Building and validating machine learning models is a critical skill for actuaries preparing for the SOA Exam C and CAS Exam 4C. These exams focus on constructing and evaluating actuarial models, which increasingly incorporate modern statistical and machine learning techniques. If you’re gearing up for these exams, understanding a clear, step-by-step approach to model building can make a big difference, not just in passing the test but in applying these skills practically in your actuarial career.

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.