Every day, underwriting teams receive applications across a patchwork of formats: PDFs from brokers, spreadsheets from agents, emails with attachments, scanned ACORD forms. Someone on your team opens each one, reads through it, and manually keys the data into your quoting or policy administration system. It’s repetitive, error-prone, and expensive and it’s consuming hours that your underwriters could spend actually underwriting.
Large language models (LLMs) are changing that. For MGAs willing to move beyond the hype and think practically about implementation, LLM-powered pre-fill represents one of the highest-ROI opportunities available right now.
The Problem with Manual Intake
For MGAs, speed is a competitive differentiator. Brokers have options. When a submission lands in your inbox and your turnaround time is three days while a competitor quotes in three hours, you lose the business, not because your pricing was wrong, but because your process was slow.
The bottleneck is almost always the time it takes to get clean, structured data into the hands of the person making the decision.
Manual data entry also introduces errors. A missed endorsement, a transposed figure, a misread classification code: these aren’t just inefficiencies. They create downstream risk in the form of mispriced policies, coverage gaps, and audit exposure. The larger your submission volume, the more this friction compounds.
How LLMs Pre-fill Insurance Applications
LLMs are uniquely well-suited to the pre-fill problem because they can read unstructured documents, regardless of layout, format, or terminology, and extract structured information from them.
In practice, this looks like the following: a broker sends in a submission package. It might be a four-page PDF, a completed ACORD form, a loss run spreadsheet, and a cover email. An LLM-powered intake layer reads all of it, identifies the relevant fields, resolves any ambiguities, and pre-populates your application with the extracted data, before a human ever touches it.
What was previously a 20-minute manual task becomes a five-second automated one. The underwriter opens the application and sees a pre-filled form ready for review and approval, rather than a blank template waiting to be populated.
The point is redirecting human attention to where it creates value: judgment, risk assessment, and relationship management.
Key Capabilities That Make This Work
For MGAs evaluating LLM-based pre-fill, there are several capabilities that separate a well-designed solution from one that creates new problems.
Document-agnostic extraction. Submissions arrive in every conceivable format. A capable LLM layer should handle PDFs, Word documents, Excel files, and plain text emails without requiring a fixed template. If your solution only works with structured inputs, it’s solving a much narrower problem than the one you actually have.
Field mapping to your specific schema. The LLM needs to understand not just what data is in the document, but how it maps to your internal application fields. This requires configuration, either through prompting, fine-tuning, or integration with your existing data model. A generic model won’t know that your system calls a field “named insured” while the broker’s form says “policyholder.”
Human-in-the-loop validation. LLMs are highly capable but not infallible. The right architecture puts AI in the role of first drafter and keeps your underwriter in the role of reviewer. Every pre-filled field should be traceable back to the source document, so reviewers can verify instantly rather than guess. Confidence scores and flagging for low-certainty extractions give underwriters a clear signal about where to focus their attention.
Integration with your existing tools. For MGAs using Excel-based rating models or existing policy administration platforms, the pre-fill layer needs to connect to where your data actually lives. This is where low-code platforms like EASA add significant value, by bridging LLM capabilities with the Excel logic and operational workflows your team already relies on, without requiring a system replacement.
The Business Case Is Straightforward
Consider a mid-size MGA processing 500 submissions per month. If manual intake takes an average of 20 minutes per submission, that’s 167 hours of staff time per month (roughly one full-time employee) spent on data entry before any underwriting judgment is applied.
Review and exception handling still require human involvement, but reducing manual entry time by 70–80% is a realistic target, which translates to meaningful capacity gains, faster turnaround for brokers, and reduced operational risk.
Where to Start
For MGA leaders thinking about implementation, the practical entry point is a focused pilot, not a wholesale transformation. Start with a single line of business where submissions follow relatively predictable patterns. Define a narrow set of fields to extract. Build in a review step and track accuracy against your ground truth.
The goal is proving that the model performs reliably on your documents, in your context, with your data. Once that confidence is established, scope can expand incrementally.
The technology is ready. The question for MGA executives is whether your workflows are designed to take advantage of it.
EASA enables MGAs to integrate LLM-powered capabilities, including application pre-fill, directly into Excel-based rating models and operational workflows. If you’d like to see how this works in practice, get in touch with our team.