Manipulated Image Detection AI for Insurance

Inaza’s Altered Image Detection Model automatically identifies digitally manipulated or tampered images, helping insurers reduce fraudulent claims and ensure claim authenticity.
Detect Fake and Edited Insurance Images

10-20%

Of all P&C claims are fraudulent.

$308 Billion

The amount paid out on fraudulent claims in the US each year.

Detect Manipulated Images & Prevent Fraud

What value does Inaza's AI Image Manipulation Detection model provide to the various teams in Insurance?

Underwriting Teams

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Better Risk Assessment – Stop altered images from impacting how you assess risk and underwrite policies.

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Fraud Prevention – Identify images that have been altered to hide pre-existing damage or commercial vehicle markers.

Claims
Teams

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Instant Image Fraud Detection – Identify manipulated or edited claim photos before payouts.

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Reduce Payout Errors – Prevents fraudulent claims from slipping through manual reviews.

Fraud & SIU Teams

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Advanced Tampering Detection – Flags images with AI-driven forensic analysis.

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Automated Alerts for Suspicious Claims – Ensures high-risk claims receive extra scrutiny.

Smarter Detection, Built for Real-World Fraud

Image fraud is getting smarter. Manual image review simply doesn't cut it anymore. Inaza's fraudulent image detection allows you to out-smart even the most advanced of fraudsters and catch altered images before they impact your bottom-line.
invoice fraud detection
Visual Manipulation Detection
Detect pixel-level manipulation including cloning, splicing, lighting inconsistencies, and compression artifacts. Perfect for identidying photoshopped or digitally editted images.
invoice fraud detection
Metadata Anomaly Analysis
Inspect file properties and hidden data for red flags: timestamp mismatches, device ID swaps, GPS coordinates, inconsistent file structures, and re-exported PDFs.
Cost Control in insurance
AI Generation Detection
Distinguish genuine images from AI-generated fabrications. The model identifies synthetic textures, image irregularities, and metadata artifacts unique to genAI-created or altered images.
invoice fraud detection
Contextual Consistency
Identify inconsistencies between the image content and the provided context such as weather, time, location, or narrative details.
invoice fraud detection
Threshold tuning & explainable scoring
Adjust detection sensitivity to align with your fraud tolerance. Every score includes clear indicators, confidence levels, and visual evidence so compliance and SIU teams can defend each decision.

From Problem to Solution in Minutes - not months.

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Select the Fraudulent Image Detection Solution

Browse and test solutions based on your needs - whether it’s claims fraud, underwriting cleanup, or fleet submission automation. All tools are transparent, tunable, and instantly testable.
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Deploy Into your Workflows

When you're ready to go live, Inaza Central provides fully documented, production-grade APIs for every solution. Whether you're automating invoice validation, underwriting data ingestion, or claims triage - everything is available in a format your developers can implement in hours, not weeks.
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How AI-Powered Altered Image Detection Works

Inaza’s Altered Image Detection Model provides insurers with a seamless, automated way to verify image authenticity.

Image Metadata & Pixel Analysis

Scans image metadata and pixel patterns to detect anomalies or alterations.
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AI-Powered Cross Checking

Compares submitted images against past records to identify inconsistencies.
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Real-Time Fraud Flagging Alerts

Automatically flags suspect images for further investigation.
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Instant Claims & UW Integration

Works within existing, Underwriting, FNOL and claims systems to enhance fraud detection without disrupting workflows.
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