Ask a claims operation what system it is trying to replace and you will usually get a shrug. There is no system. There is a shared mailbox, a spreadsheet, a folder structure someone set up in 2019, and a group of people who have learned to hold a lot of context in their heads.
That is the real incumbent in insurance document work. Not a competing platform. People, doing it by hand, carefully, and slower than anyone wants.
Document automation for insurance is usually sold as a workflow problem: route the file faster, notify the adjuster sooner, close the loop. Workflow is the easy half. The hard half is that almost none of the paperwork arriving at an insurer was designed to be read by a machine, and every automation you build on top of a reading layer that cannot cope will quietly hand the work back to a human.
Why insurance paperwork is the harder document problem
We read loan files for a living. Bank statements, payslips, tax returns, business registrations, identity documents, photographed at an angle in poor light by someone who does not much care about your extraction accuracy. It is a genuinely hard input, and it is where we do our best work.
Insurance paperwork is a harder version of the same problem, for four structural reasons.
The submitter is not incentivised to be tidy. A borrower who wants a loan will at least try to send a legible statement. A claimant photographing a damaged bumper in a car park at night is documenting an event, not preparing a file. What arrives is what the phone captured.
The document set is open-ended. A loan product has a document checklist. A claim has whatever the incident produced: a repair quote from a workshop with no standard template, a police report in one format this month and another next month, a medical summary printed from a system nobody in your building has seen, a witness statement written by hand on lined paper.
Third parties generate most of it. You control the loss-notification form. You do not control the assessor's report, the workshop invoice, the hospital discharge summary, or the surveyor's photographs. Every one of those is a layout you did not design and cannot standardise by asking nicely.
The numbers have to reconcile across documents. The amount claimed, the amount quoted, the amount the policy covers and the amount previously paid on the same policy all live in different places, in different formats, and the whole job is noticing when they disagree.
This is why template-based capture underperforms so consistently in claims. Template capture works by learning where a field sits on a page. It is excellent when the page is always the same page. Claims intake is the case where the page is almost never the same page. For the underlying distinction, see document intelligence versus OCR.
What actually defeats capture, document by document
It is worth being specific, because "messy documents" is the kind of phrase that lets everyone nod and change the subject. Here is what the difficulty actually consists of.
| Document | What it arrives as | Why template capture struggles | What reading it properly requires |
|---|---|---|---|
| Loss notification | Handwritten form, phone photo of a form, or free text in an email body | No fixed layout; handwriting varies per claimant | Handwriting recognition plus semantic field matching, not zone mapping |
| Damage photographs | Phone images, often at an angle, often at night | No text fields to map at all | Reading plates, chassis numbers and serials off a photograph, then cross-checking them against the policy record |
| Repair or replacement quote | Workshop or supplier letterhead, scanned or photographed | Every supplier uses a different layout; line items are unstructured | Line-item extraction that survives an unfamiliar layout, plus arithmetic validation of the totals |
| Third-party report | Police, assessor, surveyor or medical, often a photocopy of a printout | Degraded scan quality; format changes without notice | Tolerance for poor scans, and classification before extraction |
| Policy schedule and endorsements | Your own PDF, plus amendments issued later | The base document is clean; the amendment history is the hard part | Recognising that a later endorsement supersedes the base terms, and reading both |
| Prior claim history | Extract from your own systems, or a bordereau from a partner | Structured, but rarely in the same shape twice | Normalising it into the same fields the rest of the file uses |
Notice the pattern. Only one row in that table is a formatting inconvenience. The rest are the actual decision. If the endorsement is missed, the coverage answer is wrong. If the quote total is read but never checked against its own line items, an inflated invoice passes. If the plate on the photograph is never compared to the plate on the policy, nobody notices the mismatch.
An extraction that returns the wrong number confidently is worse than one that returns nothing, because the workflow downstream will act on it.
