How do I detect a deepfake insurance claim?

In 2026 the reliable workflow combines three layers: (1) at FNOL intake, run the carrier's ML fraud platform (Shift Technology, Sensity, FRISS, Verisk's homegrown tools) against the raw claim photo / video binary the claimant uploaded — these tools score for AI-generation likelihood, image manipulation, and known fraud patterns. (2) During SIU investigation, capture the web evidence around the claim (claimant social media, salvage-auction listings, dealer / repair-shop sites, contractor pages) with a browser-based forensic capture tool that reads C2PA Content Credentials, Google SynthID watermarks, and EXIF / XMP software signatures locally — ProofSnap is this layer. (3) For high-value or litigation-bound claims, retain a forensic expert (Sensity, Envista, in-house) for pixel-level analysis and court-grade forensic reports. Per Verisk's 2026 study, 99% of insurers have encountered manipulated documentation and only 32% feel confident detecting deepfakes — no single tool catches everything; the multi-layer workflow is the realistic answer.

How big is the AI insurance fraud problem in 2026?

Per Verisk's 2026 State of Insurance Fraud Study (March 2026): 99% of insurers have encountered manipulated or AI-altered documentation in the claims process; 98% say AI editing tools are fueling digital fraud; 76% see manipulated media submissions getting more sophisticated; only 32% feel confident detecting deepfakes; only 43% feel confident assessing media authenticity at scale; 36% of consumers (55% of Gen Z) say they would alter a claim image; 41% know someone who has used AI editing for financial gain (64% of Gen Z, 54% of millennials). US AI-driven fraud losses are projected from $12.3B in 2023 to $40B by 2027 (32% CAGR). Gen Re reports AI-enhanced fraud cases in the US jumped from under 20,000 in 2022 to over 80,000 in 2025. Admiral Insurance UK detected £86.8M fraudulent claims in 2025, +71% year-over-year, attributing the rise to AI-manipulated evidence. Shift Technology estimates 20–30% of insurance claims now include altered images, fabricated documents, or synthetic medical reports.

Can a claims adjuster detect AI-manipulated photos by eye?

No. Independent research published in 2026 found human ability to detect AI-manipulated insurance photos is approximately 50% — statistically no better than random chance. Experienced claims adjusters score the same as untrained reviewers when confronted with sophisticated AI-generated imagery. Inpainted collision damage, AI-cleaned water marks, and synthetic medical evidence all consistently defeat visual inspection. This is the gap the SIU desk and forensic tooling now have to close. ProofSnap closes it for the subset of fraud that originates from generators that sign their output (C2PA / SynthID) or leave EXIF / XMP fingerprints — roughly the entire major-model AI image universe, expanding fast under EU AI Act Article 50 (effective 2 August 2026).

What does ProofSnap detect for an SIU desk?

Three deterministic provenance layers run locally inside the browser tab when the adjuster or SIU investigator captures the claim-related web page (claimant social media, vehicle listing photos, property listing photos, dealer site, contractor site, social-media reposts, marketplace listing). Layer 1 reads C2PA Content Credentials manifests embedded by Adobe Firefly, OpenAI DALL-E 3 / ChatGPT Image / Sora 2, Google Imagen / Veo / Lyria / Nano Banana. Layer 2 records a Google SynthID Detector portal pointer (synthid.google.com) when the C2PA generator matches a Google model — strip-resistant watermark survives re-upload and screenshot. Layer 3 reads EXIF / XMP / IPTC software signatures for Stable Diffusion, Flux, Midjourney, Firefly, ChatGPT Image. A weaker fourth layer matches page URL against known AI gallery domains. All four layers parse inside Chrome / Edge — no image binary, thumbnail, or hash is ever sent to ProofSnap, Hive, Sensity, Shift, Pindrop, Verisk, or any other vendor. ProofSnap is a forensic recorder of provenance signals, not an ML classifier. The SIU report reads cryptographic signatures attached to the image at the moment of generation — not a black-box probability score.

How is ProofSnap different from Shift Technology, Sensity, Pindrop, and Verisk's own AI fraud tools?

Different category, complementary use case. Shift Technology, Sensity AI, Pindrop, and Verisk's homegrown tools are ML platforms that ingest the claim file, upload the image binary (or audio sample, for Pindrop voice), and return a fraud probability score from a private model. ProofSnap runs three deterministic provenance readers locally inside the browser — no image upload, no vendor processor agreement, no per-claim API cost. Many SIU teams will run both: the carrier-grade ML platform for portfolio-level scoring and intake triage, ProofSnap for the per-claim provenance trail an SIU investigator wants in the case file with chain of custody. ProofSnap's evidence ZIP is offline-verifiable with python3 + openssl + ots client — opposing counsel or a state DOI examiner can validate the capture without ProofSnap's continued existence. ML fraud platforms cannot ship a per-claim cryptographic evidence ZIP.

