Artificial intelligence is transforming how insurers assess claims, price risk, and streamline underwriting. But as automation expands, so does accountability. Regulators, partners, and customers now demand proof that AI-driven decisions are not only efficient but also fair, transparent, and explainable.
The next evolution in insurance isn’t just adopting AI—it’s governing it. Auditing AI introduces intelligent oversight to test models for bias, drift, and decision integrity. For claims and underwriting teams, this shift turns automation into a trust-building advantage that strengthens relationships across carriers, brokers, and policyholders.
Regulation meets reality
AI is already embedded in everyday insurance workflows, from claim triage to risk scoring and pricing. But oversight requirements are tightening:
- The NAIC created a task force focused on AI model governance.
- New York DFS Circular Letter No. 7 (2024) calls for robust oversight of AI used in pricing and underwriting.
- Illinois requires insurers to test predictive models for bias and errors. (IDOI, 2024)
For claims and underwriting leaders, the message is clear: AI governance is no longer optional. Poor oversight can jeopardize compliance, erode trust, and expose organizations to reputational risk.
Why AI auditing matters
Auditing AI means validating that models do what they claim and do so fairly. This requires more than a performance snapshot; it demands ongoing, structured evaluation to ensure the model behaves predictably across different populations, time periods, and business scenarios.
Leading practices include explainable AI (XAI) tools to clarify how models reach decisions; governance documentation to record data sources, updates, and performance metrics; and back-testing and fairness checks to detect bias or drift.
Back-testing evaluates how a model would have performed on historical underwriting or claims data. For example, comparing predicted claim severity against past closed-claim outcomes or testing risk scores against prior loss experience.
Fairness checks ensure the model produces equitable results across groups by examining whether outcomes like claim routing, pricing tiers, or approval rates differ in ways not supported by legitimate risk factors. Together, these practices help detect bias, drift, and unintended impacts before they affect customers.
For claims and underwriting operations, this approach delivers three strategic benefits:
- Transparency as a differentiator: Demonstrating fairness builds trust with regulators, partners, and customers.
- Operational confidence: Continuous auditing ensures models stay aligned with business and regulatory expectations.
- Reputation protection: Continuous monitoring signals proactive, responsible innovation.
A practical roadmap for claims and underwriting teams
AI auditing doesn’t need to be overwhelming. The key is to build a structured, repeatable framework that fits naturally into existing underwriting and claims operations. Governance must be embedded into existing workflows, so every automated decision can be traced, tested, and trusted.
- Start with clean data. Ensure claims, pricing, and underwriting data is accurate, complete, and consistently formatted, so AI models operate on reliable inputs. Validate source systems, resolve missing or inconsistent fields, and document data lineage before models are evaluated or deployed.
- Map your models. Identify every AI or ML system affecting claims, pricing, or underwriting outcomes.
- Establish governance roles. Assign human ownership for model oversight, review, and escalation.
- Automate auditing. Use AI-enabled tools (IBM OpenScale, Fiddler) that monitor fairness, drift, and accuracy.
- Communicate trust. Share transparency reports with carrier partners and regulators. Use audit insights as proof of quality and responsibility.
- Review regularly. Audit models quarterly and evolve governance as regulations and business needs change.
Beyond compliance: The trust dividend
Insurance has always run on trust. For claims and underwriting professionals, AI auditing isn’t just about passing regulatory checks but about preserving credibility. Transparent, explainable AI reinforces confidence among regulators, policyholders, and business partners alike.
Embedding audits and governance into everyday operations turns compliance into a competitive advantage. Partnering with technology providers like Vertafore, who prioritize ethical, explainable AI, helps ensure that automation strengthens rather than replaces human judgement.
The question isn’t whether your AI is compliant. It’s whether your stakeholders can trust the outcomes it delivers.
Vertafore’s role in building confidence
Vertafore helps insurance carriers and MGAs move beyond compliance toward trustworthy AI. Our solutions are built with governance, reliability, and explainability at their core. Vertafore is committed to helping the industry operationalize responsible AI, so every automated decision can be explained, defended, and trusted.