The straight-through rate nobody quotes
Every capture vendor will quote you an accuracy figure. Almost all of them are measured on clean, typed, single-layout documents, because that is the benchmark set the industry built.
The number that matters to a claims operation is different: on your real intake mix, including the phone photos and the photocopies, what percentage of files come out the other end complete enough to act on without someone opening the source document?
That number is the whole business case. It is also the number that collapses when a pilot moves from the sample pack to production, because the sample pack was assembled by someone helpful and production is assembled by claimants. Our position on how to measure this honestly is set out in the guide to document extraction accuracy.
The practical test costs you nothing. Take twenty files from last month. Not your cleanest twenty. Twenty at random, including the ones your team complained about. Run them. Count how many needed a human to open the original.
Run your worst file before you run a procurement process. Start free trial and upload the claim pack your team is currently keying in by hand, or book a demo and we will do it with you.
What changes when rules run instead of people
Reading the file is half the job. The other half is what happens to the answer.
In most claims operations, policy lives in three places at once: the written manual, the training an assessor received, and the habits they developed afterwards. Two experienced people looking at the same file will reach the same outcome most of the time, and the gap between "most of the time" and "every time" is where leakage, inconsistency and complaint volume live.
The alternative is that the rules your team wrote are the rules that run. Not a summary of them, not an interpretation of them. The same conditions, on every case, in the same order, with the reasons recorded.
In practice that means separating two things manual process blurs together:
- Hard gates. The conditions that stop a case regardless of anything else. Coverage lapsed at the date of loss. Claimed amount above the sum insured. Required document missing. These either pass or they do not, and no amount of judgement elsewhere overrides them.
- Scoring. The graded assessment that routes a case towards straightforward settlement, referral or investigation. Weighted, banded, and tuned by the people who own the risk.
Keeping those separate is what makes the outcome explainable. A case that stops tells you which gate it hit. A case that routes to review tells you which factors put it there. That is the same architecture we describe for lending in what a decisioning engine is, and the mechanics do not change when the subject matter does.
One honest boundary is worth stating plainly, because the industry tends to over-promise here. Determinism belongs to the rules layer. Given the same inputs, the same policy version produces the same outcome every time, and you can replay it years later against the exact documents and data that drove it. The reading layer is a different kind of system and we do not claim bit-identical extraction output from it. What we claim is that we show you the source document alongside what we read from it, recalculate what can be recalculated, and flag what does not reconcile, so your team verifies rather than believes.
Where the fraud signal actually lives
Insurance fraud is a large enough number that it distorts the conversation. The Coalition Against Insurance Fraud's research puts the annual cost in the United States alone above $300 billion, which is the kind of figure that gets a detection budget approved and then spent on the wrong layer.
Most of the useful signal in a claims file is not exotic. It is arithmetic and chronology. A quote whose line items do not sum to its total. An invoice date that precedes the loss date. A document whose metadata says it was edited after it was issued. Fonts that change mid-line where a figure was overwritten. A supporting document that reconciles perfectly with one number and not with the one next to it.
None of that is visible unless something read the document properly first. This is the argument for putting tampering detection inside the reading layer rather than bolting it on afterwards: the forensics run on the same pass that produced the data, on the document as submitted, not on a re-render of it.
Two limits, stated because they matter to anyone building a control around this. We detect signs that a document was manipulated. We do not certify that an untampered document is genuine, and we do not natively verify government identity documents or run biometric checks. Those are separate controls, and an honest evaluation keeps them separate. The same discipline applied to lending documents is set out in how to detect fake bank statements.
What we can and cannot tell you about insurance specifically
This is the part most vendor content skips, so here it is directly.
Floowed is the decision platform. The reading layer is Document Intelligence and the decider is the Decision Engine. Every customer we have today came off manual process, not off a competing platform, and the deepest proof we have is in lending, because that is where we have been longest.
What that proof looks like: 180 or more officer hours returned every week at one lender, credit memo preparation moving from roughly 15 hours to about three minutes, one team taking six times the volume without adding people, three times as much statement fraud caught as the manual process was finding, and a 54% cut in review time on clean cases.
