INSURANCE · SUBMISSION INTAKE AND TRIAGE
Score every submission on what will actually bind
A submission arrives as a broker email, an ACORD PDF and a scanned loss run, and an underwriter spends most of a day keying it before deciding whether it is even in appetite. The ones that arrive on a busy day are never quoted at all. OutcomeCatalyst reads all three, computes the loss history, scores appetite and bind likelihood against your own bound book, and ranks the queue by what will bind and how long is left.
Policy intelligence, in plain terms
A carrier already owns every fact it needs to price a submission correctly, reserve a claim accurately, and prove a policy file to a regulator. Those facts are in email, PDFs and scans rather than in fields, so no system can act on them. Policy intelligence reads the documents and turns them into decisions your team can work.
From document to decision, in four steps
Nothing is re-keyed and nothing is replaced. The systems you run stay exactly where they are.
Why this is worth doing in P&C
Underwriting capacity is the binding constraint at most carriers and MGAs. Brokers send far more than any team can quote, so the queue is worked first-in-first-out and the best risks age behind the worst. Bind rates fall sharply after the first day, which means the premium does not go to the best quote, it goes to whoever answered.
Claims leakage behaves the same way. The subrogation right, the apportionment argument and the fraud signal are all documented somewhere in the file. They are missed not because adjusters are careless but because reading every page of every file is not a job a person has time to do. Subrogation rights lapse on a statutory clock, so a recovery that is found late is a recovery that is gone.
Audit exposure is quieter and arrives all at once. A regulator names a policy and you have days to produce a complete, internally consistent file. Whether you can depends on work done months earlier, across systems that were never reconciled to each other.
All three problems have the same root cause. The facts that decide the outcome live in documents, and the systems of record only store fields. Joining the two is the whole job.
Agentic AI in insurance, without the hand-waving
Three words get used interchangeably by vendors and they are not the same thing. The difference decides whether you get a demo or a result.
Most agentic AI pilots in P&C fail for a reason that has nothing to do with the model. An agent asked to triage a submission needs to know what the account is, what you have already quoted for it, what your appetite says, and what the loss history shows. That sits in four systems with no shared identifier. The agent has no path to walk, so it guesses, and a confident guess on a $2M limit is worse than no answer at all.
The agents here are deliberately narrow. Each has one job, a defined set of sources it may read, a written standard to check against, and a person who approves before anything leaves the building. That is what makes them safe to run against real money, and it is why they survive an audit.
The data layer agentic AI actually needs
Every workflow above runs on one layer. Building it is most of the work, and it is the part nobody demos.
This is the part most vendors skip, because a layer does not demo well. It is also the reason one piece of infrastructure supports underwriting triage, claims recovery and file audit at once, instead of three separate tools each rebuilding the same context badly.
It also compounds. The second workflow is faster to stand up than the first and the fifth is faster still, because the entities, the ontology and the connectors already exist. Most of what a new workflow needs is already sitting in the layer.
What else runs on the same layer
Once the layer exists these are weeks of work rather than months, because they read the same resolved entities.
Built for the stack a carrier actually runs
These are the systems referenced in the workflow above. Anything with an API, a database or an export can be connected, including systems built in-house decades ago.
Questions carriers and MGAs ask first
Does this make underwriting or claims decisions?
No. It produces drafts, rankings and comparisons for a licensed person to review and sign. It does not bind coverage, set a reserve, deny a claim, or make a coverage determination. Every output cites the document it came from so a human can check the reasoning before acting.
How is this different from our policy administration system?
A policy admin system is authoritative about the fields somebody typed into it. It cannot tell you that page three of a police report assigns fault to a third party, because that page was scanned into a document store and never keyed. This reads the documents and connects what they say to the policy and claim records.
We already have a data warehouse. Why is this different?
A warehouse is excellent at joining structured tables. The problem in P&C is that the deciding facts are unstructured, sitting in PDFs, scans and free-text notes. This turns those into structured facts first, which is the step a warehouse assumes has already happened.
What about PII and claimant data?
Deployment models include running inside your own environment so data never leaves your control. Access is role-based and every read is logged. The reference architecture is designed for carriers with existing SOC 2 and state privacy obligations.
How long does implementation take?
Most engagements are live in four to six weeks on a first workflow, because nothing is migrated and no system is replaced. The first workflow is usually chosen for having a measurable dollar value, which makes the result easy to check.
What happens to our existing integrations?
They keep running. This reads from your systems and writes drafts back into them, so underwriters and adjusters continue working in Guidewire or whatever they already use. There is no new interface for the team to adopt.
Can it work if some of our systems are old?
Yes. Older policy admin systems are common in P&C and usually expose data through a database, a scheduled extract or a flat file. Any of those is enough to connect.
How do you handle documents that are poor-quality scans?
Loss runs and police reports are frequently faxed or scanned at low quality, so extraction confidence is scored per field. Low-confidence fields are surfaced for human confirmation rather than being used silently, and the source page is always linked.
AI submission triage for carriers and MGAs: common questions
What does the workflow read?
The whole submission rather than the summary. The ACORD forms including the sections left blank, the loss runs even as scans, the vehicle or location schedules, and the broker email, which usually carries context that never makes it onto a form. Every extracted field keeps a link to the page it came from.
Where does the data come from?
From the submission itself, plus sources you already subscribe to. ISO ClaimSearch for prior claims, including ones filed under a former entity name, business registries for entity history, and your own bound book for how similar risks have actually performed and which brokers bind what they send.
Does it decline or quote on its own?
Neither. It scores appetite, computes the loss ratio, flags the gaps worth asking about and drafts the response, and an underwriter decides. The value is that a submission arriving on the busiest day of the month gets read as carefully as one arriving on the quietest.
