Claims Investigation · · 7 min read

AI-Generated Medical Records in Insurance Claims: Detection Strategies

How fraudsters use generative AI to fabricate medical records and prescription documents for insurance claims — and how to detect them.

Medical documentation has always been the backbone of health, life, and workers’ compensation insurance claims. A doctor’s report, a pathology result, a prescription record — these documents carry implicit trust. That trust is now being exploited at scale by generative AI.

In 2025, the Coalition Against Insurance Fraud estimated that fraudulent claims cost the US insurance industry over USD $308 billion annually, with medical claims fraud representing the fastest-growing segment. The emergence of AI-generated medical documents has turbocharged this problem, creating forgeries that are increasingly difficult to distinguish from legitimate records.

This article examines how synthetic medical documents are being used to defraud insurers, which detection strategies work, and what claims teams need to implement now.

The Scope of AI-Generated Medical Document Fraud

What’s Being Fabricated

Generative AI tools — including large language models (LLMs), generative adversarial networks (GANs), and diffusion models — can now produce convincing versions of virtually any medical document:

  • Pathology reports: Synthetic lab results with realistic reference ranges, formatting, and laboratory identifiers
  • Radiology images: AI-generated X-rays, MRIs, and CT scans showing fabricated injuries or conditions
  • Prescription records: Fake scripts with authentic-looking prescriber details and pharmacy stamps
  • Doctor’s notes and referral letters: AI-written clinical narratives with appropriate medical terminology
  • Discharge summaries: Fabricated hospital admission and discharge documentation
  • Specialist reports: Synthetic orthopaedic, neurological, and psychological assessments

A 2025 study published in Nature Medicine found that GPT-4-generated clinical notes were rated as “indistinguishable from human-written notes” by 65% of practising physicians surveyed. If doctors themselves struggle to spot these fakes, claims assessors face an even greater challenge.

Which Insurance Lines Are Affected

Health insurance sees the most direct impact. Fabricated treatment records support claims for procedures never performed. Synthetic pathology results justify coverage for conditions that don’t exist.

Workers’ compensation is particularly vulnerable. Claimants submit AI-generated medical certificates, specialist reports, and treatment plans to extend benefit periods or inflate injury severity. The Australian Institute of Criminology noted in its 2024 fraud trends report that workers’ compensation fraud had increased 23% year-on-year, with document fabrication cited as a contributing factor.

Life insurance fraud uses synthetic medical records during both underwriting (concealing pre-existing conditions) and claims (fabricating cause of death documentation).

Income protection and total permanent disability (TPD) claims increasingly feature AI-generated psychological and psychiatric reports, which are harder to verify than physical injury documentation.

How AI-Generated Medical Documents Are Created

Text-Based Document Fabrication

Modern LLMs can generate medically accurate clinical notes with minimal prompting. A fraudster provides basic parameters — patient demographics, desired diagnosis, treatment history — and the model produces a document indistinguishable from genuine clinical output.

Key capabilities include:

  • Medical terminology accuracy: LLMs trained on medical literature use correct ICD-10 codes, pharmaceutical names, and clinical abbreviations
  • Formatting replication: Models can mimic the specific layout of hospital letterheads, pathology report templates, and referral forms
  • Internal consistency: Advanced prompting produces documents with coherent timelines, appropriate treatment escalation, and realistic complication narratives

Synthetic Medical Imaging

Generative AI has made significant advances in medical image synthesis. Research originally developed for training AI diagnostic tools — generating synthetic X-rays and MRIs to augment limited datasets — has been repurposed for fraud.

A 2024 study in Radiology demonstrated that GAN-generated chest X-rays showing pneumonia were classified as genuine by radiologists 73% of the time. Diffusion models have since improved on these results.

For insurance fraud, synthetic imaging is used to:

  • Fabricate injury evidence for personal injury claims
  • Create pre-existing condition documentation to challenge policy validity
  • Generate progressive imaging series showing deterioration that never occurred

Document Assembly and Finishing

Raw AI output is assembled into final documents using:

  • Template matching: Scraping real hospital and clinic letterheads from the web
  • Digital signature replication: Cloning prescriber signatures from publicly available documents
  • Metadata manipulation: Adjusting creation dates, author fields, and software identifiers to match expected patterns
  • Print-and-scan cycling: Converting digital forgeries to physical copies and back to introduce scanning artifacts that mask digital generation signatures

Detection Strategies

1. Metadata and Provenance Analysis

Every digital document carries metadata — creation timestamps, software identifiers, author fields, and modification history. AI-generated documents frequently exhibit metadata anomalies:

  • Creation software mismatches: A pathology report claiming to originate from a laboratory information system but created in a consumer PDF editor
  • Timestamp inconsistencies: Documents purportedly created during business hours with metadata showing 3 AM creation times
  • Missing digital signatures: Legitimate electronic health records typically carry cryptographic signatures from their originating system

Automated metadata extraction should be the first screening layer in any claims workflow. Tools that flag anomalous creation patterns can triage documents for deeper investigation.

