Sectors / Insurance
Underwriting fairness and claims explainability, on governed data.
Pricing, underwriting and claims models depend on internal, third-party and external data. aerais helps you show what fed each decision.
Why it matters
Why governing data and AI together matters in insurance
Insurers use data to decide who is covered, at what price, and how a claim is handled. As models and external data sources multiply, fairness and explainability questions arrive from customers, supervisors and internal audit.
Those questions are answered on the data side as much as the model side. Which attributes were available, which were excluded, where a third-party score came from and whether it was quality checked all belong in the same evidence trail.
Typical questions
Five questions we hear in this sector
- Which data attributes and third-party scores feed underwriting and pricing models?
- Can we explain a claims decision to a customer or supervisor in terms of its inputs?
- How do we test for unfair outcomes, and on which governed data?
- Who owns approval of an external data source before a model uses it?
- How do we withdraw or correct data when a customer exercises a privacy right?
Chain of custody
How the chain applies
Data plane controls here are classification of health, financial and claims data, lineage across policy and claims platforms, quality rules on rating and fraud inputs, and privacy operations for consent and access requests.
These seam loops tend to matter most:
Quality-gated release
A pricing or triage model should not ship on data that failed its checks. A quality gate makes release conditional on evidence.
Data-to-AI lineage
Claims explainability needs the path from a decision back to claim records, enrichment and model version.
Unified control crosswalk
Insurers face fairness, privacy and AI rules together. A single crosswalk avoids evidencing the same control three times.
Entry points
Where engagements usually begin
In insurance, a first engagement often centers on an AI risk exposure diagnostic on underwriting and claims models, alongside quality and classification work on the data they use. Most organizations start from AI governance and bring in data governance as the chain extends. A fixed-scope diagnostic is the usual first step; see engagements for how that is structured.
Regulatory context
Regulatory context, at a high level
Insurance supervisors and regulators have issued guidance on the use of AI, models and external consumer data, including anti-discrimination expectations (for example the NAIC model bulletin on AI in the US). Privacy law such as GDPR and PIPEDA applies to policyholder data, and the EU AI Act treats certain life and health insurance uses as high risk.
This is general context only. Requirements vary by jurisdiction and change over time, so confirm current requirements with counsel and regulators. Alignment with a standard is not certification.
Diagnostic
What a diagnostic would look at here
- Inventory of underwriting, pricing, fraud and claims models
- Third-party and external data sources and how each is approved
- Lineage from claims decisions to inputs and model versions
- Fairness testing practice and the data it relies on
- Handling of privacy requests across policy and claims systems
Each item is examined against the chain of custody: whether the control exists, whether it runs inside the systems rather than on paper, and whether it leaves evidence a reviewer can follow. Nothing is assumed from documents alone, and findings are written for executives and boards as well as for practitioners.
The output is a current-state maturity view, risk exposure and a prioritized roadmap. Request a diagnostic to scope it for your organization.
FAQ
Frequently asked questions
Can governance help with fairness questions?
It supports them. Governed data, documented attributes and retained evidence let your teams run and defend fairness testing. aerais does not certify that a model is fair.
Does this cover third-party data and scores?
Yes. Third-party and external sources are part of the chain of custody, with approval, quality checks and lineage recorded like internal data.
How does a diagnostic help an insurer?
It shows where underwriting, pricing and claims models depend on ungoverned data, and produces a prioritized roadmap for closing those gaps.
Contact
Make your data and AI provable.
Start with a diagnostic: current-state maturity, risk exposure and a prioritized roadmap. We aim to reply within two business days.