Deepfake Detection FAQ for Insurance Companies

Insurance carriers exploring deepfake detection face a new and rapidly evolving technology category. The questions below reflect the most common concerns we hear from CROs, claims leaders, fraud managers, and technology teams evaluating media authenticity solutions for insurance.

The Threat

1. How common is deepfake fraud in insurance?

Precise figures are difficult to establish because undetected deepfake fraud is, by definition, unmeasured. What we know: Sumsub’s 2024 Identity Fraud Report documented a 245% increase in deepfake-related fraud globally between 2023 and 2024. The Insurance Fraud Bureau (UK) has confirmed cases of AI-generated evidence in claims. Deloitte’s 2024 analysis estimated generative AI-enabled fraud could reach US$40 billion in losses across financial services by 2027. Insurance, with its reliance on submitted photographic and documentary evidence, is a primary target.

2. What types of deepfakes are being used in insurance fraud?

The most common types in insurance claims:

AI-generated damage photos — images of vehicle, property, or personal injury damage created by tools like Stable Diffusion, Midjourney, or DALL-E
Manipulated genuine photos — real images edited with AI inpainting to exaggerate damage, change timestamps, or alter details
AI-generated documents — repair quotes, medical certificates, invoices, and receipts produced by generative AI
Recycled imagery — genuine damage photos from other incidents or sourced online, submitted as fresh evidence
Voice cloning — impersonation of policyholders during phone-based claims using AI voice synthesis

3. Can’t adjusters spot deepfakes visually?

Increasingly, no. A 2023 study published in Psychological Science (Nightingale & Farid) found that AI-generated faces were rated as more trustworthy than real faces by human evaluators. Modern image generators (particularly diffusion models from 2024 onwards) produce outputs that consistently fool trained observers. While obvious errors still occur (incorrect finger counts, text anomalies, impossible reflections), the trend is toward fewer visual artifacts with each model generation. Relying on human visual inspection is not a viable long-term strategy.

4. Are there specific insurance lines more at risk?

All lines that accept photographic or documentary evidence are vulnerable, but risk concentrates in:

Motor claims — damage photos are standard evidence and relatively simple to fabricate
Property claims — particularly after catastrophe events when volume creates processing pressure
Personal injury/CTP — medical documentation and injury photos
Contents claims — photos of damaged or stolen items
Travel insurance — overseas medical receipts and documentation
Workers’ compensation — medical certificates and workplace injury documentation

5. Is this threat relevant to the Australian market specifically?

Yes. The Insurance Council of Australia estimates fraud adds A$2.2 billion annually to claims costs in Australia. The 2022 and 2024 flood events demonstrated how catastrophe surge creates conditions where fraudulent claims (including those with manipulated evidence) are more likely to succeed due to processing pressure. APRA and ASIC have both signalled increased focus on AI-related risks in financial services.

6. How does catastrophe event fraud relate to deepfakes?

Catastrophe events (floods, bushfires, cyclones, storms) create ideal conditions for deepfake fraud:

Volume pressure — thousands of claims must be processed quickly, reducing scrutiny per claim
Similar damage patterns — legitimate damage looks similar across claims, making fabricated photos harder to distinguish by context alone
Emotional urgency — pressure to pay claims quickly for genuine victims creates reluctance to investigate
Recycled imagery — photos from the same event can be submitted across multiple claims, or photos from previous events can be resubmitted

Detection Technology

7. How does deepfake detection work?

Modern deepfake detection uses multiple techniques in combination:

Artifact analysis — AI-generated images contain statistical patterns invisible to the human eye but detectable by trained models. These include frequency domain anomalies, pixel-level inconsistencies, and generation-specific fingerprints.
Metadata analysis — examining EXIF data, compression signatures, and file structure for inconsistencies with claimed capture conditions.
Environmental consistency — checking lighting, shadows, reflections, and physics for internal consistency within the image.
Provenance verification — reverse image matching against known databases and public imagery.
Model fingerprinting — identifying which specific generative AI model produced an image based on its characteristic artifact patterns.