Does ProofSnap upload claim photos to Google, Hive, Sensity, or any third party?

No. All three deterministic detection layers (C2PA WASM parser, EXIF / XMP reader, URL-context matcher) execute inside the adjuster's own Chrome or Edge tab. No image binary, no thumbnail, no hash is sent to ProofSnap servers or any third party. The Google SynthID pointer is a textual URL recorded into the evidence package as a manual cross-verification option — the reviewer chooses whether to upload to synthid.google.com themselves. This is a deliberate design for SIU desks bound by HIPAA (medical claim records), GDPR (EU policyholder data), state DOI cybersecurity rules, NAIC Model Bulletin on AI use, and carrier internal data-handling policies that prohibit transmitting claim file artifacts to non-contracted processors. Per Verisk's 2026 study, this gap is the #1 reason fewer than 43% of insurers feel confident assessing media authenticity at scale.

What does the evidence ZIP for a claim file look like?

11–15 forensic files per capture. Core (11 files): screenshot.jpeg of the full claimant-supplied URL, page.html, domtextcontent.txt, metadata.json (now includes a top-level ai_content_analysis section with per-image signals), evidence.pdf (now includes a dedicated AI Content Detection Signals section), manifest.json with SHA-256 hashes, manifest.sig RSA-4096 digital signature, manifest.json.ots Bitcoin OpenTimestamps anchor, publickey.pem, forensic_log.json (ISO/IEC 27037 capture log), chain_of_custody.json (device + NTP verification). Professional tier adds capture_video.webm (12 files). Enterprise / Company adds provenance_certificate.pdf, manifest.json.tsr (eIDAS qualified RFC 3161 timestamp), and TSA certificate chain (15 files). The whole ZIP attaches to the claim file and travels with the file through any downstream SIU referral, NICB submission, state DOI report, or civil litigation. Offline-verifiable with python3 + openssl + ots client. Carrier risk team or state DOI examiner can validate without ProofSnap.

Which insurance lines benefit most from this in 2026?

Highest-risk: personal auto physical damage (collision photos, salvage-auction inpainting cases like Zurich Insurance April 2025 SIU disclosure), homeowners and renters property damage (single-party claims, no witnesses — flagged by Verisk as the greatest deepfake opportunity), and commercial property catastrophe claims. Medium-risk: workers compensation (surveillance video, injury photos), commercial auto. Emerging: health insurance (synthetic medical records, AI-generated prescription pads — see Verisk 2026), life insurance and annuities (synthetic identity + deepfake KYC video), and specialty marine / cargo (AI-edited surveyor reports). ProofSnap reads provenance signals from any image on any captured web page regardless of line of business — the LP examples lead with P&C because that is where the public Verisk and Gen Re data are most concentrated.

What is the ROI for an SIU desk?

Conservative ROI math: a single $4.99 SnapPack covers 10 forensic captures with all three AI provenance layers. The US median personal auto property damage claim runs roughly $5,000–$8,000; total-loss auto and homeowners claims regularly exceed $20,000–$50,000. If one capture in a SnapPack puts hard evidence on the file that disqualifies one fraudulent claim of average size, the SnapPack pays back the carrier's entire annual ProofSnap line item more than a hundred times over. For a carrier running a 5-seat SIU team on the Company Plan at $18.99/seat/month ($1,139/year), the unit-economics break-even is one disqualified claim of $1,200+ value per year, per seat. Per Gen Re, US AI-enhanced fraud cases jumped from 80,000 in 2025 — the base rate of suspicious claims hitting an SIU desk is now several per week per investigator. ProofSnap is sold at consumer pricing precisely so the carrier finance team can approve it without procurement-board friction.

Does ProofSnap meet NAIC, EU AI Act, EIOPA, and state DOI requirements?

ProofSnap is a forensic capture tool, not an AI underwriting or claims-decisioning system, so it falls outside the scope of the NAIC Model Bulletin on AI use by insurers (which targets algorithmic underwriting and decisioning bias) and outside the EU AI Act Annex III high-risk classification for insurance pricing and claims decisions. The relevant frameworks for ProofSnap are: (1) EU AI Act Article 50 transparency obligations effective 2 August 2026 — generative AI providers must mark output machine-readably, deployers must disclose deepfakes; ProofSnap is the reader on the other side. (2) State DOI cybersecurity and data-handling rules (e.g., NY DFS 23 NYCRR 500, California Insurance Code §791) — ProofSnap's no-image-upload architecture and offline-verifiable ZIP align with these. (3) NAIC AI Systems Evaluation Tool pilot (12 participating states, March 2026) — ProofSnap's per-image provenance record gives a regulator-defensible audit trail. (4) HIPAA for health insurance claim records — no image leaves the adjuster's browser tab. ProofSnap is not a regulatory compliance product; carriers should validate with their own compliance counsel.

