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?

Sumsub’s 2024 Identity Fraud Report found global identity fraud rates rose from 1.10% in 2021 to 2.50% in 2024, and that detected deepfakes grew fourfold from 2023 to 2024. Deloitte’s Center for Financial Services projects that generative AI could push US fraud losses to US$40 billion by 2027. Insurance, with its reliance on photographic and documentary evidence, is directly exposed.

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

The most common types include AI-generated damage photos, manipulated genuine photos with exaggerated damage, AI-generated documents (repair quotes, medical certificates, invoices), recycled imagery from other incidents, and voice cloning to impersonate policyholders during phone-based claims.

3. Can insurance adjusters spot deepfakes visually?

Increasingly, no. A 2022 study published in Proceedings of the National Academy of Sciences found that participants identified AI-generated faces at about chance level (48%) and rated them as more trustworthy than real faces. A 2024 meta-analysis of 56 studies put average human deepfake-detection accuracy at about 55%, not reliably better than chance. 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 reports that its members detected A$560 million of opportunistic motor and property fraud in 2023, and estimates that roughly another A$400 million goes undetected each year. The 2022 floods, which generated about 246,000 claims, showed how catastrophe surge creates processing pressure in which fraudulent claims (including those with manipulated evidence) are more likely to succeed. ASIC has also reviewed how financial services licensees govern their use of AI.

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: artifact analysis (statistical patterns invisible to humans), metadata analysis (EXIF data, compression signatures), environmental consistency checks (lighting, shadows, reflections), provenance checks (Content Credentials, invisible watermarks and reverse image matching), and source attribution (estimating which generator family may have produced an image).

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, and generalization is the hard part: in the Deepfake-Eval-2024 study, the performance (AUC) of open-source detectors fell by 45–50% on real-world 2024 deepfakes compared with earlier academic benchmarks. The practical answer is to combine independent signals rather than rely on any single technique, and to evaluate any tool on media that looks like yours.

9. How accurate is deepfake detection on compressed claim photos?

Compression can remove or distort detector signals, so performance depends on the media distribution, model release, operating threshold, and compression level. Customers should evaluate representative claim media rather than rely on a universal accuracy figure.

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 account for both layers. deetech™‘s document analysis combines structural, arithmetic and fabrication checks with confidence-gated OCR and pixel forensics on supported scans. It looks for signs that a document was altered or fabricated; it does not judge whether the wording was written by AI, and it does not verify anyone’s identity.

11. Does deepfake detection work on video evidence?

Supported video can be submitted for analysis, subject to current file-format, size, plan, and provider limits. Results are decision-support signals and should be reviewed with the source media and other evidence.

12. Can voice cloning be detected?

Yes. Uploaded audio can be analyzed for synthetic-speech indicators, and live call detection is available on Enterprise plans. deetech™‘s audio detection ranked #1 of 23 systems on the Podonos audio deepfake benchmark (studio track), with 99.56% accuracy on 4,524 clips scored against labels only Podonos holds. Performance still varies by source quality and generation method, so results should be treated as review inputs rather than proof on their own. Read about the Podonos result.

13. How quickly can detection be performed?

Automated image screening often returns in seconds. Video, large documents, and deeper analysis can take several minutes and run asynchronously. Full forensic investigation can take minutes to hours depending on complexity, so claims workflows should treat deeper analysis as an asynchronous review step.

14. Do we need to analyze every insurance claim?

No universal screening policy is appropriate for every insurer. Organizations should choose risk-based or broader screening based on latency, cost, privacy, representative evaluation results, and human-review capacity.

Implementation

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

No. Deepfake detection complements claims fraud platforms such as Shift Technology and FRISS. Those platforms score claims, entities and networks, and some offer their own media modules; FRISS Media Check, for example, checks claim images and PDFs for manipulation, reuse and AI generation. deetech™ is a dedicated media-authenticity layer that sits alongside a fraud platform: it scores the claim, and we verify the media.

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

Through deetech™‘s REST API and signed webhooks. Your claims system (Guidewire ClaimCenter, Duck Creek Claims, Sapiens ClaimsPro or a custom platform) submits media to the API and receives results by webhook. Your team or an implementation partner builds and operates that integration, including automatic analysis on attachment and showing results inside the claim.

17. What systems does deetech™ integrate with?

Any claims or case system, through the REST API and webhooks
Guidewire, Duck Creek, Sapiens, Majesco and other claims platforms, through integrations built on that API
Google Drive and Dropbox, for importing files into the deetech™ library where enabled for your organization
On Enterprise plans: live call detection, and live meeting detection on Zoom, Microsoft Teams and Google Meet

18. How long does deepfake detection implementation take?

Implementation time depends on the customer’s systems, security review, workflow design, data handling, testing, and any customer-built integration. deetech does not publish a universal contract-to-production timeline. Enterprise plans can also be deployed on-premises, in a private environment, or air-gapped.