8. Can AI detect all types of deepfakes?

No detection system is 100% effective against all deepfake types. Detection is an arms race between generators and detectors. However, current multi-model detection systems achieve high accuracy against the majority of commercially available generation tools. The key is using ensemble approaches (multiple detection models in combination) rather than relying on any single technique.

9. How accurate is detection on compressed claim photos?

This is a critical question because most claims media is heavily compressed. Images sent via WhatsApp, email, or web portals lose significant quality and metadata. Detection accuracy on compressed media is lower than on uncompressed originals — typically 5-15 percentage points lower depending on compression severity. This is why detection systems designed for insurance (like Deetech) train specifically on compressed, degraded media rather than relying on benchmark accuracy achieved on clean datasets.

10. What about photos of documents (rather than digital files)?

Photos of documents — a mobile phone photo of a repair quote, for example — present unique challenges. The image contains both the content of the document and the photographic artifacts of the capture process. Detection must separate these layers. Deetech’s document analysis examines both the document content for AI generation signatures and the photographic capture for manipulation indicators.

11. Does detection work on video evidence?

Yes, though video presents additional complexity. Video analysis examines:

Frame-by-frame consistency for temporal artifacts
Face swap detection across video sequences
Audio-visual synchronisation (lip sync analysis)
Compression artifacts specific to video codecs
Editing signatures (cut points, splices, speed changes)

Dashboard camera footage, security recordings, and video documentation submitted with claims can all be analyzed.

12. Can voice cloning be detected?

Yes. Voice cloning detection analyses:

Spectral characteristics inconsistent with natural speech
Synthesis artifacts in frequency domains
Temporal patterns that differ from genuine speech production
Environmental audio inconsistencies

Detection accuracy varies by cloning tool quality. Consumer-grade cloning services are generally easier to detect than professional tools. This is an area of rapid advancement on both the generation and detection sides.

13. How quickly can detection be performed?

Automated screening: seconds per item. Enhanced multi-model analysis: under one minute per item. Full forensic investigation: minutes to hours depending on complexity. For claims processing, automated screening at the point of submission adds negligible latency to the claims intake process.

14. Do we need to analyze every claim, or just suspicious ones?

Every claim. The purpose of automated screening is to catch submissions that would not otherwise be flagged. If you only analyze claims already identified as suspicious by other means, you miss the primary value proposition: detecting fraud that looks legitimate to traditional systems.

Implementation

15. Do we need to replace our existing fraud tools?

No. Deepfake detection is complementary to pattern-based fraud detection tools like Shift Technology and FRISS. These tools analyze claims data patterns. Deepfake detection analyses media authenticity. They address different fraud vectors and work together.

16. How does deepfake detection integrate with our claims system?

Via API integration with your claims management system. Deetech provides native integrations with Guidewire ClaimCenter, Duck Creek Claims, and Sapiens ClaimsPro, as well as a REST API for custom systems. Media is analyzed automatically when attached to a claim, and results appear in the adjuster’s workflow.

17. What systems does Deetech integrate with?

Guidewire ClaimCenter
Duck Creek Claims
Sapiens ClaimsPro
Custom systems via REST API
SIU case management platforms
Document management systems
Cloud storage (for batch analysis of historical claims)

18. How long does implementation take?

Typical implementation: 4-8 weeks from contract to production. This includes API integration, model calibration on representative media samples, workflow configuration, and user training. Cloud deployment is faster; on-premises deployment takes longer.

19. Do adjusters need training to use it?

Minimal. Deetech’s results appear as risk scores and summary findings within the adjuster’s existing claims interface. Adjusters don’t need to interpret technical detection data — they see a clear indication of whether media is flagged, with a recommendation for action. Full forensic reports are generated for SIU teams who require technical detail.

20. Can we run it on historical claims?

Yes. Batch analysis of historical claims media can identify potentially fraudulent claims that were previously approved. This serves both as a recovery opportunity and as a baseline assessment of deepfake fraud exposure in your portfolio.

