Insights
Most actuarial machine-learning projects fail because the operating model is weak, not because the model is. The convergence of actuarial control and MLOps is the blueprint for production-ready actuarial AI.
Removing the protected variable from the model does not make the model fair. That single technical fact has done more to reshape insurance fairness work in the last three years than any regulatory pronouncement — because it forces a harder question about when risk classification turns into unfair discrimination.
As actuarial teams use generative AI for documentation, code, anomaly detection and assumption setting, the validation playbook needs explicit extensions. Four extensions and a structured way to apply them.