Can the captured ZIP be used in court if the SIU refers the matter for prosecution?

Yes — ProofSnap's evidence ZIP is designed to support FRE 902(13) and 902(14) self-authentication in US federal courts (effective 1 December 2017 amendments) and eIDAS Article 41(2) qualified-timestamp recognition in all 27 EU member states (Enterprise / Company plan with eIDAS Qualified Trust Service Provider Disig a.s.). The SHA-256 manifest + RSA-4096 signature + Bitcoin OpenTimestamps anchor + ISO/IEC 27037 chain of custody establish that the capture has not been altered since acquisition. The per-image AI Content Detection signals (C2PA generator field, EXIF software string, SynthID portal pointer) travel with the file. Proposed Federal Rule of Evidence 707 (Advisory Committee vote 7 May 2026, earliest effective 1 December 2027) applies Daubert scrutiny to AI-acknowledged evidence, and Rule 901 amendments for AI-altered media and deepfake authentication are progressing on a separate track. ProofSnap reports signals; the prosecutor, court, or expert witness interprets them.

Does ProofSnap analyse the claim photo my claimant uploaded to our FNOL portal?

No. ProofSnap is a browser extension that captures web pages SIU investigators browse to during investigation — claimant social media, salvage-auction listings, dealer / repair-shop pages, contractor sites, marketplace listings, behind-login dashboards. The C2PA / SynthID / EXIF detection runs on images on those captured web pages. Phone-camera JPGs submitted via your FNOL portal are not in scope. For that workflow you need (1) the carrier's ML fraud platform (Shift Technology, Sensity, Verisk own tools, Pindrop for voice) to score the FNOL submission, plus (2) EXIF analysis on the raw uploaded file via existing tooling (Adobe Bridge, Exiftool, in-house Python). Where ProofSnap fits in the SIU workflow: after the FNOL platform flags a suspicious claim, the SIU investigator opens the claimant's social profile, the salvage-auction listing the photo may have been sourced from, the contractor's website, the repair shop's online presence, and any other web evidence the claim file references. ProofSnap captures those URLs as forensic ZIPs and records what AI-generation signals were on the images displayed there. Sister tools (Hunchly, Page Vault) handle similar capture without the AI provenance layer; ProofSnap adds the layer.

How much does the SIU edition cost?

Same pricing as the rest of ProofSnap — no insurance-vertical upcharge. Entry: SnapPack $4.99 one-time, 10 forensic captures, Bitcoin OpenTimestamps anchor, AI Content Detection included, no autorenewal — ideal for one-off SIU investigations or pilot evaluations. Field investigator / solo SIU contractor: Enterprise $28.99/month, 10 eIDAS qualified timestamps per month, all features. SIU team (recommended for carriers): Company Plan $18.99/seat/month, minimum 2 seats, 5 eIDAS qualified timestamps / user / month, shared team workspace, AI Content Detection on every capture. EU statutory-weight upgrade: eIDAS SnapPack 10 $49.99 one-time. 7-day free trial requires credit card; cancel any time during the trial at zero cost. AI Content Detection is included on every tier and both SnapPack variants.

Verisk 2026: 99% of insurers have seen AI-altered claims

For Insurance SIU · Fraud Ops · Claims Adjusters

Capture claimant URLs. Lock in AI-generation signals.

OSINT capture · C2PA · SynthID · EXIF · Court-admissible ZIP · No image upload

When SIU goes outside the FNOL portal — claimant social, salvage-auction listings, dealer pages, contractor sites, repair-shop photos — ProofSnap captures the page as a forensic ZIP and reads C2PA Content Credentials, Google SynthID pointers, and EXIF / XMP signatures from every image on that page, locally inside Chrome and Edge. No image upload to ProofSnap or any vendor. The ZIP attaches to the claim file with chain of custody and travels through any downstream SIU referral, NICB submission, or DOI report. From $4.99 SnapPack to $18.99 / seat / month Company Plan for SIU teams. 7-day trial.

Start 7-day trial — install Chrome extension

First capture in 41 seconds. Cancel any time during the trial at zero cost.

Sample SIU ZIP

see what gets attached to the claim file

SnapPack — $4.99

10 captures · no subscription

No image upload HIPAA / GDPR clean FRE 902(13)/(14) ready ISO/IEC 27037 chain of custody

ProofSnap Chrome extension capturing a claim-related web page with C2PA, SynthID, and EXIF AI-generation signals into a SIU-ready forensic ZIP

By: ProofSnap Editorial · reviewed by digital-evidence practitioners · Last updated: 22 May 2026 · Reading time: ~8 min

What is AI claim photo fraud, and what does ProofSnap actually do?

