Health Insurance Fraud and AI: Detecting Synthetic Medical Evidence

Healthcare fraud is the most expensive category of insurance fraud in the United States. The Coalition Against Insurance Fraud reported US$3.1 billion in false and fraudulent healthcare claims in 2020 alone — and that figure represents only detected fraud. Medicare fraud is separately estimated at US$60 billion annually, according to AARP.

The US Department of Health and Human Services recovered US$5.9 billion from healthcare fraud investigations in a single fiscal year, filed 809 criminal actions, and excluded 2,640 individuals and entities from federal healthcare programs. Improper Medicare payments alone totalled an estimated US$31.6 billion in 2018, according to the Government Accountability Office.

Generative AI is now creating an entirely new attack surface. Medical records, diagnostic imaging, prescriptions, telehealth recordings, and provider documentation can all be fabricated or manipulated using freely available AI tools — at a speed and quality that overwhelms traditional fraud detection.

The New Threat: AI-Generated Medical Evidence

Synthetic Medical Records

Large language models can generate clinically plausible medical documentation — patient histories, examination findings, diagnosis narratives, treatment plans, and progress notes — that reads like authentic physician documentation. These records can be produced in the formatting style of specific electronic health record (EHR) systems, complete with appropriate clinical terminology, ICD-10 diagnosis codes, and CPT procedure codes.

The challenge: genuine medical records vary enormously in quality, formatting, and writing style. There is no single “authentic” look. A hurried emergency physician’s notes look very different from a detailed specialist consultation. This natural variation makes it difficult to establish a baseline against which to detect AI-generated forgeries.

Fabricated Diagnostic Imaging

AI image generation can produce medical imaging — X-rays, CT scans, MRI sequences, ultrasound images — that depicts pathology supporting a fraudulent claim. While current generation tools may not perfectly replicate the technical characteristics of genuine medical imaging, the quality is improving rapidly.

More immediately dangerous is the manipulation of genuine imaging: real diagnostic images altered to add, enlarge, or change pathological findings. A genuine chest X-ray with no abnormalities can be manipulated to show a pulmonary nodule. A genuine MRI with minor degenerative changes can be altered to show a herniated disc.

Deepfake Telehealth Consultations

The explosive growth of telehealth — accelerated by the COVID-19 pandemic and now a permanent feature of healthcare delivery — creates a new fraud vector. Deepfake video and audio technology can fabricate telehealth consultations that never occurred:

A synthetic video of a “physician” conducting an examination
A deepfake of a “patient” presenting symptoms to justify treatment claims
Fabricated recorded consultations submitted as evidence of services rendered

The CNN-reported Hong Kong deepfake case — where a finance worker was fooled by an entire room of deepfake participants in a live video call — demonstrates that the technology is already convincing enough for real-time interactive impersonation. Pre-recorded telehealth consultations, with no live interaction required, are substantially easier to fake.

Synthetic Prescriptions and Pharmacy Records

AI-generated prescriptions — with correct drug names, dosages, DEA numbers, and prescriber information — can support fraudulent claims for medications never dispensed. When combined with fabricated pharmacy records and patient medication histories, these create a comprehensive paper trail for phantom pharmaceutical claims.

Voice-Cloned Provider Verification

When insurers call to verify services with treating providers, voice cloning technology can intercept or pre-empt these verification calls. Pindrop’s 2025 Voice Intelligence and Security Report documented US$12.5 billion in contact center fraud in 2024, with 2.6 million fraud events. Voice cloning from short audio samples means a fraudster can impersonate a specific physician during a verification call, “confirming” services that were never provided.

Why Health Insurance Is the Highest-Value Target

Claim Volume and Complexity

Health insurance processes more individual claims transactions than any other insurance line. The sheer volume — hundreds of millions of claims annually across the US market — means that individual claims receive minimal manual review. Automated processing pipelines accept claims with supporting documentation at face value unless specific red flags trigger investigation.

Documentation Density

A single health insurance claim may involve:

Provider notes from multiple practitioners
Laboratory results
Diagnostic imaging and radiology reports
Prescription records
Hospital admission and discharge summaries
Procedure and operative reports
Durable medical equipment orders
Physical therapy and rehabilitation records

Each document type is a potential target for AI fabrication. The documentation density that makes health insurance claims legitimate also provides extensive surface area for fraud.

Limited Physical Verification

Unlike auto or property insurance, where damage can be physically inspected, healthcare services are inherently intangible after delivery. An insurer can’t “inspect” a medical consultation that was claimed to have occurred. The evidence is entirely documentary — exactly the type of evidence that AI can fabricate.

Provider Trust Model

The healthcare system operates on a trust model: licensed providers are generally assumed to provide services in good faith, and their documentation is accepted as authoritative. This trust model works well for the vast majority of honest providers but creates a significant vulnerability when providers are complicit in fraud — or when fraudsters impersonate legitimate providers using AI.

Detection Strategies

Medical Document Forensics

Formatting analysis. While medical records vary in style, specific EHR systems produce documents with characteristic formatting: specific fonts, header structures, page layouts, and field arrangements. AI-generated records that claim to originate from a specific system can be checked against the system’s known output format. Subtle deviations — wrong font sizes, incorrect margin spacing, non-standard field ordering — indicate fabrication.

