A property submission arrives with a Statement of Values spread across six spreadsheet tabs, three more schedules in PDF, and a scanned page with handwritten notes across 340 locations. Re-keying all of that by hand takes most of a day, so teams often sample a few locations instead of checking every one, or let the account sit in the queue.
Plenty of tools already claim to automate this. Most handle one piece of it, cleaning up a schedule before a broker sends it, or reading a single tab, without reconciling the whole file. That's the gap AI closes.
How does AI extract data from a Statement of Values? It reads the file in whatever format a broker sends it, normalizes the layout into one consistent schema, then pulls construction, occupancy, protection, and exposure (COPE) data, along with total insured value (TIV), for every location. It reconciles the totals across every tab and routes anything it isn't confident about to a reviewer before the data reaches an underwriter.
This accuracy matters because errors in this data distort pricing downstream. Getting it right the first time counts more than getting it fast.
Here's the full process, from broker file to rating-ready data, in 6 steps.
Reads every format. AI built for this work reads the file exactly as it arrives. There's no separate setup step for each broker and no template to configure ahead of time. The system opens the file, figures out what kind of document it's looking at, and starts extracting right away. It doesn't matter whether that's a clean spreadsheet or a scanned page with a coffee stain on it.
Normalizes the schema. Every broker builds their exposure schedule a little differently, with column order, field names, and even which details share a tab changing from one submission to the next. AI normalizes everything into one consistent schema before it pulls any values out. The underwriter, the actuary, and the workbench all see the same structure regardless of which broker sent the file or how they laid it out. This is the mapping work an underwriting assistant used to do by hand, tab by tab, on every submission.
Extracts construction, occupancy, protection, and exposure (COPE) data, along with total insured value (TIV) data. Once AI normalizes the schema, it pulls COPE data, plus TIV, for every location. This isn't a summary of the account. It's detail for every single location, each building with its own construction type, its use, its fire protection class, and its exposure to nearby hazards, tied back to one specific address and one specific value. A schedule with 340 locations produces 340 sets of COPE attributes, not one blended estimate for the whole account.
For example, a broker's schedule lists a location's value as "Bldg Value" on one tab. Another tab calls the same figure "Structure TIV." AI built to read Statements of Values recognizes both as the same field. It extracts the value once, correctly, instead of treating them as two separate numbers to untangle later.
Reconciles totals across tabs. AI cross-references every tab's totals and catches mismatches before they reach pricing. A Statement of Values with multiple tabs almost always states a total TIV somewhere, and that number has to match what the per-location data adds up to. It often doesn't, maybe because a location got duplicated, a tab didn't get updated, or a formula broke somewhere upstream. AI flags gaps, duplicates, and inconsistent values instead of passing them through quietly.
Grounds every value and routes exceptions to review. Every extracted value carries a confidence score and a link back to its exact spot in the source file. Anything below the threshold routes to a reviewer instead of going straight to the underwriter.
Delivers system-ready data. From there, the exposure data lands in the underwriting workbench, the rating engine, or the data warehouse, whichever system the carrier uses to price the risk.
Speed on its own isn't the win here. A fast quote built on exposure data nobody checked is still a fast way to mispriced risk. Underwriting teams that have been burned by extraction tools that miss edge cases aren't looking for another partial fix. They want something that holds up on the messiest file in the stack. And that's what AI does. It reads, normalizes, and reconciles every tab before anything reaches pricing. Fast and right beats fast alone.
The underwriter still owns the price, the terms, and the bind decision. What changes is what lands on their desk first. Instead of spot-checking a handful of locations or letting the account wait its turn, AI checks every location and flags the exceptions upfront. That's a full risk picture on every account, not just the ones with time to spare.