Construction of rating systems using global sensitivity analysis: A numerical investigation

Arianna Vallarino, Giovanni Rabitti*, Amir Khorrami Chokami

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

The ratemaking process is a key issue in insurance pricing. It consists in pooling together policyholders with similar risk profiles into rating classes and assigning the same premium for policyholders in the same class. In actuarial practice, rating systems are typically not based on all risk factors but rather only some of factors are selected to construct the rating classes. The objective of this study is to investigate the selection of risk factors in order to construct rating classes that exhibit maximum internal homogeneity. For this selection, we adopt the Shapley effects from global sensitivity analysis. While these sensitivity indices are used for model interpretability, we apply them to construct rating classes. We provide a new strategy to estimate them, and we connect them to the intra-class variability and heterogeneity of the rating classes. To verify the appropriateness of our procedure, we introduce a measure of heterogeneity specifically designed to compare rating systems with a different number of classes. Using a well-known car insurance dataset, we show that the rating system constructed with the Shapley effects is the one minimizing this heterogeneity measure.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalASTIN Bulletin
Early online date19 Oct 2023
DOIs
Publication statusE-pub ahead of print - 19 Oct 2023

Keywords

  • heterogeneity measures
  • model interpretability
  • Quantile regression
  • Shapley effects

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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