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Data Analytics for Non-Life Insurance Pricing

Author

Listed:
  • Mario V. Wuthrich

    (RiskLab, ETH Zurich and Swiss Finance Institute)

  • Christoph Buser

Abstract

These notes aim at giving a broad skill set to the actuarial profession in non-life insurance pricing and data science. We start from the classical world of generalized linear models, generalized additive models and credibility theory. These methods form the basis of the deeper statistical understanding. We then present several machine learning techniques such as regression trees, bagging, random forest, boosting and support vector machines. Finally, we provide methodologies for analyzing telematic car driving data.

Suggested Citation

  • Mario V. Wuthrich & Christoph Buser, 2016. "Data Analytics for Non-Life Insurance Pricing," Swiss Finance Institute Research Paper Series 16-68, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp1668
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2870308
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    Citations

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    Cited by:

    1. Philippe Deprez & Pavel V. Shevchenko & Mario V. Wuthrich, 2017. "Machine Learning Techniques for Mortality Modeling," Papers 1705.03396, arXiv.org.

    More about this item

    Keywords

    non-life insurance pricing; car insurance pricing; generalized linear models; generalized additive models; credibility theory; neural networks; regression trees; CART; bootstrap; bagging; random forest; boosting; support vector machines; telematic data; data science; machine learning; data analytics;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation

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