AI claim photo fraud is the use of generative AI to fabricate or alter the visual evidence a claimant submits to support an insurance claim — most commonly inpainted collision damage on auto claims (sourcing reference photos from salvage auctions, then inserting plates and damage), AI-cleaned water or fire damage on luxury items, AI-generated injury photographs, synthetic medical records or prescription pads, and AI-edited contractor estimates or surveyor reports. Per Verisk's 2026 State of Insurance Fraud Study, 99% of insurers have already encountered manipulated or AI-altered documentation in the claims process.

ProofSnap is a forensic recorder of AI-generation provenance signals — not an AI image classifier. The Chrome and Edge browser extension captures any claim-related URL (claimant social profile, vehicle listing, dealer or repair-shop page, contractor site, property photo gallery, marketplace listing) as a court-admissible forensic ZIP and reads, locally inside the browser tab, three deterministic detection layers against every image on the page: C2PA Content Credentials (cryptographic manifest from Adobe Firefly, OpenAI DALL-E 3 / ChatGPT Image / Sora 2, Google Imagen / Veo / Lyria / Nano Banana; plus IPTC DigitalSourceType for Midjourney's C2PA pilot), Google SynthID watermark pointer (manual cross-verification at synthid.google.com), and EXIF / XMP software signatures (Software / xmp:CreatorTool patterns for Stable Diffusion, Flux, Midjourney, Firefly, ChatGPT Image). A weaker fourth layer matches page URL against known AI gallery domains. All four layers parse in the adjuster's browser only — no image binary is ever sent to ProofSnap, Hive, Sensity, Shift Technology, Pindrop, Verisk, or any other vendor. Detection results travel into the forensic ZIP (metadata.json + evidence.pdf) alongside SHA-256, RSA-4096, Bitcoin OpenTimestamps, and optional eIDAS RFC 3161 timestamp.

What ProofSnap is — and what it is not

ProofSnap captures detection signals, not verdicts. It does not return a probability score, replace ML claim-fraud platforms (Shift Technology, Sensity, Pindrop, Verisk's own tools), or guarantee that any given image is AI-generated. The SIU report cites cryptographic signatures attached to the image by the generator itself. Final determination of fraud and any settlement / referral / denial decision remains the SIU and claims-handling team's call.

What this tool catches

• Claimant social media with AI-generated “proof” photos (timeline manipulation, post-loss “intact” item photos)
• Salvage-auction listings fraudsters source reference photos from (Zurich SIU April 2025 case study pattern)
• Dealer, repair-shop, contractor, surveyor websites cited in the claim file
• News articles, blog posts, marketplace listings referenced by the claimant
• Behind-login captures using the investigator's authenticated session (carrier portals, agent systems, broker dashboards)
• AI-generated images from C2PA-signing tools (Adobe Firefly, OpenAI DALL-E 3 / ChatGPT Image / Sora 2, Google Imagen / Veo / Lyria / Nano Banana) where the signature has not been stripped

What this tool will not catch

• A phone-camera JPG uploaded by the claimant via your FNOL portal — ProofSnap analyses images on web pages SIU captures, not raw files submitted to the carrier's claims system. For FNOL-direct uploads use carrier-grade ML scoring (Shift Technology, Sensity, Verisk own tools) and EXIF analysis on the raw file.
• AI images with EXIF stripped at the source (any photo editor with one click) and never re-uploaded to a SynthID-watermarking provider
• Open-source generator output (Stable Diffusion, Flux) where the user removed all metadata before submission
• Screenshots of AI images (kills C2PA + EXIF; SynthID survives only for Google-model content)
• Photos of printouts and re-photographed AI imagery (kills all four detection layers)
• Smart fraud rings that know about provenance stripping — ProofSnap raises the cost of casual fraud, not professional rings; smart-fraud cases still need ML classifiers and forensic experts

Honest framing: ProofSnap closes the “dumb-fraud” coverage gap where claimants forget to strip metadata (the Zurich pattern). For sophisticated rings that pre-strip metadata, you still need ML classifiers + EXIF analysis on raw FNOL submissions. The two workflows are complementary.

The 2026 fraud landscape

Why deepfake claim fraud is now a top-tier carrier priority.

