AI-Generated Fraud in Canadian Insurance Claims: An SIU Authentication Field Guide
Canadian insurance fraud investigations rose 76% in 2024. A methodology-grade triage protocol for authenticating suspect dashcam footage, injury photographs, and claim media.
Canadian insurance fraud investigations increased 76 percent in 2024. Two thirds of those investigations were auto claims. Staged collision detections rose sharply in the final quarter of 2024, and in parallel, Aviva Canada named AI-enabled falsified and forged documents as one of its five identified fraud trends for 2025.
The tools enabling this shift are not sophisticated. A $20 monthly subscription is sufficient to generate convincing video, alter a photograph, or clone a voice. The barrier to AI-assisted fraud is lower now than at any previous point in the history of the insurance industry.
What has not kept pace is the investigative protocol for handling the media that arrives in support of claims.
This guide is written for SIU investigators and claims professionals who need a practical, methodology-grade framework for identifying, triaging, and authenticating suspect media in Canadian auto and personal lines claims. It is not theoretical. Every step in this guide maps directly onto forensic practice and onto the evidentiary standards that apply if a file moves from investigation to litigation or regulatory proceedings under Ontario's Fraud Reporting Service Rule.
76% increase in fraud investigations across Canada in 2024. Two thirds were auto claims. Staged collision detections rose sharply in Q4 2024. Aviva Canada named AI-enabled falsified and forged documents a named fraud trend for 2025. Source: Aviva Canada, March 2025.
The Five Media Types Now Appearing in AI-Enabled Canadian Fraud
Not all claim media carries the same authentication risk. The following five categories represent the current pattern of AI-assisted fraud in Canadian property and casualty claims.
Dashcam and vehicle-mounted footage. Staged collision schemes depend on footage corroborating the claimant's account of an incident. With consumer-grade video editing tools, a claimant can alter timestamps, composite a vehicle into a recording, or generate a synthetic collision sequence. The technical signatures of this manipulation are detectable through file structure and encoding analysis, but they are invisible to the eye.
Injury photographs. AI image-generation and editing tools can produce convincing photographs of soft-tissue injury, bruising, swelling, and post-operative presentation. In accident benefits claims, where injury severity determines benefit entitlement, a fabricated or manipulated injury photograph can misrepresent the clinical picture entirely. These files often originate on a smartphone, which means the metadata is present, specific, and checkable.
Vehicle damage photographs. Damage extent determines repair cost. AI-assisted compositing can place damage that did not occur onto a real photograph of a vehicle, or can generate a synthetic photograph of a vehicle that does not exist. Lighting inconsistency, shadow mismatch, and edge artifact patterns are the primary detection markers.
AI-generated documents. Invoices, repair estimates, clinical records, and receipts are now within the capability of consumer-grade generative AI. Document authentication involves a different analytical pathway than media authentication, but the underlying methodology, examining structural metadata, font consistency, formatting artifacts, and digital fingerprints, applies equally.
Recorded statements and audio submissions. Voice cloning technology has reached the point where a synthetic reproduction of a claimant's voice is indistinguishable to the human ear. Audio authentication requires spectral analysis, cadence examination, and background noise pattern analysis.
Why Visual Review Is Not Sufficient
The instinct of most investigators when reviewing suspect media is to look at it carefully. This instinct is not wrong, but it is increasingly insufficient.
The Mendones v. Cushman and Wakefield decision (Cal. Super., September 9, 2025) is instructive. In that case, a California Superior Court judge identified deepfakes in submitted court exhibits by observing looping video feeds, the absence of natural facial expressions, monotone delivery, and mouth movement that did not match the words spoken. She also identified a photographic composite where the subject was in colour against a black and white background.
These tells were visible. Future fabrications will not be.
The same generation of AI tools that produced those detectable artifacts has already been superseded. The practical implication for Canadian SIU investigators is that visual review catches yesterday's fabrications, not today's. The metadata and file structure are where current fabrications leave traces.
A California Superior Court judge identified deepfakes in submitted court exhibits by observing looping video feeds, absent facial expressions, monotone delivery, and mouth movement inconsistent with the spoken words. A composited photograph placed a colour subject against a black and white background. The metadata claimed iPhone 6 Plus running iOS 12.5.5. The AI generation feature used required iPhone 15 Pro and iOS 18. The court issued a terminating sanction. The judge noted she lacked the time, funding, and technical expertise to examine every suspicious submission in the record.
