Executive summary

As generative AI makes image manipulation faster and virtually undetectable to the naked eye, insurance fraud is reaching a breaking point. This paper explores the shift from traditional rule-based detection to advanced AI image forensics. By analyzing pixels, metadata, and environmental context, insurers can identify synthetic images and tampered damage in real-time, protecting their loss ratios without sacrificing a seamless customer experience.

What you’ll read

• The fraud challenge: Why digital claims and generative AI are accelerating fraud risks.
• Evolution of detection: From rule-based systems to machine learning and deep learning.
• Image forensics: Detecting manipulation through pixel-level, metadata, and statistical signals.
• AI-generated fraud: Identifying synthetic and diffusion-model images.
• Multimodal detection: Combining images, text, metadata, and telematics for stronger fraud signals.
• Practical architecture: How insurers can build scalable, real-time fraud detection pipelines.

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"Image forensics is no longer optional. it is becoming a critical component of modern fraud risk management."

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