What is Coverage Reasoning?
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Quick Answer
Coverage Reasoning is the AI process of analyzing insurance policy language, exclusions, and endorsements against claim facts to determine whether coverage applies. It interprets policy terms, evaluates applicability of exclusions, and provides explainable coverage determinations with citations to specific policy provisions.
Definition
Key Points
- Interprets complex insurance policy language using NLP
- Maps claim facts to policy coverages and exclusions
- Cites specific policy provisions for every determination
- Handles multi-policy scenarios and coordination of benefits
- Identifies ambiguities that require human adjuster review
- Provides confidence scores and alternative interpretations
When NOT to Use This
- For novel policy language not seen in training data
- When state-specific case law heavily influences interpretation
- For policies with extensive manuscript endorsements
- When claim facts are incomplete or contradictory
Frequently Asked Questions
How accurate is AI coverage reasoning?
AI achieves 85-95% accuracy on standard policy forms (ISO, AAIS) with common claim scenarios. Accuracy is lower for manuscript policies and novel claim situations. Human review is recommended for edge cases.
Can coverage reasoning handle exclusions?
Yes. The AI evaluates whether exclusions apply and whether exceptions to exclusions bring coverage back. It reasons through the hierarchy: coverage grant → exclusions → exceptions to exclusions.
What happens when policy language is ambiguous?
The AI flags ambiguity, provides multiple reasonable interpretations, and escalates to human adjuster. It applies rules like 'interpret against the drafter' where appropriate.