Clinical consistency checking. AI-powered analysis can verify that the clinical content of a record is internally consistent:

Do diagnosis codes match the narrative findings?
Are prescribed medications appropriate for the documented diagnosis?
Is the treatment timeline clinically realistic?
Do laboratory values and vital signs fall within physiologically possible ranges?
Are procedure codes consistent with the documented procedure descriptions?

Writing style analysis. Genuine physician documentation has characteristic patterns: specific abbreviation conventions, dictation artifacts (from voice-to-text documentation), copy-forward text from previous encounters, and individual practitioner quirks. AI-generated text tends to be more uniform, formally written, and lacking these natural irregularities.

Cross-record verification. Compare documentation across multiple records in the same claim: does the patient history documented by a specialist match what the primary care physician recorded? Do laboratory results reference values consistent with the timing of specimen collection? Are there impossible overlaps (e.g., the patient documented as being in two different facilities on the same day)?

Imaging Forensics

DICOM metadata verification. Medical images stored in DICOM format contain extensive metadata: scanner manufacturer and model, acquisition parameters, institution identifiers, patient demographics, study dates, and referring physician information. AI-generated medical images either lack this metadata, contain inconsistent values, or include metadata that doesn’t match any real imaging equipment.

Acquisition artifact analysis. Each imaging modality (X-ray, CT, MRI, ultrasound) and each scanner manufacturer produces images with characteristic artifacts — noise patterns, resolution profiles, edge characteristics — that result from the physics of the imaging process and the engineering of the specific equipment. AI-generated images may lack these expected artifacts or contain artifacts inconsistent with the claimed modality.

Pathology plausibility. Advanced analysis can assess whether depicted pathology is anatomically and pathologically consistent: is the lesion in a location consistent with the diagnosis? Is the appearance characteristic of the claimed condition? Does the progression match the clinical timeline?

Telehealth Verification

Video forensics. Recorded telehealth consultations can be analyzed for deepfake indicators: temporal inconsistencies between frames, audio-visual synchronisation issues, face generation artifacts, and metadata indicating the video was produced by generation software rather than recorded by a camera.

Audio analysis. Voice characteristics of participants can be verified against known voiceprints (where available) and analyzed for synthetic speech indicators. Natural human speech has micro-variations in pitch, cadence, and spectral properties that current voice synthesis doesn’t perfectly replicate.

Session metadata verification. Telehealth platforms generate session logs with timestamps, IP addresses, device information, and connection metadata. Cross-referencing claimed consultations against platform session data can verify whether a session actually occurred.

Provider Network Analysis

Billing pattern analysis. AI-powered analysis of billing patterns can identify providers with statistically anomalous claim profiles: unusually high volumes, unusual procedure mixes, billing for services inconsistent with their specialty, or patterns suggesting upcoding.

Referral network mapping. Mapping referral patterns between providers can reveal coordinated fraud schemes: circular referral networks, providers exclusively referring to each other, or referral patterns that don’t align with geographic or specialty logic.

Patient overlap analysis. Identifying patients appearing across multiple providers in patterns suggesting a mill operation — the same patients cycling through the same set of providers generating maximum billable services.

Implementation for Health Insurers

Priority Deployment

Medical document authentication on all claims above a value threshold — this catches the highest-value fabrications immediately
Imaging forensics on claims involving diagnostic imaging — high-value claims that depend heavily on imaging evidence
Voice analysis on provider verification calls — closing the voice cloning loophole
Telehealth recording analysis on claims involving telehealth consultations — a rapidly growing fraud vector

Integration Points

Pre-payment review — analyze documentation before claims are paid, not after
Prior authorisation — verify supporting documentation at the authorisation stage
Post-payment audit — periodic batch analysis of paid claims to identify patterns missed at the individual level
Provider credentialing — verify provider documentation during network enrollment

Regulatory Compliance

Health insurance fraud detection must operate within:

HIPAA — all evidence handling and analysis must comply with patient data protection requirements
State fraud reporting mandates — detected fraud must be reported to appropriate state agencies
CMS requirements — for Medicare and Medicaid claims, compliance with Centers for Medicare & Medicaid Services fraud and abuse regulations
False Claims Act — documentation standards for pursuing fraud recoveries under federal and state false claims statutes

The Scale of Opportunity

Healthcare fraud represents the single largest category of insurance fraud by dollar value. The combination of claim volume, documentation complexity, limited physical verification, and the trust-based provider model makes it both the most targeted and the most difficult to defend.

AI-powered detection — particularly medical document authentication and imaging forensics — addresses the specific evidentiary challenges that make healthcare fraud so difficult to catch through traditional methods. As AI-generated medical evidence becomes more prevalent, these detection capabilities transition from competitive advantage to operational necessity.

deetech’s platform analyses medical records, diagnostic imaging, and telehealth recordings for AI generation and manipulation. Our forensic analysis meets the evidentiary standards required for fraud recovery and regulatory compliance. Request a demo.

Sources cited in this article: