Property claims
Water, fire, window, furniture, and other property evidence compared with dates, files, and related photos.
Claims & warranty evidence
Fraudulent Claims Detection via Image Forensics
Check claim photos before making a payout decision
essential-fd checks claim and warranty images for technically verifiable signs of manipulation and inconsistency. Identify edited, reused, downloaded, or historically inconsistent files before they influence payout, repair, or replacement decisions.
Low-friction checks
Detect suspicious visual evidence without slowing down every claim
essential-fd analyzes the existing submitted file in the background. No extra photograph, application, or customer action is required for the initial check. Every flag is linked to a specific technical finding.
- Submitted in an earlier claim
- External editing software traces
- Created as a screenshot
- Capture date conflicts with metadata
- Claim photos have inconsistent sources
- Downloaded or unexpectedly processed
- Embedded preview differs from image
- Resized, cropped, or recompressed
- Device information conflicts
- Missing expected channel characteristics
Workflow coverage
Image fraud in insurance and warranty workflows
Vehicle claims
Scratches, dents, accident scenes, components, and repair evidence checked for reuse and inconsistencies.
Consumer electronics
Evidence for phones, televisions, laptops, appliances, and other protected devices.
Bicycle and mobility
Photos of bicycles, e-bikes, scooters, and components combined with invoices and serial numbers.
Warranty and repair
Before-and-after images, defect photos, packaging evidence, and repair documentation.
Travel and baggage
Damaged luggage, missing items, receipts, and related visual evidence.
How it works
Claim image forensics in four steps
The image is submitted
The policyholder, customer, repair provider, or handler uploads it through the existing process.
File-level checks run
Analyze metadata, encoding, source traces, compression, duplicate relationships, and other findings.
Connect result to claim
Return OK or FLAG with claim number, file type, loss date, policy data, and explanation.
Your process decides
Continue or route the flagged file to a claims handler, investigator, or specialist queue.
Claims-specific analysis
Examples of image checks
Reused claim photos
Compare new submissions with prior files to identify exact or technically related damage images.
Capture time inconsistencies
Compare reliable timestamp data with loss, repair, and submission dates.
Editing software traces
Assess metadata and encoding associated with editing, screenshot, document, and export apps.
Screenshot detection
Identify characteristics of screen captures that may hide original source and context.
Cross-photo consistency
Compare device, session, dimensions, orientation, encoding, and processing history.
Externally sourced images
Find naming, software, encoding, and metadata traces inconsistent with direct camera upload.
Connected case evidence
Combine image findings with the claim
Use image evidence together with dates, policy information, invoices, repair estimates, serial numbers, device information, previous claims, customer data, document findings, and internal rules.
An earlier image match plus an invoice with changed values creates a clearer review case than either finding alone.
- Claim and loss dates
- Policy information
- Invoices and receipts
- Repair estimates
- Serial and device numbers
- Previous claims
- Customer and supplier data
- Document forensic findings
Reviewer clarity
Explainable findings for claims handlers
A claims handler receives a concrete reason, not a vague message that a photo appears suspicious.
Prior claim match
This image matches evidence submitted in another case.
Date inconsistency
The available capture time conflicts with the reported loss.
Unexpected source
The file was created as a screenshot or by external editing software.
Claim-set mismatch
Related photos came through different processing workflows.
Operational value
Reduce unnecessary investigations
Hard, explainable findings let claims organizations screen more images, prioritize concrete evidence, reduce avoidable review, improve consistency, document flags, and preserve a simple customer journey. The final decision stays with your organization.
Add image forensics to your current claims process
API
SFTP
OneDrive / SharePoint
Web application
Before payout
Add explainable image forensics to your claims workflow
Screen claim and warranty photos before they influence a payout decision.
FAQ
Frequently asked questions
Can image forensics prove that an insurance claim is fraudulent?
No. It identifies technical findings for assessment alongside claim details and other evidence.
Can essential-fd detect the same image in several claims?
Yes, exact duplicates and, depending on configuration, related versions can be identified.
What if the customer submitted the image through WhatsApp?
Messaging can resize, rename, remove metadata, and recompress files. Checks should account for that workflow; messaging alone should not create a fraud flag.
Can you verify when a damage photo was taken?
Reliable remaining timestamps can be compared with event dates, but timestamps may be missing, changed, or removed.
Does essential-fd automatically reject a claim?
No. The insurer or warranty provider determines how the result is used.
Can essential-fd check documents in the same claim?
Yes. Images can be analyzed with PDFs, invoices, receipts, estimates, and other files.