AI Powered Verification: How Insurers Spot Forged Documents Before Payouts

April 2025 will be remembered in compliance circles for the Romarine scandal, when Norwegian regulators revealed that a shipping intermediary had issued counterfeit insurance certificates to Russian oil tankers (Business Insurance). The PDFs looked flawless, complete with cloned Financial Supervisory Authority letterheads, until a forensic review exposed copy-and-paste seals and pixel-level edits.

The incident confirmed what many insurers already suspected: manual reviews cannot keep pace with highly automated forgery tools. This article explains how to verify insurance documents with AI, why the problem has intensified in the past two years, and how claims and SIU teams can create an AI-first defence without overhauling their entire stack.

Why document forgery surged in 2025

Global synthetic document fraud rose by 195 percent between the first quarters of 2024 and 2025, and Europe recorded an even steeper 378 percent increase, according to recent industry data. Three forces drive the spike. First, generative models can now replicate logos, signatures and anti-copy patterns that once required a graphics professional. Second, low-cost online marketplaces offer deepfake kits as a service, lowering the barrier to entry for organised rings. Third, more insurers accept digital uploads from mobile apps, widening the attack surface. Fraud rings know that a single doctored proof of coverage can unlock six-figure payouts or allow illicit cargo to leave port, so they invest heavily in PDF and image manipulation.

Manual checks break, AI steps in

Large carriers still rely on adjusters to eyeball invoices, certificates and repair estimates. That approach worked when fakes were blurry scans, but today’s fabrications survive standard red-flag checks such as inconsistent fonts or mismatched dates. A recent internal audit at a European multiline carrier found that human reviewers missed 34 percent of forged endorsements containing AI-generated holograms. Catfish detection technology is now common in online dating; similar logic applies to insurance paperwork. Media forensics, the discipline behind catfish-scanner tools and deepfake detection workflows, analyses every pixel, every metadata field and every version-history entry. AI trained on millions of authentic and synthetic samples can flag subtle noise patterns or ghost layers invisible to the naked eye. When this capability is combined with cloud-security controls, files are examined in an isolated environment that prevents malicious code from entering the network.

Building an AI-first verification playbook

Insurers exploring automated document validation typically start small. The aim is to route low-risk claims straight through while escalating only dubious material. The architecture below balances speed with rigour.

• Pixel integrity analysis – convolutional neural networks locate localised edits such as cloned seals, copied signatures or inpainted policy numbers. This approach expands on techniques described in Vaarhaft’s post on detecting fake insurance claim images.
• Metadata and C2PA validation – AI reads embedded timestamps, GPS coordinates and cryptographic provenance markers and compares them with business rules. Our earlier article on C2PA explains why provenance alone is no silver bullet (read here).
• Duplicate and cross-claim search – a document fingerprint derived from perceptual hashing reveals whether the same certificate or invoice was reused in another claim.
• Contextual scoring – the outputs feed a risk engine that also ingests policy limits, claimant history and external watchlists. Low scores trigger automatic payment, medium scores trigger human review and high scores can trigger both SIU escalation and a SafeCam request for secure image recapture.

The Vaarhaft Fraud Scanner fits neatly into this stack. The web tool or API analyses an upload in seconds, produces a PDF report that highlights manipulated areas, extracts C2PA data and deletes the file immediately to remain fully GDPR compliant. When the score exceeds a defined threshold, SafeCam prompts the claimant to retake images in a controlled environment. The combination minimises false positives while blocking most deepfake documents at the perimeter.

Looking ahead: compliance, trust and next steps

The Romarine case shows that fake insurance certificates threaten not only payouts but entire supply chains. Incoming regulations, from the EU AI Act to stricter maritime oversight, will require carriers and brokers to demonstrate robust controls. By unifying pixel-level integrity analysis with metadata logic and secure recapture, AI verification turns a once ad hoc review into an auditable process. Insurers that move now can reduce manual workload, improve customer experience and build a reputation for uncompromising trust.

Explore how a within seconds AI authenticity check can secure your claims workflow. Contact our team for a short walkthrough of the Vaarhaft platform.