Data Analytics for Non-Life Insurance Pricing

Mario V. Wuthrich and Christoph Buser
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Mario V. Wuthrich: RiskLab, ETH Zurich and Swiss Finance Institute

No 16-68, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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.

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 (search for similar items in EconPapers)
JEL-codes: G22 G28 (search for similar items in EconPapers)
Pages: 142 pages
Date: 2016-11
New Economics Papers: this item is included in nep-cmp and nep-ias
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp1668

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