The Triage Protocol: What to Look For Before Sending for Authentication
Before a file is submitted for forensic authentication, an investigator can conduct a preliminary triage to determine whether the indicators justify the cost and time of a full analysis. The following checklist is not a substitute for forensic examination. It is a triage tool.
1. Metadata consistency check
Every media file carries embedded technical data: the recording device, operating system version, capture date and time, GPS coordinates where enabled, and encoding parameters. Open the file properties on a desktop system and compare the claimed recording device against the metadata. In Mendones, the metadata claimed an iPhone 6 Plus running iOS 12.5.5. The AI generation feature used to create the file requires an iPhone 15 Pro and iOS 18. The device named in the metadata had not existed when the relevant Apple Intelligence features were introduced. This type of mismatch is not rare. It is the most common single indicator of AI-generated or AI-assisted media.
2. Date and time plausibility
Compare the file creation date and time against the claimed incident date and time. Check whether the file was modified after creation. Most media files record a creation timestamp and a modification timestamp. A discrepancy between the two warrants further examination.
3. Device consistency
Every recording device produces a characteristic technical profile. A dashcam records at specific resolutions, frame rates, and bitrates. A smartphone records at different parameters. If the claimed source device is inconsistent with the technical profile of the submitted file, that inconsistency is documentable and challengeable.
4. File size anomaly
A two-minute dashcam recording at standard resolution produces a file of predictable size. A file that is substantially smaller or larger than expected for its claimed source and duration may indicate re-encoding, compression, or synthetic generation.
5. Visible loop seams in video
Watch the video at reduced speed with attention to micro-expressions, eye movement, and background motion. A looping video feed leaves a visible seam. This is a crude visual check, but it catches unsophisticated fabrications.
6. Lighting and shadow consistency in photographs
In any composite image, the lighting direction, shadow length, and colour temperature of the foreground subject must be consistent with the background. Inconsistencies, especially in outdoor accident-scene photographs, are often visible even without magnification.
7. Compression artifact pattern
Authentic photographs and videos have compression artifact patterns that are consistent with their claimed source device. A photograph that has been edited and re-saved carries double-compression artifacts in the manipulated areas that are detectable through error level analysis.
8. Audio spectral anomalies
Synthetic voices often display unusual cadence, absent background noise bleed, and spectral characteristics that differ from natural recordings. A spectrogram view of a voice recording reveals these patterns to a trained examiner.
9. File provenance gap
Ask where the file came from. What device captured it. How it was transmitted to the claimant and then to the insurer. A file that arrived via a messaging application has different chain of custody characteristics than one exported directly from a dashcam device. Each transmission step is a potential point of manipulation.
10. Absence of expected artifacts
Authentic dashcam footage carries GPS track data, G-sensor shock data, and loop-recording timestamps depending on the device. A file that claims to originate from a dashcam but lacks these embedded data streams warrants examination.
What to Preserve on Intake Before Sending for Authentication
Chain of custody begins at the moment media is received. A file that has been opened, played repeatedly, saved in a new location, or shared through multiple messaging applications may have its metadata altered and its integrity compromised before a forensic examiner sees it.
The following intake protocol protects the evidentiary value of the file.
Receive the original file, not a screenshot or a compressed copy. If the claimant submits a photograph taken of a screen rather than the original file, request the original.
Document the file name, file size, and file format exactly as received. Record the date, time, and method of receipt.
Do not open the file in an application that modifies metadata. Certain applications update the file access timestamp on opening. Use a file manager to copy the original without opening it.
Generate a hash value of the original file immediately on receipt. A hash value is a fixed-length string that is unique to the exact binary content of the file. Any subsequent modification of the file, including opening it, will produce a different hash. The original hash value is the chain of custody anchor.
Store the original file in a separate, unmodified copy before beginning any review. Work only from the copy.
Document the transmission path of the file from the claimant to the insurer, including any messaging applications, email platforms, or physical devices through which it passed.
