SHAP (SHapley Additive exPlanations) is a model agnostic AI explainability framework that can be used for global and local explainability. Starting from scratch, the theory of SHAP values will be explained and the usage of the Python framework will be illustrated on a classification example in the transaction monitoring domain. After the presentation, you will have learned how to use SHAP to investigate feature importance, feature sensitivity and how to explain individual prediction in a human readable output.