SHAP for Actuaries: Explain any Model

25 Pages Posted: 21 Mar 2023

See all articles by Michael Mayer

Michael Mayer

Schweizerische Mobiliar Versicherungsgesellschaft

Daniel Meier

Swiss Reinsurance Company

Mario V. Wuthrich

RiskLab, ETH Zurich

Date Written: March 15, 2023

Abstract

This tutorial gives an overview of SHAP (SHapley Additive exPlanation), one of the most commonly used techniques for examining a black-box machine learning (ML) model. Besides providing the necessary game theoretic background, we show how typical SHAP analyses are performed and used to gain insights about the model. The methods are illustrated on a simulated insurance data set of car claim frequencies using different ML models and different SHAP algorithms.

Keywords: XAI, explainability, machine learning, SHAP, Shapley values, regression modeling, interaction, partial dependence plot, motor insurance, claims frequency

JEL Classification: G22, C45, C18, C52, C55, C71

Suggested Citation

Mayer, Michael and Meier, Daniel and Wuthrich, Mario V., SHAP for Actuaries: Explain any Model (March 15, 2023). Available at SSRN: https://ssrn.com/abstract=4389797 or http://dx.doi.org/10.2139/ssrn.4389797

Michael Mayer

Schweizerische Mobiliar Versicherungsgesellschaft ( email )

Daniel Meier

Swiss Reinsurance Company ( email )

Mythenquai 50/60
P.O. Box
CH-8022 Zurich
Switzerland

Mario V. Wuthrich (Contact Author)

RiskLab, ETH Zurich ( email )

Department of Mathematics
Ramistrasse 101
Zurich, 8092
Switzerland

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