Tier 1 Screening versus Tier 2 Comprehensive Analysis
Veridium Forensics provides two levels of forensic authentication service. Not every suspect file requires a full analysis. The appropriate level depends on the nature of the claim, the value at stake, and the degree of suspicion raised by the preliminary triage.
Preliminary assessment of metadata, file structure, compression artifact patterns, and provenance indicators. Returned within 48 to 72 hours. Written report documenting examination methodology, findings, and limitations. Appropriate for claim screening before a coverage decision, for files where the triage checklist has raised one or two indicators without definitive findings, and for initial assessment before deciding whether Tier 2 analysis is warranted.
Full forensic examination including binary-level file inspection, pixel-level statistical analysis, encoding profile analysis, full metadata extraction, source authentication attempt, and SWGDE-aligned methodology documentation. Formal forensic report of 10 to 20 pages with documented findings, opinion, and stated limitations. Appropriate for files where Tier 1 has identified indicators of manipulation, for high-value claims where authenticity is central to the coverage decision, and for any file that may be needed to support a fraud determination under the FSRA Fraud Reporting Service Rule or to withstand challenge in subsequent proceedings.
How Forensic Authentication Findings Hold Up in Canadian Proceedings
The FSRA Fraud Reporting Service Rule, enacted under Ontario's Insurance Act, requires insurers to report fraud at three thresholds: suspicion, reasonable grounds, and conclusion of fraud. A documented forensic authentication report provides the methodology-disclosed analytical basis that supports a reportable fraud determination at the reasonable grounds or conclusion level.
If a file moves from a fraud investigation to civil litigation, the authenticity burden under section 31.1 of the Canada Evidence Act falls on the party tendering the media as evidence. A forensic authentication report produced at the claims stage becomes the evidentiary anchor for that burden in subsequent proceedings. A coverage denial supported by a documented, reproducible forensic analysis is significantly harder to challenge in subsequent proceedings than one based on an investigator's visual judgment.
Ontario Regulation 384/24, in force since December 2024, requires any expert whose report is filed in civil proceedings to certify the authenticity of every document or record cited in that report. A SWGDE-aligned forensic authentication report produced at the claims stage meets this standard directly.
FSRA Fraud Reporting Service Rule
The Investigative Standard Is Changing
Canadian insurers are investing in AI-based fraud detection platforms. These platforms produce confidence scores. A confidence score tells an investigator whether a detection model thinks the media is synthetic. It does not document how that conclusion was reached. It cannot be examined by opposing counsel. It cannot satisfy the evidentiary standard required for a fraud determination under the FSRA rule or for litigation.
Forensic authentication produces a different class of output: a documented, methodology-disclosed, reproducible written analysis that can be examined, challenged, and defended.
As fraud methods become more sophisticated and as the regulatory expectations on insurers become more specific, the gap between a detection score and a defensible forensic report becomes the gap between a claim decision that holds and one that does not.
Veridium Forensics provides Tier 1 and Tier 2 forensic authentication analysis for Canadian insurance investigators. Preliminary assessments are returned within 48 to 72 hours.
Frequently Asked Questions
What is the difference between a deepfake detector and a forensic authentication report?
A deepfake detector produces a probability score based on a trained model's analysis of a file. It does not document its methodology, identify specific file characteristics, or produce a conclusion that can be tested by another examiner. A forensic authentication report documents every step of the analysis, identifies the specific technical findings in the file, states the methodology used to reach those findings, and produces an opinion that another qualified examiner can independently verify.
Does forensic authentication require access to the original recording device?
Source device access assists authentication but is not always required. Forensic examination of the file itself, including its metadata, encoding profile, compression artifact pattern, and binary structure, can produce documented findings and a supportable opinion without access to the recording device.
Can AI-generated media be detected if the fabrication is sophisticated?
No authentication methodology guarantees detection of all fabrications. Any competent forensic opinion states its limitations clearly. What forensic analysis provides is a documented examination that identifies the technical indicators present in the file, supports or challenges the authenticity claim to the degree the evidence permits, and is transparent about what it cannot conclude. That is a more defensible basis for a claims decision than a visual inspection or a detection score.
How long does a Tier 1 assessment take?
Tier 1 media authenticity screening is returned within 48 to 72 hours of evidence receipt, subject to file complexity and volume.
