Fake Repair Estimates and Invoices: How AI Is Forging Insurance Documents

Every property and motor insurance claim requires documentation: repair estimates, invoices, receipts. These documents have always been a fraud vector — inflated quotes, phantom repairs, padded invoices. But generative AI has fundamentally changed the economics and sophistication of document fraud.

Creating a convincing fake repair estimate used to require access to industry-specific software, knowledge of trade pricing, and design skills to replicate a legitimate business’s branding. Today, a large language model and a PDF template can produce a document in under a minute that passes casual inspection.

The Insurance Fraud Bureau (IFB) in the UK reported that document manipulation was identified in 27% of investigated claims in 2024, up from 18% in 2022. Australia’s Insurance Council estimates document fraud contributes to $2.2 billion in fraudulent claims annually. AI-generated documents are accelerating both figures.

The Anatomy of AI-Forged Insurance Documents

Automotive Repair Estimates

Motor insurance claims typically require one or more repair estimates. AI-generated automotive quotes exhibit several characteristics:

Realistic line items: LLMs trained on automotive data produce accurate part numbers, labor hour estimates, and paint codes for specific vehicle makes and models
Convincing business branding: Templates scraped from legitimate repair shops are populated with AI-generated content
Inflated but plausible pricing: AI models can be prompted to generate estimates at 20–40% above market rates — enough to profit from but not so extreme as to trigger immediate scrutiny
Multiple quote consistency: Fraudsters generate several quotes from different “businesses” that converge on similar totals, creating an appearance of market validation

A common pattern involves generating three quotes — as many insurers require — from fictitious or compromised repair shops, all converging on an inflated figure. The AI ensures internal consistency across documents while varying formatting and language enough to appear independent.

Property Damage Contractor Estimates

Property claims for storm damage, water damage, fire, and burglary follow similar patterns:

Scope inflation: AI-generated estimates include legitimate damage items plus fabricated additional work — unnecessary structural assessments, inflated materials quantities, phantom subcontractor charges
Trade-specific terminology: Models produce estimates using correct trade terminology, Australian Standard references, and building code citations
Staged documentation: AI generates a sequence of documents — initial assessment, scope of works, progress invoices, completion certificate — creating a paper trail for work never performed or significantly overstated

Manipulated Receipts and Invoices

Receipt manipulation is the simplest form of AI document fraud:

Total modification: Changing amounts on genuine receipts using image editing tools guided by AI
Wholesale fabrication: Generating complete receipts from scratch, including realistic ABN numbers, store formatting, and item descriptions
Date manipulation: Altering purchase dates to fall within claim periods
Duplicate receipts: Generating variations of the same receipt for submission across multiple claims

The Technical Methods Behind Forged Documents

LLM-Driven Content Generation

Large language models generate the textual content of fraudulent documents. When prompted with vehicle details, damage descriptions, or property specifications, they produce:

Accurate trade pricing (sourced from training data that includes industry publications and price lists)
Correct regulatory references (building codes, Australian Standards, manufacturer specifications)
Appropriate professional language and formatting conventions
Internally consistent documentation across multiple related documents

Template Replication

Fraudsters obtain document templates through several methods:

Web scraping: Many legitimate businesses publish sample quotes or have their branding visible online
Previous legitimate documents: A genuine quote from a prior claim serves as a template for future fabrications
AI-assisted design: Image generation models recreate business logos, letterheads, and formatting from minimal reference material

PDF Manipulation

AI-assisted PDF manipulation tools can:

Edit text within existing PDF documents while preserving formatting
Modify embedded images (stamps, signatures, logos)
Alter metadata to match expected creation patterns
Generate multi-page documents with consistent formatting throughout

Detection Methods

OCR and Layout Analysis

Optical character recognition (OCR) combined with layout analysis is the primary automated detection method for document fraud. Advanced systems go beyond simple text extraction:

Structural analysis examines document layout characteristics:

Font consistency across the document (AI-manipulated documents often show subtle font rendering differences where text has been altered)
Text alignment and spacing irregularities
Inconsistent resolution between original and inserted content
Compression artifact patterns that differ between genuine and manipulated regions

Content extraction and validation pulls structured data for automated checking:

ABN and business registration verification against the Australian Business Register
Pricing validation against industry databases
Part number and product code verification
Address and contact detail validation

Metadata Forensics

Document metadata provides critical forensic evidence:

Creation and modification timestamps: Documents claiming to be created weeks ago but with metadata showing recent creation
Software fingerprints: Legitimate repair shop management software (Audatex, Mitchell, Estimage) leaves distinct metadata signatures. Documents created in generic PDF editors or word processors are immediately suspicious.
Author and creator fields: Mismatches between the claimed author/business and the metadata creator field
Edit history: Some PDF formats retain modification history that reveals alterations

Cross-Document Analysis

When multiple documents are submitted for a single claim, cross-document analysis identifies inconsistencies:

