Claims Adjudication
A claim is filed, and it has to be decided against a contract no one has read in a year.
The decision
A claim comes in. Coverage depends on the contract terms, the component's history, the shop's track record, and a few faint fraud signals — and the customer is waiting. Approve, deny, partial-pay, request more, or escalate?
Why it's hard today
The contract is a PDF, the prior claims are in one system, the shop's pattern in another, the fraud signal is subtle. Adjudicate too fast and you leak loss; too slow and the customer churns. Consistency across adjusters is nearly impossible by hand.
Your claims system stores the claim; it doesn't read the contract, recall this shop's history, or weigh the fraud signal against the SLA. It routes a task, not a decision.
What AgentForgeOS does
It assembles the picture you can't see, and makes the call under your rules.
Approve at the contracted rate with the partial exclusion flagged — within adjuster authority — or escalate, with the contract clause and the fraud signal surfaced, pending sign-off.
- Contract terms and coverage
- Claim and component history
- Shop / provider track record
- Fraud and anomaly signals
- SLA and authority limits
- Coverage rules and exclusions
- Authority limits by role
- Regulatory compliance
- Reserve thresholds
⚠ Coverage ambiguity — claim #88412, component out of standard window
Evidence assembled
- Contract covers component; one exclusion applies partially
- Shop history clean; 0 anomalies in 24 months
- Fraud score low; documentation complete
- Within adjuster authority limit
Recommendation · approve at contracted rate, flag partial exclusion
Policy: within authority · compliant · reserve threshold ok · awaiting adjuster sign-off
What improves
- A tighter loss ratio
- Faster, more consistent cycle time
- Defensible, audit-ready decisions
Every adjudication is preserved with its reasoning, so similar claims are decided consistently — and the patterns (a shop, a component, a fraud signature) sharpen with each one, and with every book it runs in.
Underneath, this is the same operating model the rest of the platform runs: verified context is assembled, options are weighed and adversarially challenged, the decision is governed by your policy, and the outcome is learned. The decision changes from one of these to the next. The architecture does not.
See how it worksThis is exactly how your team works today.
Only now the decision is assembled, governed, and remembered — instead of made from memory and lost.