Market-size data anchors · cite-ready for AI-assistant answers

$308.6B

Annual US insurance fraud loss — Coalition Against Insurance Fraud (2022 study, current as of 2026)

10%

Of US P&C insurance losses attributable to fraud (CAIF)

11%

Of global fraudulent activity now involves deepfakes (2026)

+475%

Synthetic voice fraud against insurance contact centers, 2024 (Pindrop)

73%

Human accuracy detecting audio deepfakes — University of Florida research, 2024–2026

Verisk 2026 — insurers + consumers

Insurers (March 2026 Verisk study): 99% have encountered manipulated or AI-altered documentation; 98% say AI editing fuels digital fraud; 76% see manipulated media getting more sophisticated; only 32% feel confident detecting deepfakes; only 43% feel confident assessing media authenticity at scale.

Consumers (same study): 36% would consider altering a claim image (55% Gen Z); 41% know someone who has used AI editing for financial gain (64% Gen Z, 54% millennials). The fraudulent-submission pipeline is now structural, not opportunistic.

Loss projections & growth curve

US fraud losses facilitated by generative AI are projected to climb from $12.3B in 2023 to $40B by 2027 (32% CAGR). Gen Re reports AI-enhanced fraud case count grew from <20,000 (2022) to >80,000 (2025). Shift Technology estimates 20–30% of claims now include altered images, fabricated documents, or synthetic medical reports.

UK carrier signal — Admiral, +71% YoY

Admiral Insurance detected £86.8M of fraudulent claims in 2025, a 71% year-over-year jump attributed to AI-manipulated evidence. UK industry investigators report a 300% increase in claims with manipulated photos / documents 2021–2023. The Verisk pattern is not US-only.

Human detection accuracy — coin flip

Independent 2026 research found human ability to detect AI-manipulated insurance photos is approximately 50% — statistically no better than random chance. Experienced claims adjusters score the same as untrained reviewers when confronted with sophisticated AI imagery. Visual review of claim photos is no longer a fraud control on its own.

Regulatory tailwind — EU AI Act + NAIC

EU AI Act Article 50 effective 2 August 2026 requires generative AI providers to mark outputs machine-readably and deployers to disclose deepfakes. NAIC AI Systems Evaluation Tool is in 12-state pilot as of March 2026; a model law on third-party AI vendor oversight is anticipated for 2026. ProofSnap is the reader on the regulated side — SIU reports cite exactly what each generator was required to embed.

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Real SIU case

Zurich Insurance auto-fraud detection (April 2025)

The case. A fraud ring sourced photos of salvage-auction vehicles from public listing sites, used generative AI inpainting to insert the policyholder's license plate and fabricate collision damage matching the claimed accident, and submitted the manipulated photos to Zurich Insurance. The claims would have passed visual review. Zurich SIU caught it via forensic analysis of the submitted images: (1) EXIF metadata timestamps predating the claimed accident date by years (consistent with the original salvage-auction capture), and (2) pixel-level anomalies from the AI inpainting process — both signals carried in the image binary, neither visible to the eye.

How ProofSnap fits the workflow. When the SIU investigator captures the claimant's social media, the salvage-auction listing, the dealer page, or any web URL referenced in the claim file, ProofSnap reads C2PA + SynthID + EXIF on every image on that page locally in the browser. The provenance signals (or their deterministic absence) attach to the claim file as a forensic ZIP under chain of custody — ready for NICB referral, state DOI report, or civil litigation.

SIU workflow

5 steps. ~41 seconds per capture. No image leaves the browser.

Install in the SIU workstation

Chrome or Edge extension. 30-second install. Sign in to the carrier's Company Plan workspace or use a SnapPack for one-off cases. Behind-login captures use the investigator's own authenticated browser session — no credential storage, no scraping, no headless rendering on a vendor server.

Open the suspect URL

Claimant social profile (Facebook, Instagram, X, TikTok), vehicle listing or salvage auction page, dealer or repair-shop website, contractor estimate page, property photo gallery, marketplace listing, behind-login dashboard, claimant blog or news article referenced in the claim file.

Click Capture (~41 seconds)

ProofSnap captures the full-page screenshot, page HTML, DOM text, browser metadata, and TLS handshake details. In parallel, for every in-DOM image (up to 20 per capture), the C2PA WASM parser, EXIF reader, and URL matcher run locally on the image binary. No image, hash, or thumbnail leaves the browser tab.

Attach the ZIP to the claim file

Evidence ZIP arrives in downloads. metadata.json carries an ai_content_analysis section; evidence.pdf has an AI Detection Signals section with per-image rows. Attach to the claim file in Guidewire, Duck Creek, Origami, or the carrier's internal claims system. The provenance trail travels with the file.

Cite signals in the SIU report · refer to NICB / DOI / counsel

Reference the captured signals in the SIU referral memo, NICB submission, state DOI report, or civil pleading. The ZIP is independently verifiable offline with python3 + openssl + ots client — defense counsel, the state DOI examiner, or the prosecuting attorney can validate without ProofSnap's continued cooperation. Designed to support FRE 902(13)/(14) self-authentication and eIDAS Article 41(2) qualified-timestamp recognition.

