THE ASK

How can my insurance business validate its AI models and maintain AI governance to meet regulatory requirements?

The non-life division of a bancassurer had begun deploying large language models (LLMs), AI systems trained to generate human-like responses. While this enhanced operational efficiencies, it also introduced risks: Sensitive customer data could be exposed, the AI outputs might become less accurate over time, and regulators were asking hard questions about model oversight. The company needed a rigorous framework for monitoring and governing its AI tools before those risks became costly problems.

THE SOLUTION

Milliman built an AI monitoring framework to protect customers and comply with regulators

Milliman began with a structured workshop to assess the client’s existing AI monitoring practices against industry best practices, identifying both strengths and potential weak points. From there, the team defined specific performance indicators to detect “model drift”—the tendency of AI systems to degrade in accuracy or reliability as real-world conditions change over time. The engagement also included targeted testing for adversarial vulnerabilities, meaning deliberate attempts to manipulate AI outputs. Finally, Milliman introduced IT monitoring tools to embed oversight into day-to-day operations.

THE OUTCOME

Stronger AI governance, faster risk detection, and a competitive edge on responsible AI use

Milliman’s framework gave the bancassurer clear, ongoing visibility into how its AI tools were performing—and early warning if and when something started to go wrong. Thanks to documented governance controls and transparent reporting, internal stakeholders and external regulators alike gained confidence in the systems. The organization also gained a meaningful reputational advantage: a demonstrable commitment to responsible AI adoption in a sector where that credibility is increasingly a differentiator.