Claims Decision Assurance
A claim can appear straightforward until one policy-relevant fact changes.
The situation
AI may assist with claims triage, fraud screening or authorization. Coverage dates, loss type, documentation, claim amount, prior claims, fraud indicators and authorization thresholds can all affect the appropriate decision.
Testing thousands of historical claims can demonstrate performance on those examples. It does not necessarily demonstrate how the decision behaves between those examples — and many insurance decisions occur near policy boundaries.
How AnantVerify evaluates it
AnantVerify can create controlled transformations around a claim:
- Move the loss date across a coverage boundary.
- Change a non-material attribute that should not affect the decision.
- Move the amount above an authorization threshold.
- Remove required documentation.
- Introduce conflicting evidence.
- Combine individually acceptable conditions.
Business value
For every transformation, a contract specifies the required decision behavior. The insurer can see not only whether a model performed well historically, but whether its decisions behave correctly when policy-relevant state changes.
When historical practice disagrees with current policy, AnantVerify does not automatically declare history correct — it surfaces the disagreement for review.
Historical decisions are valuable evidence. They are not automatically ground truth.