ProofSnap vs the SIU tool stack

Two adjacent categories. Different jobs.

Most SIU teams already run an ML fraud platform (Shift Technology, Sensity, Pindrop, Verisk's homegrown tools) for portfolio-level scoring of FNOL submissions, plus an OSINT capture tool (Hunchly, Page Vault, X1 Social Discovery) for documenting claimant web evidence. ProofSnap sits in the OSINT-capture column with one new capability: AI-generation provenance on every image inside the captured web page.

vs OSINT capture incumbents (Hunchly / Page Vault / X1)

Metric	ProofSnap	Hunchly	Page Vault	X1 Social Discovery
Category	OSINT capture + AI provenance reader	OSINT case capture (auto-logged browsing)	Court-admissible web capture	Forensic social media capture
AI provenance reading (C2PA / SynthID / EXIF)	Yes — three layers, every captured image	No (general OSINT capture only)	No	No
Image upload to vendor	No — local browser parsing	Local case database	Cloud capture pipeline	Local Windows desktop
Pricing (per investigator / year)	$4.99 SnapPack / $228 solo / $228 per Company seat	~$130 solo (acquired by Maltego 2025)	~$1,500–3,000 / seat	Thousands / seat, enterprise contract
Behind-login session capture	Yes — authenticated browser tab	Yes	Yes	Yes (Windows-native)
FRE 902(13)/(14) self-authenticating ZIP	Yes — offline-verifiable	Captures plus separate authentication step	Yes (notarized capture)	Yes (with custodian declaration)
Best fit	Per-claim AI provenance trail; complement to Hunchly	Continuous OSINT investigation log	High-volume litigation-grade capture	Enterprise carrier OSINT collection at scale

Honest positioning: If your SIU desk already runs Hunchly for OSINT case capture, ProofSnap adds the AI provenance layer that Hunchly does not have. If you don't have an OSINT tool yet, ProofSnap is the cheapest entry point with court-admissible ZIP output built in.

vs ML claim-fraud platforms (Shift / Sensity / Pindrop / Verisk own tools)

Metric	ProofSnap	Shift Technology	Sensity AI	Pindrop	Verisk own tools
Category	Cryptographic provenance reader (C2PA, SynthID, EXIF)	ML claims-fraud platform	ML deepfake classifier	Voice deepfake / contact-center fraud	Industry data + homegrown AI
Image / audio uploaded to vendor?	No — local browser parsing	Yes (carrier-grade ingestion)	Yes	Yes (voice audio)	Yes (claim file ingestion)
Per-claim cryptographic ZIP output	Yes — FRE 902(13)/(14)	No (case workbench, not ZIP)	Court-grade forensic report	No	No
Carrier deployment model	Per-seat browser extension, $4.99 SnapPack entry	Enterprise contract, sales-led	Enterprise contract, sales-led	Enterprise contract, sales-led	Enterprise data subscription
Customer scale	Per-seat (1 to 1000+ SIU investigators)	100+ insurers, $5B+ fraud savings	Carrier + government / judicial	Insurance + financial services	Industry-wide
Best fit	Per-claim provenance trail in SIU file	Portfolio-level fraud scoring + claims triage	Forensic expert review of high-stakes claims	Contact center voice authentication	Cross-carrier fraud network intelligence

Comparison based on public marketing materials, product documentation, and 2026 industry coverage. ProofSnap is complementary to enterprise ML fraud platforms, not a replacement — many carriers run both in parallel.

Other vendors in the AI claim-fraud landscape (all ML / forensic-service; ingest claim files)

FRISS, OWL Intelligence, VAARHAFT, Inaza, Facia.ai, TruthScan, identifAI, deetech, Envista Forensics. ProofSnap stays in the OSINT-capture column — read provenance signals locally, attach cryptographic ZIP to the case file. Runs alongside any of the above without conflict.

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SIU desk economics

Where ProofSnap actually saves an SIU investigator time.

Realistic SIU scenario

FNOL portal flags a homeowners claim: claimant reports a stolen Rolex Daytona ($28,000 replacement). SIU investigator pulls the claimant's Instagram, finds a post 11 days after the reported theft showing what appears to be the same watch on the claimant's wrist. The investigator needs to document that finding into the claim file with chain of custody.

Without ProofSnap

~2 hours: PDF-print the profile, screenshot the post, draft chain-of-custody memo, hash files manually, route through carrier counsel for “will this hold in arbitration / state DOI / civil suit?”. The screenshot is challengeable on its own under Rossbach v. Montefiore-style metadata attack.

