Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data

DOI10.1007/S13385-021-00270-5zbMath1480.91230OpenAlexW3138084929MaRDI QIDQ2066785

Arthur Maillart

Publication date: 14 January 2022

Published in: European Actuarial Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s13385-021-00270-5

zbMATH Keywords

interpretabilityrandom forestrisk classificationgradient boostingexplainabilityblack-box model explanationpay-as-you-drive insuranceusage-based insurance

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Actuarial mathematics (91G05)

Uses Software

• ElemStatLearn
• XGBoost
• shap
• BartPy

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Greedy function approximation: A gradient boosting machine.
• BART: Bayesian additive regression trees
• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Explanation in artificial intelligence: insights from the social sciences
• Non-Life Insurance Pricing with Generalized Linear Models
• Finite mixture models
• A data driven binning strategy for the construction of insurance tariff classes
• Boosting Insights in Insurance Tariff Plans with Tree-Based Machine Learning Methods
• Extremely randomized trees
• Random forests

This page was built for publication: Toward an explainable machine learning model for claim frequency: a use case in car insurance pricing with telematics data