AI Can Fake Your Paper Trail: What Gets Forged and How To Stop It

Could a single AI prompt cost your company six figures? In a widely reported case, a finance team wired millions after a convincing deepfake video call (Financial Times). That headline was a wake up call for every back office and risk team that still trusts screenshots and PDFs at face value. The same technologies behind lifelike faces now generate equally lifelike documents. This article answers a practical question that compliance, HR and procurement leaders ask every week: which documents can be faked with generative AI, how are such fakes created so easily and how can companies build document fraud detection into digital processes without slowing the business.

Which documents can be faked by generative AI?

By industry

• HR and Business: sick notes and medical certificates, leave approvals, diplomas and certificates, professional licenses, quality certificates, bills of lading, procurement forms, invoices and delivery notes.
• Banking and Finance: income statements and payslips, rental leases, work contracts, bank statements, proof of address and employment letters.
• Insurance: invoices, repair estimates, medical invoices and supporting photos attached to claims.
• E commerce and procurement: invoices, order confirmations, delivery slips and supplier onboarding documents.

Why these documents are attractive targets

Fraudsters favor documents that trigger money movement or access. A single invoice update can redirect payment. A forged payslip can unlock a loan or rental. A doctored sick note can pass HR checks. Generative AI lowers effort and raises believability, which is why forged invoices and synthetic identities are rising across channels.

How are documents faked? Tools and typical fraud workflows

The common toolchain

Modern forgery looks like a simple creative workflow. Large language models draft the content of a work contract or invoice line items. Image generators and editors create or alter the page layout, letterheads, signatures and stamps. Publicly discussed tools like NanoBanana style models, Adobe Firefly and OpenAI image APIs make this accessible to non experts. The output is a tidy PDF or PNG that passes a quick human glance. Attackers often strip or spoof metadata to hide editing history and they reuse logos from the web at convincing resolutions.

Typical attack patterns

Invoice fraud is the most visible pattern. The attacker clones a known supplier template, edits bank details and sends the PDF in a hijacked email thread. In HR, a fake sick note or medical certificate is assembled from a stock template with hospital logos and a generated signature. In lending and tenant screening, forged payslips and bank statements support a synthetic identity, which several industry reports flag as a fast growing threat (TransUnion).

Modifiers that raise success rates

Fraudsters combine document forgery with channel tricks. They spoof domains to deliver the forgery in a believable thread. They embed QR codes or clickable remittance notes that point to attacker controlled portals. They balance fonts and kerning to match real templates and adjust compression levels to mimic office scanners. The goal is simple: look ordinary and bypass manual checks.

How companies can detect fake documents and stop fraud

Automated media analysis inside your workflows

The fastest win is to scan images and documents at upload. Effective pipelines detect AI generation, flag editing traces from common tools, analyze metadata, extract and validate C2PA content credentials, run reverse image search for reused assets and perform duplicate checking across submissions. These document fraud detection steps help detect fake sick reports, fake income reports, fake rental leases and fake work contracts before they reach payment or onboarding. When evidence is inconclusive, trigger a controlled re capture step rather than escalating to slow manual review.

Two building blocks work well together in practice. First, a forensic analyzer that produces a clear, audit friendly report suitable for SIU and compliance teams. Second, a secure capture flow that only accepts photos of real three dimensional scenes and blocks screen re shootings. If you want to see how this looks in a production grade workflow, explore VAARHAFT’s Fraud Scanner for document analysis and the web based SafeCam re-capture step that is delivered via SMS without any app download.

Process and organizational controls

Technology must sit inside sound processes. Require out of band verification for changes to payment details. Enforce two person approvals for high risk payouts. For hiring and licensing, validate diplomas directly with issuing registries and train recruiters on signs of altered transcripts. Separate duties for supplier onboarding and payment release. Maintain document retention and hashing policies to detect repeat submissions of the same image or PDF under different case numbers.

Legal and audit readiness

Regulators increasingly expect provenance. The EU AI Act signals a shift toward transparency obligations for certain AI uses and stronger oversight of deceptive practices. Align evidence collection with these expectations. Preserve logs of checks, store signed provenance where available and keep audit ready reports for each decision. For a deeper look at provenance, see our analysis of the standard and its limitations in practice.

From detection to trust by default

Generative models will keep improving. That does not mean companies must accept higher losses. The pattern that works is simple: detect early, re-capture when in doubt and keep decisions traceable. Teams that adopt layered document fraud detection stop fake sick reports, fake income reports and forged invoices without adding friction for legitimate users. If you want to see how pixel level evidence, metadata checks and provenance extraction fit into your HR, AP, claims or onboarding flow, browse our breakdown of AI generated document fraud and how to harden upload steps with practical controls. Our team is happy to walk you through a live analysis and share sample reports so you can evaluate the approach in your environment.