Executive summary

Generative AI is making image manipulation faster, cheaper, and increasingly difficult for traditional fraud controls to detect. This paper examines how insurance fraud detection is evolving from manual review and rules-based systems towards next-generation image forensics that assess authenticity across pixels, metadata, context, and connected claim evidence.

It also presents Bernoly’s multimodal fraud detection architecture, showing how insurers can detect classical manipulation, identify AI-generated content, uncover reused images, combine signals into an interpretable risk score, and route cases through human-centred triage. The result is stronger claims integrity without creating unnecessary friction for legitimate claimants.

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What you’ll read

• The changing fraud landscape: Why generative AI and digital claims are exposing the limitations of traditional fraud controls.
• The evolution of fraud detection: How insurers have progressed from manual reviews and fixed rules to machine learning and multimodal analysis.
• Next-generation image forensics: How modern methods detect copy-move manipulation, splicing, inpainting, and AI-generated imagery.
• Metadata and contextual validation: How timestamps, location data, weather, lighting, and claim narratives can reveal inconsistencies.
• Bernoly’s fraud detection architecture: How multiple forensic models, image similarity analysis, and interpretable risk scoring work together in one workflow.
• Human-AI decision-making: How explainability, triage, and investigator feedback improve accuracy while maintaining human oversight.

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“Ultimately, next-generation image fraud detection systems succeed not because they remove humans from the process, but because they augment human judgment with powerful, interpretable AI insights.”