Why OCR Alone Isn’t Enough for Insurance Document Processing

In the insurance industry, data drives decisions. Every quote, claim, compliance check, and customer interaction relies on the accurate extraction and interpretation of information – much of which resides in loss runs, submissions, first notice of loss (FNOL) reports, policy decks, handwritten notes, email attachments, and myriad other unstructured documents.

Optical character recognition (OCR) has long been a bridge between paper-bound and PDF-locked documents and digital systems. But, while OCR is a powerful tool for digitizing text, it wasn’t built to understand and convey what that text means, let alone operate within the nuanced context of insurance.

That’s why insurance businesses are turning to a more modern, flexible approach: pairing OCR with large language models (LLMs) trained specifically on insurance language and workflows. This hybrid model doesn’t just recognize text – it understands it.

Here are some important things to know.  

The Basics of OCR in Insurance

OCR technology does one thing and does it very well. It scans PDFs, images, faxes, and scanned forms and converts the visual characters into machine-readable text. It's a key enabler of digitization and has been widely used in claims processing, underwriting submissions, and policy administration for decades.

Historically, OCR has relied on templates. These templates define where certain fields are expected to appear – e,g., “the insured’s name is always in the top-left corner” – and the system extracts text accordingly.

Template-based OCR can be highly accurate and fast, but only when documents match the expected structure. When formats vary – as they often do in insurance – it becomes a game of constant template tweaking and rule rewriting. 

Benefits and Drawbacks of Template-Based OCR 

Template-based OCR can be a powerful tool for processing structured insurance documents at scale, offering speed and precision when forms are predictable. However, its limitations become clear when flexibility, context, or scalability are required – especially in dynamic or unstructured environments.

Benefits:

• High accuracy for structured forms – OCR works well on consistent document types like ACORD forms or standardized invoices.
• Fast processing – Once configured, it can run in milliseconds.
• Predictable output – The output fields are exactly what the template defines.
• Cost-effective for high-volume use cases – Especially when dealing with a narrow range of document types.
• Developer control – You can precisely target which areas to read or ignore.
  
  

Drawbacks:

• Low flexibility – If a submission format changes or the sender uses a different form layout, accuracy plummets.
• Manual upkeep – Each template must be built, tested, and maintained over time.
• Poor scalability – Supporting many carriers, brokers, or document types quickly becomes unsustainable.
• No context – OCR sees letters, not meaning. It can’t resolve ambiguous fields, interpret abbreviations, or infer missing data.
• Struggles with unstructured data – Think of documents like loss narratives, email threads, or handwritten notes.
• Limited multilingual support – Typically requires extra configuration to handle documents in other languages.

In short, template-based OCR is great at digitizing information, but not at understanding it.

LLMs Are the Pathway to Understanding Insurance Data

LLMs, particularly those designed for multimodal input (text and image), are redefining what's possible with OCR. These models don’t rely on rigid templates. Instead, they use machine learning trained on massive datasets – including industry-specific documents – to interpret both text and its context.

Hybrid approaches to OCR/LLM synthesis are becoming commonplace – and this is true in insurance. In insurance use cases, OCR extracts the raw text, then an insurance-trained LLM processes that text to extract structured data, answer queries, summarize content, and more.

OCR + LLMs in Insurance Is the Best Approach

Pairing OCR with LLMs unlocks a smarter, more flexible way to process insurance documents. The hybrid approach is especially well-suited for the industry’s complex, varied, and often unstructured data.

What’s Different with LLM-Based Systems? 

• Contextual understanding – LLMs grasp what the terms “Named Insured,” “Reinstatement,” or “Admitted Company” mean – even if those words aren’t explicitly labeled.
• Adaptability – They can handle various document formats (PDFs, scanned handwritten notes, multi-column emails, etc.) without predefined rules.
• Higher accuracy processing complex documents – Especially when text is freeform, tables are inconsistent, or handwriting is involved.
• Simplified configuration – No need to create templates – just prompt the model with what you’re looking for.
• Multilingual by design – Most modern LLMs understand dozens of languages natively.
• Advanced capabilities – Beyond extraction, LLMs can summarize a loss run, highlight missing information, or flag inconsistencies.

The Tradeoffs That Insurance Leaders Should Know

While LLM-based approaches are powerful, they come with considerations, including:

• Processing power and latency – Running LLMs, especially on high volumes of documents, can be more expensive and slower than traditional OCR.
• Risk of hallucinations – LLMs may sometimes generate plausible but incorrect data – especially when asked to “fill in the blanks.”
• Lack of confidence scores – Many LLMs don’t clearly indicate their confidence in the extracted data.
• “Black box” processing – Unlike rule-based systems, auditing decisions made by LLMs can be more complex.

In response, these challenges are being addressed with insurance-specific LLMs that incorporate domain knowledge, embed safeguards against hallucinations, and flag low-confidence extractions for human review (a process known as human-in-the-loop, or HITL feedback functionality).

The bottom line: for modern insurance businesses, OCR is only a “part-way” solution.

Insurance runs on documents that are increasingly diverse, complex, and unstructured. While template-based OCR still plays a vital role in insurance automation, especially for standardized forms, it can’t keep up with the variability and nuance of modern insurance workflows.

That’s where insurance-trained LLMs as part of a hybrid solution come in. By pairing OCR with today’s advanced models, insurers can unlock faster processing, better accuracy, deeper insights – and most importantly, a scalable path to automation.

If your team still relies predominantly on templated OCR solutions, it might be time to explore how AI transforms document intelligence in insurance. 

Curious how insurers are putting Roots to work? Check out our case studies to see insurance-specific AI in action.