Document extraction in the insurance industry often faces significant challenges due to the inconsistent structure of documents across different states and providers. Many rely on large language models (LLMs) for extraction, but these models struggle in production environments due to their lack of understanding of document structure. A more effective approach involves first classifying the document type before routing it to a type-specific extraction process, which can significantly improve accuracy. Additionally, using vision-language models that account for document layout, fine-tuning models on industry-specific documents, and incorporating human corrections into training can further enhance performance and scalability. This matters because improving document extraction accuracy can significantly reduce manual validation efforts and increase efficiency in processing insurance documents.