2. Linguistic and Stylometric Analysis

AI-generated clinical text exhibits detectable patterns:

  • Perplexity uniformity: Human-written clinical notes show variable complexity — shorthand in routine observations, detailed language in unusual findings. AI-generated text maintains unnaturally consistent complexity throughout.
  • Vocabulary distribution: LLM outputs favor certain word choices and sentence structures that differ from the statistical patterns of genuine clinical writing
  • Template adherence: AI-generated documents often follow textbook formats too precisely, lacking the idiosyncratic shortcuts and abbreviations individual clinicians develop

Natural language processing (NLP) tools trained on genuine clinical documentation can flag statistical outliers. DeeTech’s document analysis pipeline applies these techniques specifically to insurance claim documentation.

3. Medical Image Forensics

Synthetic medical images contain forensic signatures invisible to the human eye:

  • Frequency domain analysis: GAN-generated images exhibit characteristic spectral artifacts, particularly in high-frequency components
  • Noise pattern analysis: Real medical images contain sensor-specific noise signatures from the imaging equipment. Synthetic images lack these or contain inconsistent noise patterns.
  • Anatomical consistency checks: AI models occasionally generate anatomically impossible features — vertebrae counts that don’t match, bilateral asymmetry inconsistencies, or soft tissue patterns that violate known physiology
  • DICOM header verification: Genuine medical images in DICOM format contain detailed equipment and acquisition parameters. Synthetic images either lack these headers or contain fabricated values that don’t correspond to real imaging equipment

4. Cross-Referencing and Verification

Document-level analysis is necessary but insufficient. Effective detection requires cross-referencing against external data:

  • Provider verification: Confirming the named practitioner exists, holds current registration, and practises at the stated location. In Australia, the AHPRA register provides public verification of health practitioner registration.
  • Facility validation: Checking that the named hospital, clinic, or laboratory exists and that the document format matches their known templates
  • Treatment plausibility: Comparing claimed treatments against clinical guidelines for the stated condition. AI-generated documents sometimes prescribe treatments inconsistent with current standard of care.
  • Timeline verification: Cross-referencing claimed treatment dates against the claimant’s known movements, employment records, and other claim documentation

5. Digital Provenance Standards

The emerging C2PA (Coalition for Content Provenance and Authenticity) standard provides cryptographic content credentials that track how digital content was created and modified. While adoption in healthcare is still early, forward-thinking insurers are beginning to require C2PA-compliant documentation for high-value claims.

For more on content provenance in insurance, see our article on deepfake detection technology.

Building a Detection Workflow

Triage Layer

Every medical document submitted with a claim should pass through automated screening:

  1. Metadata extraction and anomaly detection — flags documents with suspicious creation patterns
  2. Format validation — compares document layout against known templates for the stated provider
  3. Provider verification — automated lookup against registration databases

Documents passing triage proceed normally. Flagged documents enter the investigation pathway.

Investigation Layer

Flagged documents receive deeper analysis:

  1. Linguistic analysis — NLP-based assessment of clinical text authenticity
  2. Image forensics — frequency and noise analysis for any embedded medical images
  3. Cross-reference checks — treatment plausibility, timeline verification, provider contact

Escalation Layer

Documents that fail investigation checks are escalated for:

  1. Expert review — qualified medical professionals assess clinical plausibility
  2. Direct provider contact — verification with the named practitioner or facility
  3. Forensic investigation — full document forensics including print analysis if physical copies are involved

Admissibility of AI Detection Evidence

Insurance investigators using AI detection tools need to establish the reliability and validity of their detection methods. Courts in Australia and internationally are still developing frameworks for admitting AI-generated evidence analysis.

Best practice is to:

  • Maintain detailed audit trails of detection methodology
  • Use validated, peer-reviewed detection techniques
  • Combine automated detection with human expert verification
  • Document false positive and false negative rates for detection tools used

Privacy Obligations

Medical document verification must comply with privacy legislation, including the Australian Privacy Act 1988 and applicable state health records legislation. Automated analysis of medical documents may constitute collection and use of health information, triggering specific consent and handling requirements.

Duty of Good Faith

Insurers must balance fraud detection with their duty of good faith to policyholders. Detection workflows should be designed to minimize delays for legitimate claims while effectively identifying fraudulent documentation.

The Path Forward

AI-generated medical documents represent a fundamental shift in insurance fraud methodology. The barrier to creating convincing medical forgeries has collapsed from requiring specialist knowledge and equipment to requiring only a text prompt.

Insurers that rely solely on human review of medical documentation are increasingly exposed. The Insurance Council of Australia has flagged document fraud as a priority concern for 2026, and regulatory guidance on AI-enabled fraud detection is expected within the year.

Effective defense requires a layered approach: automated screening for every document, deeper analysis for flagged submissions, and continuous updating of detection models as generative AI capabilities evolve.

The insurers who invest in detection infrastructure now will be positioned to manage this risk. Those who wait will find themselves funding an AI-powered fraud epidemic.

DeeTech provides AI-powered document verification and deepfake detection solutions purpose-built for insurance claims teams. Contact us to discuss how we can help protect your claims process.