Linguistic fingerprinting: If three “independent” quotes share the same sentence structures, vocabulary choices, or formatting quirks, they likely share a common author — or generator
Pricing correlation: Statistical analysis of line items across multiple quotes can detect artificially correlated pricing
Template detection: Identifying common underlying templates across documents from supposedly different businesses
Temporal analysis: Creation timestamps that cluster suspiciously (three “independent” quotes all created within the same hour)

Provider Verification

Automated verification of document sources:

Business existence checks: ABN lookup, business registration verification, physical address validation
License verification: Trade license checks for builders, electricians, plumbers (state-specific databases)
Reputation validation: Cross-referencing against known business directories, Google Business listings, industry association memberships
Contact verification: Automated calls or messages to listed phone numbers to verify the business acknowledges the document

For a broader view of how AI is enabling document fraud across insurance, see our article on AI-generated medical records in insurance claims.

Visual Forensics

Image-level analysis of scanned or photographed documents:

Error level analysis (ELA): Identifies regions of a document image that have been modified by detecting compression inconsistencies
Copy-move detection: Finds duplicated regions within a document, indicating cut-and-paste manipulation
Noise analysis: Different source images have different noise characteristics. Composite documents show noise boundary discontinuities.
Print analysis: If a document claims to be an original print, analysis of printer-specific dot patterns can identify the actual printing device

Building an Effective Detection Pipeline

Automated Screening (Every Claim)

All submitted documents should pass through automated screening:

OCR extraction — structured data extraction from all document types
Metadata analysis — automated flagging of suspicious creation patterns
ABN/business verification — real-time lookup against government registers
Pricing reasonableness checks — comparison against industry pricing databases

This layer should process documents within minutes of submission, adding risk scores to each document in the claims workflow.

Enhanced Analysis (Flagged Claims)

Documents flagged by automated screening receive deeper analysis:

Visual forensics — ELA, noise analysis, and copy-move detection
Cross-document comparison — linguistic and structural analysis across all claim documents
Detailed provider investigation — physical verification of businesses and contact persons
Industry database cross-reference — checking claimed parts and services against manufacturer databases

Investigative Support (High-Risk Claims)

For claims identified as high-risk:

Physical inspection — verifying claimed damage and repairs on-site
Forensic document examination — expert analysis of physical documents
Network analysis — identifying connections between the claimant, repair providers, and other claims in the system
Law enforcement referral — where evidence supports criminal fraud

Industry-Specific Challenges

Automotive Claims

The automotive repair industry’s fragmentation makes verification difficult. Australia has over 25,000 registered automotive repair businesses. Many small shops lack sophisticated IT systems, making it harder to distinguish between a low-tech legitimate business and a fabricated one.

The Motor Trades Association has advocated for standardized digital quoting systems that would include provenance data, but adoption remains voluntary and limited.

Building and Construction Claims

Property damage estimates present unique challenges:

Pricing varies significantly by region, material availability, and market conditions
Scope of works is inherently subjective — what constitutes “necessary” repair is often debatable
Many legitimate contractors provide handwritten or informally formatted estimates
Emergency repair situations may produce documentation after the fact

These factors create noise that fraudsters exploit. AI-generated estimates that fall within normal variability are harder to flag than those with obvious anomalies.

Contents Claims

Personal contents claims (burglary, fire, flood) involve receipts for individual items:

Original receipts may legitimately not exist for older items
Replacement cost calculations involve subjective assessment
AI can generate plausible purchase histories for items that never existed
Accumulated small-value items can sum to significant fraud amounts

The Scale of the Problem

Quantifying AI-generated document fraud precisely is difficult because successful forgeries are by definition undetected. However, available data paints a concerning picture:

The UK’s IFB estimates that AI tools have reduced the cost of producing fraudulent documents by 90% since 2022
Verisk reported a 34% increase in suspicious document submissions in US property and casualty claims during 2024
The Australian Federal Police has noted an increase in referrals involving synthetic business documentation used for insurance and financial fraud

As AI tools become more accessible and capable, the volume of sophisticated document fraud will continue to increase. Insurers need detection capabilities that scale correspondingly.

What Insurers Should Do Now

Implement automated document screening for all claims, not just flagged ones. The cost per document is minimal compared to the fraud exposure.
Invest in OCR and layout analysis tools purpose-built for insurance documents. Generic document processing misses insurance-specific fraud patterns.
Build provider verification databases with known templates, contact details, and document signatures for frequently used repair businesses.
Train claims teams to recognize AI-generated document characteristics and escalate appropriately.
Adopt digital submission standards that capture document provenance data at the point of submission.

The documents supporting insurance claims were never designed to withstand AI-powered fabrication. Detection infrastructure must evolve to match the threat.

DeeTech’s document verification platform uses OCR, layout analysis, and AI forensics to detect manipulated and synthetic insurance documents. Book a demo to see it in action.