Using SHAP values to explain actuarial models is a powerful way to bring transparency and trust to complex predictive models that are often viewed as black boxes. SHAP, which stands for SHapley Additive exPlanations, breaks down a model’s prediction into the contribution of each feature, making it easier to understand how individual variables affect outcomes. This is especially valuable in actuarial science, where decisions impact financial risk assessments, insurance pricing, and regulatory compliance.