Machine learning models have become increasingly popular in actuarial science, helping actuaries make better predictions for insurance claims, pricing, and risk assessment. But one common challenge many actuaries face—especially those without a deep technical background—is understanding how these complex models arrive at their predictions. This is where SHAP, or SHapley Additive exPlanations, comes into play. SHAP offers a clear and mathematically sound way to interpret machine learning models by breaking down their predictions into understandable pieces. If you’ve ever struggled to explain a model’s output to colleagues or stakeholders, this step-by-step guide is for you.