Accuracy and Reliability

21. What about false positives?

False positives — genuine media incorrectly flagged as AI-generated — are a concern for any detection system. Deetech’s three-layer architecture addresses this:

Automated screening is calibrated for high sensitivity (catch as much as possible)
Enhanced analysis applies multiple independent models to reduce false positives
Items flagged by only one model at low confidence are handled differently from items flagged by multiple models at high confidence
Confidence scores allow carriers to set their own thresholds based on risk appetite

Typical false positive rates at recommended thresholds are below 2% — meaning fewer than 2 in 100 genuine submissions are incorrectly flagged. Flagging triggers enhanced review, not automatic claim denial.

22. What about false negatives?

False negatives — AI-generated media that passes undetected — are the more dangerous error for insurers. No detection system eliminates false negatives entirely. Deetech’s multi-model ensemble approach reduces false negatives by applying multiple independent detection techniques. If one model misses a particular generation technique, others may catch it.

Detection models are continuously updated as new generation techniques emerge. This is an ongoing process, not a one-time deployment.

23. How do detection models stay current?

Generative AI evolves continuously. Detection models must evolve with it. Deetech’s approach:

Continuous monitoring of new generative AI tools and techniques
Regular model updates incorporating new generation signatures
Threat intelligence specific to insurance fraud trends
Ongoing validation against real-world claims media samples

Model updates are deployed without service interruption for cloud deployments.

24. What happens when a completely new type of deepfake appears?

Multi-model ensemble detection provides resilience against novel techniques. While any single detection model might miss a new generation approach, the combination of multiple independent detection methods — artifact analysis, metadata verification, environmental consistency, frequency domain analysis — provides a defense-in-depth approach. New generation techniques are typically detectable by at least some ensemble components even before specific model updates are released.

Legal and Regulatory

25. Is deepfake detection evidence admissible in court?

Detection evidence can be admissible if properly documented. Key requirements:

Methodology must be disclosed and scientifically sound
Chain of custody for digital evidence must be maintained
Expert testimony may be required to explain findings
Reports must meet the evidentiary standards of the relevant jurisdiction

Deetech’s forensic reports are designed for admissibility under Australian evidence law, including methodology disclosure, chain of custody documentation, and statistical confidence intervals.

26. Can we deny a claim based solely on deepfake detection?

This depends on your jurisdiction and regulatory framework. In Australia, claims decisions must be made in accordance with the Insurance Contracts Act 1984 and the General Insurance Code of Practice. Deepfake detection findings should be considered alongside other evidence and investigation findings. They strengthen the evidence base for denying fraudulent claims but should not typically be the sole basis for denial without supporting investigation.

27. What are APRA and ASIC’s positions on deepfake fraud?

APRA’s CPS 234 (Information Security) and SPS 220 (Risk Management) create frameworks that implicitly require insurers to manage AI-related fraud risks. ASIC has published guidance on AI governance in financial services and has signalled increasing focus on AI-enabled fraud threats. Neither regulator has issued specific deepfake detection mandates as of early 2026, but the regulatory direction is toward greater accountability for managing emerging technology risks.

28. Do we need to disclose deepfake detection to policyholders?

This depends on your jurisdiction and the specific implementation. In Australia, using automated analysis of claims media should be considered in the context of privacy obligations (Privacy Act 1988) and the Insurance Code of Practice requirements around claims handling transparency. Generally, disclosing the use of fraud detection technology (without revealing specific techniques) in product disclosure statements or claims process documentation is good practice.

29. What about privacy implications of analyzing claims media?

Deepfake detection analyses the technical characteristics of media files — compression patterns, frequency domain properties, metadata structures. It is not facial recognition technology. It does not identify individuals or create biometric profiles. The analysis examines whether the media is authentic, not who appears in it. Standard data handling practices for claims media apply.

Cost and ROI

30. How much does deepfake detection cost?

Costs vary by provider and deployment model. Deetech uses per-claim pricing with volume tiers. The per-claim cost is a small fraction of average claim values. For specific pricing, contact the relevant vendor.

31. What’s the ROI of deepfake detection?

The ROI calculation depends on:

Fraud rate — what percentage of claims involve AI-generated evidence?
Average fraudulent claim value — varies by line of business
Detection rate — what percentage of deepfake fraud does the system catch?
Implementation cost — per-claim pricing × claims volume

A conservative estimate: if deepfake-enabled fraud represents even 1% of claims by value, and detection catches 80% of those, the return on per-claim detection costs is substantial. The board-level briefing on generative AI fraud provides detailed financial modeling for executive audiences.