19. Do adjusters need training to use it?

The dashboard presents verdicts, confidence and supporting findings. Showing results inside another system requires a customer-built integration, and organizations remain responsible for training reviewers to understand limitations and escalation policy.

20. Can we run it on historical claims?

Yes. Batch analysis of historical claims media can surface previously paid claims whose media warrants a second look. That gives you a baseline of your exposure and, where investigation confirms fraud, a possible recovery opportunity. Batch requests take up to 20 files each on standard plans and up to 100 on Enterprise; images, audio and documents can be batched, and video is analyzed individually.

Accuracy and Reliability

21. What about false positives in deepfake detection?

False positives — genuine media incorrectly flagged as AI-generated — are a concern for any detection system. deetech™ is designed so that a flag starts a review rather than ending one:

Each result includes a verdict, a confidence level and the evidence behind it
Deep Scan adds deeper analysis for media that warrants a closer look
Organizations decide, in their own review policy, how confidence levels route to adjusters or the SIU

False-positive rates depend on the media distribution, model release, and operating threshold. deetech does not publish a universal false-positive rate; customers should measure it on representative data. A flag should trigger qualified review, not automatic denial. Benchmark results for image, video and document detection are available under NDA; ask us.

22. What about false negatives?

False negatives — AI-generated media that passes undetected — are the more dangerous error for insurers. No detection system eliminates them entirely. Combining independent signals (detector output, metadata, provenance and forensic checks) reduces the chance that a single blind spot turns into a missed fake.

23. How do deepfake detection models stay current?

Model releases are versioned operational changes reviewed against dated evaluation evidence. deetech does not promise a fixed continuous-update cadence or interruption-free upgrade for every release.

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

Any detector can miss a generation method it has never seen, so layered checks matter. Artifact analysis, metadata verification, provenance checks and environmental consistency fail in different ways, and a new generator rarely defeats all of them at once. Combined with human review and re-evaluation as new tools appear, this defense-in-depth approach limits how long a new technique goes unnoticed.

Legal and Regulatory

25. Is deepfake detection evidence admissible in court?

Courts and tribunals generally look for:

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

Admissibility is a legal determination that varies by jurisdiction and case. deetech reports document analysis outputs and limitations for qualified review; they do not guarantee admissibility or a particular legal outcome.

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

Deepfake detection findings should be considered alongside other evidence and investigation findings. They can strengthen the evidence base in a fraud investigation but should not be the sole basis for denying a claim.

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

APRA’s prudential standards (CPS 234 on information security, CPS 220 on risk management, and CPS 230 on operational risk management, in force since July 1, 2025) require insurers to manage information-security and operational risks, which in practice include AI-enabled fraud. ASIC’s REP 798 (October 2024) reviewed how financial services and credit licensees govern their use of AI. As of September 2026, neither regulator has issued a deepfake-specific detection mandate, but the 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 General 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 analyzes 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 credits. The free Pilot plan includes 50 credits on signup and 10 credits a month after that; Enterprise plans start from 100,000 credits a month on negotiated terms. A Scan uses 1 credit and a Deep Scan 2 credits, and audio and video are billed per started minute.

31. What’s the ROI of deepfake detection for insurers?

ROI depends on claim mix, fraud prevalence, review costs, operating thresholds, and confirmed outcomes. Customers should model value using their own representative data and investigation workflow rather than assumed catch-rate projections.

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

In the short term, possibly, if AI-enabled fraud is a small share of your claims. But generative AI tools are improving quickly and getting easier to use, so exposure is likely to grow. Carriers that wait may find themselves building defenses after losses have already mounted. Starting early lets you measure your exposure while the stakes are lower.

33. Can we start with a pilot program?

Yes. deetech™‘s free Pilot plan lets a team test the platform on its own media, and many carriers scope a first evaluation around one line of business (often motor or property claims) or one region. Agree the duration and success metrics up front (detection on known cases, false positives on known-genuine claims, turnaround and reviewer workload) before deciding on a wider rollout.

Operational Questions

34. What media formats does deepfake detection support?

The platform accepts documented image, video, audio, and document formats, with route-specific size and format limits. Check the current API documentation before integration; unsupported or undecodable media is rejected rather than treated as analyzed.

35. Is there a file size limit?

System ceilings are 20 MB for images, 50 MB for audio and documents, and 150 MB for video. Your plan’s effective limits can be lower and are returned by the authenticated configuration endpoint. Batch processing handles volumes of files that each remain within those limits.

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 with the detection finding (in your claims system, if you have integrated it)
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 can be re-examined with Deep Scan and reviewed alongside its supporting evidence, the original file and the rest of the claim. The adjuster or SIU investigator makes the final determination. A genuine photo that is incorrectly flagged should never, on its own, lead to a 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 called from a customer-built mobile workflow. The customer remains responsible for secure capture, consent, upload orchestration and result handling; direct capture may preserve useful metadata but does not guarantee authenticity or improved 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 see how deetech™ helps insurers and claims teams verify claims media, with detection built around claims workflows, visit our solutions page or request a demo.