Peeking into the Black Box: An Actuarial Case Study for Interpretable Machine Learning

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See all articles by Christian Lorentzen

Christian Lorentzen

Schweizerische Mobiliar Versicherungsgesellschaft

Michael Mayer

Schweizerische Mobiliar Versicherungsgesellschaft; formerly at Consult AG Statistical Services

Date Written: May 7, 2020

Abstract

This tutorial gives an overview of tools for explaining and interpreting black
box machine learning models like boosted trees or deep neural networks. All
our methods are illustrated on a publicly available real car insurance data set
on claims frequencies.

Keywords: XAI, machine learning, explainability, interpretability, black box models, model-agnostic technique, flashlight, motor insurance, claims frequency

JEL Classification: G22, C52

Suggested Citation

Lorentzen, Christian and Mayer, Michael, Peeking into the Black Box: An Actuarial Case Study for Interpretable Machine Learning (May 7, 2020). Available at SSRN: https://ssrn.com/abstract=

Christian Lorentzen (Contact Author)

Schweizerische Mobiliar Versicherungsgesellschaft ( email )

Michael Mayer

Schweizerische Mobiliar Versicherungsgesellschaft ( email )

formerly at Consult AG Statistical Services ( email )

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