From Text to Multi-modal, Why Broader RAG can help insurers.
.svg){kind=icon}
Beyond Text: Why Multimodal RAG Changes Everything for Insurance
The insurance industry has always been visual. Claims adjusters photograph damage, underwriters review building blueprints, brokers analyze site maps, and everyone scrutinizes documentation that mixes text, tables, charts, and images. Yet most RAG systems treat insurance as if it's purely textual, missing critical information embedded in photos, diagrams, and visual documentation. This blind spot isn't just limiting. It's dangerous in an era of sophisticated fraud and evolving cyber threats.
Consider what your brokerage handles daily: claims photos showing property damage, certificates of insurance with embedded logos and seals, construction site photos for liability assessments, screenshots of cyber incidents, and complex policy schedules mixing text with visual elements. A text-only RAG system might read the words on a COI but miss that the logo is fabricated. It might process a claim description but fail to notice that the damage photos are AI-generated. It might analyze a cyber claim narrative but overlook the phishing email screenshot that reveals the attack vector.
The stakes are escalating rapidly. AI-generated images have become virtually indistinguishable from real photos, making fraudulent claims harder to detect. Sophisticated criminals create convincing fake documents complete with authentic-looking logos, watermarks, and signatures. Cyber attacks increasingly use visual elements like spoofed email interfaces, fraudulent websites, and manipulated screenshots to deceive victims. When your RAG system can't "see" these visual elements, it's operating with incomplete and potentially misleading information.
Multimodal capabilities also unlock valuable insights hiding in plain sight. Property condition photos contain risk indicators that no text description captures completely. Aerial imagery reveals exposure accumulations that written reports might miss. Hand-drawn diagrams from claim files often explain complex incidents better than pages of text. Even simple things like being able to process tables and charts from carrier reports, rather than losing that structure in text extraction, can dramatically improve the accuracy of your AI-powered insights. The brokerages that can leverage this visual intelligence will have a massive advantage over those stuck in a text-only world.
How Cova's Multimodal RAG Protects and Empowers Your Brokerage
Our team can build multimodal RAG system processes and understands visual information as naturally as it handles text, creating a comprehensive intelligence platform that matches how insurance actually works. When analyzing a complex claim, Cova doesn't just read the adjuster's notes. It examines the photos for consistency, checks for signs of digital manipulation, and correlates visual evidence with written descriptions. This complete picture enables faster, more accurate decisions while dramatically reducing fraud exposure.
These tools could be used for visual authentication capabilities are particularly powerful for fraud prevention. When processing claims photos, Cova can detect telltale signs of AI generation or digital manipulation including unnatural lighting patterns, impossible shadows, and pixel-level anomalies that human reviewers might miss. It can verify that vehicle damage photos match the claimed incident date by checking environmental details, validate that property damage is consistent with the reported cause, and flag when multiple claims suspiciously share similar images. This isn't just about catching fraud after it happens; it's about preventing it from entering your pipeline in the first place.
Other Cova capabilities