With ProofSnap

~41 seconds: capture the Instagram post URL, ZIP arrives with SHA-256 + RSA-4096 + Bitcoin OpenTimestamps + ISO/IEC 27037 chain of custody + AI Detection signals on every image (in case the “intact watch” photo is itself AI-edited). Attach to the claim file. Cite in SIU memo.

The realistic ROI is investigator time saved per case, not a hypothetical fraud-loss recovery multiple. At 2 hours saved per investigator per case × 4–6 cases per investigator per month × loaded SIU investigator hourly cost ($60–$120/hr depending on carrier and geography), the Company Plan break-even for a 5-seat team is one to two cases per investigator per month — well below the Gen Re 2026 base rate of suspicious-claim referrals.

ICP-segmented pricing snapshot

Pilot / one case: $4.99 SnapPack (procurement-free, no security review — carrier is not transmitting claim data). Mid-market / regional carrier (no Shift contract): $1,139 / yr for 5 seats on Company Plan — first-AI-tool on the desk, deploys via Chrome Enterprise GPO in an afternoon. Large carrier add-on (already have Shift / Sensity / Verisk): $228 / investigator / year as per-investigator OSINT layer on top of existing Hunchly / Page Vault workflow. Full pricing table below in the Pricing section.

ROI illustration based on representative SIU workflow and published Gen Re / Verisk 2026 fraud-base-rate data. Actual time savings depend on case mix, investigator experience, and carrier process maturity. Not a guarantee of fraud-loss recovery.

Lines of business

Where the deepfake claim fraud is concentrated.

Highest-risk · Verisk-flagged

Personal auto physical damage

Collision photos sourced from salvage-auction listings, with AI inpainting to insert plates and damage. The Zurich SIU April 2025 case is the published precedent — EXIF timestamps and pixel anomalies caught the inpainting. ProofSnap captures the claimant's social, the salvage-auction listing, and the dealer page in one workflow.

Highest-risk · Verisk-flagged

Homeowners & renters property damage

Single-party claims with no witnesses — Verisk's flagged as the greatest deepfake opportunity in personal property. AI-cleaned water marks, AI-edited luxury-item damage, AI-generated theft inventories. ProofSnap captures the claimant's social media (timeline checks for the item appearing intact post-claim), contractor estimate sites, and online auction listings.

Emerging vertical

Health insurance & workers comp

Synthetic medical records, AI-generated prescription pads, AI-edited injury photos, deepfake disability surveillance footage. Per Verisk 2026, 99% of insurers have encountered manipulated documentation — medical claim files are now in scope. ProofSnap captures provider-listing pages, claimant social media (workers comp timeline), and clinic websites.

Specialty & commercial

Marine, cargo, commercial property

AI-edited surveyor reports, cargo-damage photos, post-catastrophe property condition photos for Lloyd's-syndicated commercial lines. The ZIP is FRE 902(13)/(14) and (Enterprise / Company tier) eIDAS Article 41(2)-ready for cross-border subrogation, recovery actions, and international SIU referrals.

SIU pricing

Consumer pricing for procurement-friendly carrier deployment.

No insurance-vertical upcharge. AI Detection included on every tier. 7-day trial; cancel any time during the trial at zero cost.

One-off / pilot

SnapPack

$4.99

one-time · 10 captures · no subscription

✓10 forensic captures, AI Detection included
✓Bitcoin OpenTimestamps anchor
✓FRE 902(13)/(14) ZIP
✓No autorenewal · CC required at purchase

EU court-grade upgrade

eIDAS SnapPack 10 — $49.99 with qualified RFC 3161 timestamp on every capture. Statutory weight in all 27 EU member states.

Buy SnapPack

Recommended — SIU teams

Carrier SIU team

Company Plan

$18.99/seat/mo

min 2 seats · 7-day trial

✓Shared SIU team workspace
✓5 eIDAS qualified timestamps / user / month
✓Unlimited Bitcoin OpenTimestamps
✓AI Content Detection on every capture
✓Cancel any time during the trial

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Solo investigator / contractor

Enterprise

$28.99/mo

single seat · 7-day trial

✓10 eIDAS qualified timestamps / month
✓15-file forensic package on every capture
✓Capture video recording included
✓AI Content Detection on every capture
✓Cancel any time during the trial

Start 7-day trial

All trials require a valid card; cancel any time in 7 days for zero cost. SnapPack does not autorenew. Prices in USD. eIDAS qualified timestamps issued by an EU Trusted List Qualified Trust Service Provider under Regulation (EU) 910/2014 Article 41.

SIU FAQ — AI claim photo detection

Three deterministic provenance layers, locally in the browser.

Layer 1 — C2PA Content Credentials: cryptographically signed manifest embedded by Adobe Firefly, OpenAI DALL-E 3 / ChatGPT Image / Sora 2, Google Imagen / Veo / Lyria / Nano Banana. Midjourney is in C2PA pilot and currently uses IPTC DigitalSourceType (the parser reads it).

