Police Report Verification: Detecting AI-Manipulated Incident Reports

Police reports occupy a privileged position in insurance claims. They’re treated as authoritative, third-party documentation of events — accidents, thefts, property damage, assaults. Claims assessors routinely accept police reports at face value, applying less scrutiny than they might to other supporting documents.

Fraudsters know this. And generative AI has made exploiting this trust dramatically easier.

Fabricated and altered police reports appear in motor vehicle accident claims, burglary claims, property damage claims, and personal injury claims. The FBI’s Internet Crime Complaint Center reported a 41% increase in fraudulent government document complaints in 2024, with insurance-related fraud cited as a primary driver. In Australia, state police forces have noted a rise in reports of forged police documentation, though comprehensive statistics remain limited.

This article covers how AI-manipulated police reports are created, the forensic techniques that detect them, and the verification workflows claims teams should implement.

How Police Reports Are Being Manipulated

Complete Fabrication

The most brazen approach: creating a police report from scratch. AI tools enable this by:

Format replication: Police reports follow jurisdiction-specific templates. These templates are often publicly available through freedom-of-information requests, court documents, and government websites. AI can replicate layouts precisely.
Reference number generation: Fraudsters generate plausible-looking case reference numbers. Some jurisdictions use predictable numbering systems that can be reverse-engineered.
Officer details: Names of real police officers are publicly available through court records, media reports, and departmental directories. Fabricated reports use real names to add credibility.
Narrative generation: LLMs produce incident narratives that use correct police terminology, appropriate legal references, and the characteristic writing style of law enforcement documentation.

A fully fabricated police report — complete with formatting, reference numbers, officer names, and a coherent incident narrative — can be produced in minutes. The result is a PDF that looks identical to a genuine report from the relevant jurisdiction.

Selective Alteration

More sophisticated fraud involves modifying genuine police reports:

Date changes: Altering incident dates to fall within policy coverage periods
Location modifications: Changing the reported location to match the claimant’s jurisdiction or policy territory
Severity escalation: Modifying damage descriptions, injury assessments, or theft inventories to support larger claims
Party information changes: Altering names, vehicle details, or contact information
Narrative editing: Revising the officer’s narrative to better support the insurance claim, changing fault determinations or adding details that weren’t in the original

Selective alteration is harder to detect than complete fabrication because the bulk of the document is genuine. Only specific fields have been changed.

Composite Documents

Some fraudsters combine elements from multiple genuine documents:

The header and formatting from one authentic report
Reference numbers and dates from a different report
Officer details from a third source
An AI-generated narrative tailored to the claim

This approach maximises the amount of “real” content in the document, making forensic detection more challenging.

Document Forensics for Official Government Documents

Digital Document Analysis

Most police reports submitted to insurers are digital — PDFs, scanned images, or photographs. Each format offers different forensic opportunities.

PDF forensics examines:

Document structure: Genuine police reports generated by records management systems (RMS) have characteristic PDF structures — specific object hierarchies, font embedding patterns, and form field configurations. Documents created by editing tools show different structural patterns.
Font analysis: Modified text often uses fonts that don’t precisely match the original, even if they appear identical to the eye. Subtle differences in font metrics, kerning, and hinting reveal edits.
Layer analysis: Some PDF editors leave modification layers intact. A document with visible text on one layer and different text on a hidden layer indicates manipulation.
Incremental saves: PDF specifications support incremental updates. Forensic analysis of the update history can reveal what was changed and when.

Image-based forensics (for scanned reports) applies:

Error level analysis (ELA): Regions that have been edited show different compression characteristics than untouched areas
Noise consistency analysis: Scanned documents should have uniform noise characteristics. Inserted or modified elements often have different noise profiles.
Resolution analysis: Added elements may have different resolution characteristics than the original scan
Edge detection: Text or graphics that have been overlaid on the original document often show detectable edge artifacts

Physical Document Analysis

When physical police reports are submitted:

Paper analysis: Genuine police reports are typically printed on specific paper stock. Photocopied or home-printed forgeries use different paper.
Printer identification: Laser and inkjet printers leave microscopic patterns (Machine Identification Codes) that can identify the specific printer used
Ink analysis: Different inks show different spectral characteristics under infrared or ultraviolet examination
Impression evidence: Genuine documents may carry impression marks from writing on overlying sheets, stamps, or embossing that forgeries lack

Linguistic Forensics

AI-generated police narratives exhibit detectable patterns when compared to genuine law enforcement writing:

Vocabulary distribution: Police officers develop jurisdiction-specific and personally idiosyncratic writing patterns. AI-generated text shows statistical distributions characteristic of language models rather than individual human writers.
Jargon accuracy: While LLMs use correct general police terminology, they may misuse jurisdiction-specific codes, abbreviations, or procedural references
Narrative structure: Genuine incident narratives follow learned patterns from police academy training and department-specific templates. AI narratives follow patterns learned from broader training data.
Hedging language: Real police reports contain characteristic uncertainty markers (“the reporting party stated that…”, “it appeared that…”) that follow specific patterns. AI-generated text often uses these markers differently.

For related detection techniques applied to medical documents, see our article on AI-generated medical records in insurance claims.