32. Is it cheaper to just absorb the fraud losses?

In the short term, possibly — if AI-enabled fraud represents a small percentage of claims. But generative AI capability is improving rapidly and becoming more accessible. The percentage of fraudulent claims using AI-generated evidence is growing, not shrinking. Carriers that delay implementation face compounding losses as the threat escalates. Early adoption is both a risk mitigation strategy and an investment in competitive positioning.

33. Can we start with a pilot program?

Yes. Most carriers begin with a pilot focused on a specific line of business (typically motor or property claims) or a specific geography. Pilot programs typically run 8-12 weeks and provide data on detection rates, false positive rates, and integration effectiveness before full deployment.

Operational Questions

34. What media formats are supported?

Common image formats (JPEG, PNG, TIFF, WebP, HEIC), video formats (MP4, MOV, AVI, MKV), audio formats (MP3, WAV, AAC, M4A), and document formats (PDF, DOCX, scanned images). Essentially any media format commonly submitted with insurance claims.

35. Is there a file size limit?

Practical limits exist based on processing infrastructure, but they exceed what’s typical in claims submissions. Individual files up to several gigabytes (relevant for video evidence) can be processed. Batch processing handles large volumes of smaller files efficiently.

36. What about metadata-stripped media?

Many claims submissions arrive with metadata stripped by messaging platforms, email clients, or web portals. Detection systems must function without relying on metadata alone. Deetech’s detection works on the media content itself — pixel-level analysis, frequency domain properties, artifact patterns — and uses metadata as supplementary evidence when available, not as a requirement.

37. How do we handle a positive detection?

A positive detection (media flagged as potentially AI-generated or manipulated) triggers a defined workflow:

Automated flag — the claim is flagged in the claims system with the detection finding
Enhanced review — the adjuster reviews the flagged media alongside the detection report
SIU referral — if warranted, the claim is referred to the special investigations unit with the full forensic report
Investigation — SIU conducts their investigation using the detection findings as part of their evidence base
Decision — claim decision made based on the totality of evidence, including but not limited to the detection findings

38. What if the system flags a genuine photo?

False positives are handled through the escalation workflow. A flagged item receives enhanced analysis. If multiple detection models disagree, the item is treated as requiring human review rather than being automatically classified as fraudulent. The adjuster or SIU investigator makes the final determination. Genuine photos incorrectly flagged do not result in automatic claim denial.

39. Can detection distinguish between deliberate fraud and innocent image editing?

This is context-dependent. Detection identifies that media has been modified or AI-generated. It does not determine intent. A photo that has been edited could represent fraud (fabricating damage), innocent editing (cropping, brightness adjustment), or platform processing (automatic filters applied by social media apps). The detection report describes what was detected; the investigation determines whether it constitutes fraud.

40. How does this work with mobile claims apps?

Deetech’s API can be integrated into mobile claims applications, enabling real-time analysis of photos taken within the app. This has an additional advantage: media captured directly within the claims app includes fresh metadata and hasn’t been processed through third-party platforms, potentially improving both authenticity verification and detection accuracy.

Getting Started

41. What’s the first step for an insurer evaluating deepfake detection?

Assess your exposure — review your claims media submission processes and identify where AI-generated evidence could enter your workflow
Quantify the risk — use the board-level briefing framework to estimate potential losses
Evaluate tools — the top deepfake detection tools for insurance comparison provides a starting framework
Run a pilot — test with a representative sample of claims in a specific line of business
Deploy — roll out to production based on pilot findings

42. Where can I learn more about the deepfake fraud threat to insurance?

The State of Deepfake Fraud in Insurance 2026 — comprehensive overview of the current threat landscape
Board-Level Briefing on Generative AI Fraud — executive summary for leadership teams
Top Deepfake Detection Tools for Insurance — technology evaluation guide

For specific questions not covered here, contact the Deetech team directly.

To learn how deetech helps insurers detect deepfake fraud with purpose-built AI detection, visit our solutions page or request a demo.