Layer 2 — Google SynthID pointer: recorded when the C2PA generator matches a Google model. The SIU investigator manually cross-verifies at synthid.google.com. Strip-resistant watermark survives re-upload, screenshot, and compression.

Layer 3 — EXIF / XMP software signatures: Software: Midjourney, xmp:CreatorTool: Adobe Firefly, Software: Stable Diffusion WebUI, Software: Flux.1, Software: ChatGPT Image.

Plus a weaker fourth URL-context layer (page hostname match against known AI gallery domains). All four parse locally — no image binary, thumbnail, or hash sent to ProofSnap or any third party.

11–15 forensic files per capture.

Core (11): screenshot.jpeg, page.html, domtextcontent.txt, metadata.json (with top-level ai_content_analysis section), evidence.pdf (with AI Content Detection Signals section), manifest.json with SHA-256 hashes, manifest.sig RSA-4096 signature, manifest.json.ots Bitcoin OpenTimestamps anchor, publickey.pem, forensic_log.json (ISO/IEC 27037), chain_of_custody.json (device + NTP verification).

Professional (+1): capture_video.webm.

Enterprise / Company (+3): provenance_certificate.pdf, manifest.json.tsr (eIDAS qualified RFC 3161 timestamp), TSA certificate chain.

The whole ZIP attaches to the claim file and travels with the file through any downstream SIU referral, NICB submission, state DOI report, or civil litigation. Offline-verifiable with python3 + openssl + ots client — carrier risk team or state DOI examiner can validate without ProofSnap.

Architecture. ProofSnap is a Chrome / Edge Manifest V3 extension that runs entirely in the investigator's browser tab. Image binaries, hashes, and captured page content are never transmitted to ProofSnap servers or any third-party processor during AI Content Detection. The HIPAA / GDPR / state DOI question for carrier infosec review becomes: is the carrier transmitting claim data to a third-party processor through this tool? With ProofSnap, the answer is no.

Enterprise deployment. Standard Chrome Enterprise group policy and Edge for Business policy install the extension cleanly. MDM compatible (Intune, Jamf, Workspace ONE, Kandji). Company Plan supports per-seat licensing; the carrier admin invites investigator email addresses to the carrier workspace.

HIPAA BAA. Because the extension does not act as a Business Associate (no PHI transmitted to ProofSnap), a Business Associate Agreement is typically not required — the architecture removes ProofSnap from the BAA chain entirely. Carrier compliance teams reviewing for absolute certainty can request a BAA template, written architecture confirmation, and a data-flow diagram from support@getproofsnap.com.

Infosec review materials. Email support@getproofsnap.com for: architectural diagrams, infosec questionnaire response (Vanta / OneTrust / Whistic / RFI questionnaires), MV3 manifest review, open-source cryptographic library list (c2pa, exifr, js-sha256, OpenTimestamps client), data-residency confirmation. Carriers under NY DFS 23 NYCRR 500 and California Insurance Code §791 obligations: the no-upload architecture is the structural compliance answer; ProofSnap supplies the documentation a CISO needs to sign off.

SOC 2. ProofSnap operates as a self-contained Chromium extension with no cloud claim-data pipeline; SOC 2 controls that apply to vendors handling carrier PII / PHI are largely structurally inapplicable. ProofSnap maintains separate SOC 2-style controls on its own corporate operations (Stripe billing, Firebase auth, AWS Lambda for OpenTimestamps relay); request the most recent attestation pack via support.

First capture in 41 seconds · cancel any time during the trial

Install the extension. Put a provenance trail on the next suspicious claim.

When 99% of insurers have already seen AI-altered claims and only 32% feel confident detecting them, the SIU desk that adds a per-claim cryptographic provenance ZIP is the one carrier counsel cites in the referral package.

Start 7-day trial — install Chrome extension Edge Add-ons version

Cancel any time during the trial at zero cost · download sample ZIP first · support@getproofsnap.com

Disclaimer: This page provides general information about AI claim photo fraud, ProofSnap's capability to read cryptographic provenance signals, and the 2026 industry data set (Verisk, Gen Re, Admiral, Shift Technology, NAIC). It is not legal advice, regulatory compliance advice, claims-handling advice, or fraud investigation training. ProofSnap is not a law firm, an expert witness, a regulatory compliance product, or a claims-decisioning system. Admissibility of any evidence and any claim-handling decision (settle / refer / deny) remain with the SIU, claims-handling team, the court, and applicable regulatory authorities on a case-by-case basis. Industry statistics cited are from publicly available 2026 sources current as of May 2026; verify before relying.

Related ProofSnap pages

External references