INSURANCE
AI you can explain to and the policyholder.
Underwriting, claims, and customer service with AI decisions you can trace, test, and defend.
What we hear
“We have ML models in underwriting and pricing that nobody can fully explain anymore.”
“Claims teams are trying generative AI tools, and we don’t know what data goes into them.”
“Regulators are asking how we test our AI for unfair discrimination.”
“Every new AI use case starts with months of data cleanup.”
"Can you explain why this claim was flagged?"
A claims ML model flags a claim for fraud review. The policyholder complains, and the state insurance department asks why. Many states have adopted the NAIC’s model bulletin on insurers’ use of AI, so that question needs a ready answer. A3R sets it up so the answer exists before anyone asks:
- A decision record: every flag records which model version, which data, and which factors drove the score.
- Proxy testing: factors that can stand in for protected characteristics are tested before release and monitored after.
- A person decides: an adjuster reviews every flag before action is taken, and that decision is recorded.
- Monthly monitoring: flag rates are reported across customer groups and regions, so drift is caught early instead of in a complaint.
What you get
AI inventory with risk tiers
ML models, LLM models, and AI inside vendor software.
Unfair discrimination testing
Before release and on a schedule after.
Decision records
For every AI-assisted claim and underwriting decision.
Vendor AI review
What the vendor model does, what data it uses, and what you can verify.
Examiner-ready documentation
Produced as part of normal operations, not assembled when asked.
Data readiness plan
So the next use case doesn’t start with months of cleanup.
Start with the assessment.
In about four weeks, you’ll know what AI you’re running, where you stand on the maturity curve, and what to fix first.