Those are lending numbers and we are labelling them as lending numbers. We are not going to restate them as insurance benchmarks, invent a carrier case study, or describe insurance-specific features that do not exist. Our non-lending decision streams are opening shortly, and the honest description of the capability today is narrow and real: we read documents of any quality, including the ones built for humans rather than machines, and we run the rules your team writes on what we read, identically, every time, with the record kept.
If that is the shape of your problem, the mapping is direct. If you need a claims management system, a payment rail or a policy administration platform, it is not, and you should keep looking. We return the decision. We never move money.
How to evaluate this properly
Five things separate a real evaluation from a demo you enjoyed.
- Bring your own worst documents. Not the sample pack. The photocopy of a photocopy. Any vendor confident in their reading layer will welcome it, and the ones who redirect you to a curated dataset are telling you something.
- Ask who can change a rule. If the answer involves an engineer and a release cycle, every policy change carries a queue and a cost, and over two years that usually exceeds the licence.
- Ask what the audit trail retains. Not "we log everything." Specifically: can you reopen a case decided 18 months ago and see the exact documents, the exact extracted values, the exact policy version, and the reason it landed where it did.
- Ask what happens before a change goes live. Replaying a proposed rule change against your historical book, with its real outcomes, is the difference between a considered change and a guess. Supervisory expectations on governance and controls, set out in the IAIS Insurance Core Principles, point the same direction.
- Ask where the reading layer comes from. Several decisioning platforms do not read documents themselves; they connect to someone who does. If your intake is clean that costs you nothing. If it is not, you are buying a second product with its own contract, its own ceiling, and its own finger-pointing when accuracy disappoints.
Information-governance research from AIIM has made the same point about content-heavy operations for years: the constraint is rarely the workflow tool, it is whether the content entering the workflow is usable. On data handling our position is stated rather than implied: we are PDPA-compliant, and the details of processing, retention and access live on the security page rather than in a marketing claim.
Frequently asked questions
Does this replace our claims management system?
No. We sit in front of it. We read the documents, turn them into data your team can verify, run your rules on that data, and hand the outcome and the record to whatever system administers the claim. We do not administer policies, issue payments or manage reserves.
What happens when the platform is not confident about a value?
It surfaces the issue rather than resolving it quietly. We do not fabricate a figure to make a document reconcile, which is a real behaviour in this category and a genuinely dangerous one. Where something does not add up, the case routes to a person with the discrepancy shown and the source document alongside it. The design of that handoff is covered in human-in-the-loop document automation.
Do you have insurance customers today?
Our shipped track record is in lending. Non-lending decision streams, insurance among them, are opening shortly. We would rather tell you that than show you a case study we cannot stand behind. The capability that transfers is the reading layer and the rules layer, and both are demonstrable on your own documents in a single session.
How long does it take to get live?
Weeks of proper configuration, then live. That is deliberately not a speed brag. The work is defining your rules precisely, mapping the document set, connecting the systems that need the output, and testing against real files. Compared with a tier-one implementation running six to eighteen months with a consulting engagement attached, it is a different order of magnitude, and that is the comparison that actually matters.
Can it handle documents in more than one language?
Yes, including files where the form is in one language and the attachments are in another, which is common in claims and defeats a surprising amount of capture tooling.
What does it cost?
We quote rather than publish a rate card, because the number genuinely varies with volume, document difficulty and how many systems need connecting. Messy input costs more to read than clean input, and pricing that pretends otherwise is pricing that will surprise you later. The pricing page sets out how a quote is built.
See it on your own documents
The fastest way to settle whether any of this applies to you is to stop reading and run a file. Book a demo and we will put your messiest claim pack through the platform, show you the analysed output next to the source, and walk through how the rules would run on it. To try it yourself first, start free trial.
Last updated 2026-08-04 by Kira.