Verification Workflows for Claims Teams

Tier 1: Automated Verification (All Claims)

Every police report submitted with a claim should undergo automated checks:

Reference number verification

Cross-reference the report number against the issuing police force’s records system
In Australia, most state police forces offer some form of report verification service — though access methods vary by jurisdiction
The NSW Police Force provides online event verification for specific report types
Queensland Police Service offers report verification through their Policelink service

Format validation

Compare the submitted document’s layout against known templates for the stated jurisdiction and report type
Check for formatting anomalies — unusual fonts, spacing irregularities, misaligned fields
Verify that logos, watermarks, and official markings match current versions

Metadata extraction

Extract and analyze PDF metadata for creation software, timestamps, and author information
Flag documents created by unexpected software (consumer PDF editors rather than police RMS)
Check for timestamp inconsistencies between the stated report date and the document creation date

OCR and data extraction

Extract structured data from the report: names, dates, locations, vehicle registrations, reference numbers
Cross-reference extracted data against claim details and other submitted documents
Flag inconsistencies between the police report and the claimant’s account

Tier 2: Enhanced Verification (Flagged Claims)

Claims flagged by automated screening receive additional verification:

Direct police verification

Contact the issuing police station or records department to confirm the report exists and matches the submitted document
Request a certified copy directly from police if the claim value warrants the effort and delay
Verify the named officer exists and was stationed at the listed location on the stated date

Document forensics

Apply ELA, noise analysis, and structural analysis to the submitted document
Compare against reference exemplars from the same jurisdiction
Assess linguistic patterns against known characteristics of genuine reports from that jurisdiction

Cross-reference investigation

Verify the incident against any publicly available information (court records, media reports, traffic incident databases)
Check for duplicate reports — the same incident reported across multiple claims
Investigate the claimant’s history for patterns of police report-supported claims

Tier 3: Investigative Escalation (High-Risk Claims)

For high-value claims or those showing strong indicators of fraud:

Formal police liaison

Engage with police fraud units through established channels
Request formal verification through inter-agency cooperation agreements
File reports with police if document fraud is confirmed

Expert forensic examination

Engage qualified forensic document examiners for court-admissible analysis
Apply physical document examination techniques if originals are available
Prepare evidence documentation for potential prosecution or claim denial proceedings

Network analysis

Investigate connections between the claimant and other claims involving similar police reports
Check for patterns suggesting organized fraud rings using templates or shared fabrication tools
Cross-reference with industry fraud databases (in Australia, the Insurance Fraud Bureau of Australia maintains shared intelligence)

Jurisdiction-Specific Considerations

Australia

Australian police report formats vary significantly by state and territory:

New South Wales: Event numbers (E-numbers), accessible through NSW Police online services
Victoria: Reference numbers issued by Victoria Police, verification through the police assistance line
Queensland: QP numbers, verification through Policelink (131 444)
Western Australia: Incident report numbers, verification through WA Police
South Australia: Report numbers, verification through SA Police

Each jurisdiction uses different report templates, terminology, and reference numbering systems. Detection systems must be calibrated for each.

International Claims

Travel insurance, international motor, and cross-border claims present additional challenges:

Police report formats vary dramatically between countries
Verification channels may be limited or non-existent for some jurisdictions
Translation adds another layer where manipulation can occur
Some countries have systemic issues with police report integrity, making even “genuine” reports unreliable

Common Red Flags

Claims teams should escalate police reports exhibiting any of these characteristics:

Report submitted as a photo or low-quality scan when digital originals should be available
Reference numbers that don’t follow the jurisdiction’s known format
Officer names that can’t be verified through public records
Incident details that don’t match publicly available information (road conditions, weather, traffic reports)
Narrative language that’s unusually formal or structured for routine incident reports
Formatting that’s close but not exact compared to known genuine reports from that jurisdiction
Multiple claims from the same claimant supported by police reports from different jurisdictions
Reports from jurisdictions where the claimant has no apparent connection
Metadata indicating the document was created long after the stated incident date
Reports that are “too perfect” — no handwritten amendments, corrections, or supplementary notes that genuine reports typically contain

Implementing Change

Technology Requirements

Effective police report verification requires:

Document forensics platform capable of PDF structure analysis, image forensics, and metadata extraction
Template library of genuine police report formats from relevant jurisdictions, regularly updated
Verification API integrations with police records systems where available
OCR and data extraction calibrated for police report formats
Claims management integration to embed verification checks into existing workflows

Process Requirements

Technology alone is insufficient. Claims teams need:

Clear escalation procedures for flagged documents
Established police liaison channels for verification requests
Training on AI-generated document characteristics for claims assessors and investigators
Documentation standards for verification activities (essential for litigation and regulatory compliance)
Feedback loops to improve detection models based on confirmed fraud cases

Cultural Requirements

The most significant barrier is the historical assumption that police reports are inherently trustworthy. Claims teams must shift from a trust-by-default model to a verify-by-default model for all official documentation.

This doesn’t mean treating every claimant as a suspect. It means applying consistent, automated verification to every document — transparently and without delay to legitimate claims.

The Evolving Threat

AI capabilities for document generation are improving faster than most detection methods. Today’s detectable anomalies may not exist in tomorrow’s forgeries. Sustainable defense requires:

Continuous updating of detection models
Investment in fundamental verification infrastructure (direct police verification channels)
Industry collaboration on fraud intelligence sharing
Advocacy for digital provenance standards in government documentation

The C2PA standard for content provenance, if adopted by government agencies for official documents, would fundamentally change the verification landscape. Until then, insurers should rely on forensics, verification, and vigilance.

DeeTech helps insurers verify document authenticity across all claim types. Our document forensics platform includes police report verification workflows built for Australian and international jurisdictions. Talk to us.