Trained on the work itself
Claims adjudication produces its own training data: the claim as it arrived, the plan rules applied, the examiner’s decision, the appeal, and the payment outcome. Ebix has been recording that loop for over three decades. Models built on it learn from what adjudicators actually decided, not from generic text, and every subsequent settlement cycle verifies their predictions against what really happened.
The intelligence layer attaches to a claims engine already running in production, with 400+ built-in reports and full claim lifecycle management, so no core migration stands between you and the results.
Where the models sit in the claim path
The models score claims between the rules engine and settlement. Rules keep deciding; the models decide where people are needed.
Claims intake
EDI, paper, portal
Rules engine
Plan rules, eligibility, accumulators
Decision models
Scored against adjudication history
Settlement or routing
Auto-settle, or route with context
Human review
Examiners decide, release, and teach the models
Three capabilities
Predictive auto-adjudication
Decision models trained on historical outcomes identify which claims can settle without touch. The rules engine remains the authority; the models expand what it can safely clear. The result is a higher auto-adjudication rate and a smaller manual queue.
- Rules engine stays the system of record
- Models score each claim before the settlement cycle
- Automation expands within the plan rules, never around them
Intelligent exception routing
Claims likely to pend are flagged before they pend, with the probable reason and the relevant history attached, and routed to the examiner best suited to resolve them. Examiner time concentrates on claims that need judgment.
- Pend reason predicted and attached to the claim
- Routing by examiner specialty and workload
- Member and provider history in view on arrival
Grounded drafting
Determination letters, appeal responses, and member explanations drafted from the plan document and the claim record, with citations. A person reviews and releases every item before it leaves the system.
- Every draft cites the plan provision it relies on
- Human review and release on every letter
- Full log of drafts, edits, and approvals
How an engagement runs
Three phases, each with its own deliverable. The first phase is data work on your own history, so value lands before any model goes live.
Discuss your claims environmentData foundation
Extract and structure your adjudication history from the core system. Scoped, concrete work that stands on its own.
Predictive models
Train and validate auto-adjudication and routing models on your own history, measured against your current baseline.
The assistant layer
Grounded drafting and conversational access for examiners and service teams, with role-based permissions and audit logging.