video of a person who doesn’t exist, or a face swap placing one person’s face onto another’s body.

Where they appear in insurance:

Application photos for identity verification
Video calls during remote underwriting or claims assessment
Photographs submitted as proof of identity
Social media profiles supporting synthetic identities

Example scenario: A fraudster creates a synthetic identity — AI-generated face, fabricated documents, fake digital history — and purchases a life insurance policy. After a waiting period, a death claim is submitted using fabricated documentation.

Document Deepfakes

What they look like: AI-generated or AI-manipulated text documents that replicate the format, language, and content of legitimate records.

Where they appear in insurance:

Medical records and pathology reports
Repair estimates and invoices
Police and incident reports
Employment and income verification documents
Legal correspondence and court orders

Example scenario: A motor insurance claimant submits three AI-generated repair estimates from fictitious businesses, all inflated by 30%. The documents use correct trade terminology, realistic pricing, and legitimate-looking business branding.

Image Deepfakes

What they look like: AI-generated or AI-manipulated photographs showing damage, injuries, property conditions, or events that didn’t occur or have been exaggerated.

Where they appear in insurance:

Vehicle damage photographs
Property damage images (storm, fire, water, burglary)
Personal injury photographs
Before/after comparison images
CCTV or dashcam footage

Example scenario: A property insurance claimant uses AI to add storm damage to photographs of their roof, or to enhance minor damage to appear severe. The AI-generated elements are photorealistic and blend seamlessly with the genuine image.

Audio Deepfakes

What they look like: AI-cloned voices that sound identical to a real person, speaking words that person never said.

Where they appear in insurance:

Phone-based claim submissions
Voice authorization for claim payments
Recorded statements
Voicemail messages used as evidence

Example scenario: A fraudster clones a policyholder’s voice (from publicly available audio or a brief phone call) and uses it to authorize a change of bank details for claim payments, redirecting funds.

How to Spot Deepfakes: Practical Indicators

Visual Indicators (Photos and Video)

While AI quality is improving rapidly, current deepfakes may exhibit:

Inconsistent lighting: Shadows that don’t match the light source direction
Skin texture anomalies: Overly smooth skin, or inconsistent texture between the face and neck/ears
Eye irregularities: Mismatched reflections in pupils, unnatural eye movement patterns
Hair and edge artifacts: Blurring or distortion where the face meets the background or hairline
Temporal inconsistencies (video): Flickering, momentary distortions, or unnatural blinking patterns
Background anomalies: Warping or distortion in the background near the subject’s face

Important caveat: These visual indicators are becoming less reliable as AI improves. A 2024 meta-analysis of 56 studies found that people identify deepfakes correctly only about 55% of the time, not reliably better than a coin flip (more deepfake statistics). Visual inspection alone is no longer sufficient.

Document Indicators

Unusually polished language: Legitimate documents often contain minor grammatical irregularities, abbreviations, and shorthand. AI-generated text is often too clean, although polish alone proves nothing.
Metadata anomalies: Documents created by unexpected software, at unusual times, or with inconsistent modification histories
Formatting that’s close but not quite right: Templates that don’t precisely match known formats from the stated source
Internal inconsistencies: Treatment plans that don’t match diagnoses, repair items that don’t match damage descriptions, timeline gaps

Audio Indicators

Unnatural pauses or cadence: AI-generated speech may have slightly odd timing
Consistent quality: Real phone calls have variable audio quality. Deepfake audio may be suspiciously clean throughout.
Limited emotional range: Voice clones may struggle with natural emotional variation, particularly spontaneous reactions

Behavioral Indicators

Beyond the media itself, behavioral patterns suggest deepfake fraud:

Reluctance to engage through alternative channels (refusing video when audio is questioned, or vice versa)
Submission of documentation in formats that resist forensic analysis (photos of screens, screenshots, heavily compressed files)
Claims that rely entirely on documents from a single source or provider
Patterns of claims that specifically target automated processing thresholds

What Insurance Professionals Should Do

For Claims Assessors

Don’t rely on visual inspection alone for any media submitted with a claim. AI-generated content is too good for human detection.
Check metadata on every digital document. Creation software, timestamps, and author fields provide forensic evidence that visual inspection cannot.
Verify providers independently. Don’t use contact details from the submitted document — look up the provider directly through official channels.
Cross-reference everything. Does the police report match the claimant’s account? Do the medical records match the claimed timeline? Do the repair estimates align with the damage photos?
Escalate when uncertain. If something feels off but you can’t pinpoint why, escalate. Your instinct is picking up on patterns even if you can’t articulate them.

For Underwriters

Implement multi-factor identity verification that doesn’t rely solely on document submission
Be aware of synthetic identity indicators: new credit files, limited digital history, perfect documentation
Question applications that are “too clean” — real applicants make mistakes, omissions, and corrections

For Investigators

Use specialized forensic tools for deepfake detection — not general-purpose image editing software
Preserve original files exactly as submitted. Compression, format conversion, and resizing destroy forensic evidence.
Document your detection methodology for legal admissibility
Stay current on deepfake technology — capabilities change rapidly

For Leadership

Invest in detection technology now. The threat is growing quickly.
Train your teams. Awareness is the first line of defense.
Update policies and procedures to address AI-generated content specifically
Engage with industry bodies: the Insurance Council of Australia and its counter-fraud function, the UK’s Insurance Fraud Bureau, the US National Insurance Crime Bureau, and their counterparts elsewhere are developing shared approaches

Key Terms

For a comprehensive glossary of AI and deepfake terms relevant to insurance, see our Insurance Fraud Glossary: AI and Deepfake Terms Explained.

Quick reference:

Deepfake: AI-generated or AI-manipulated media designed to misrepresent reality
GAN: Generative Adversarial Network — an AI architecture that learns to generate convincing fake content
Diffusion model: An AI model that generates images by progressively refining random noise
LLM: Large Language Model — an AI that generates human-quality text
Synthetic identity: A fabricated identity combining real and fake information, often with AI-generated elements
Liveness detection: Technology that verifies a person is physically present (not a deepfake) during identity verification
C2PA: Coalition for Content Provenance and Authenticity, the body behind the Content Credentials standard for signing the origin and edit history of digital content

The Bottom Line

Deepfakes are not a future threat. They’re a current, escalating problem that affects every insurance line and every stage of the insurance lifecycle. The technology is accessible, the results are convincing, and the financial impact is measured in billions.

The good news: detection technology exists and is improving. The bad news: most insurers haven’t deployed it yet. The window between deepfake capability and detection deployment is where fraud thrives.

Understanding what you’re dealing with is the first step. This guide gives you that foundation. What you do with it determines whether your organization is ahead of this threat or behind it.

deetech™ helps insurance organizations analyze supported claim and underwriting media for synthetic-media indicators. It does not verify the identity or liveness of the person behind that media. Learn about our solutions or talk to our team.