Data Analytics for Non-Life Insurance Pricing

This paper explores the application of spatial models to non-life insurance data focused on the multi-risk home insurance branch. In the pricing modelling and rating process, spatial information should be considered by actuaries and insurance managers because frequencies and claim sizes may vary by region and the premium should be different considering this rating variable. In addition, it is relevant to examine the spatial dependence due to the fact that the frequency of claims in neighbouring regions is often expected to be more closely related than those in regions far from each other. In this paper, a comparison between spatial models, such as spatial autoregressive models (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM), and a non-spatial model has been developed. The data used for this analysis are for a home insurance portfolio located in Spain, from which we have selected peril of water coverage.

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Concordance Probability for Insurance Pricing Models

Risks ◽

10.3390/risks9100178 ◽

2021 ◽

Vol 9 (10) ◽

pp. 178

Author(s):

Jolien Ponnet ◽

Robin Van Oirbeek ◽

Tim Verdonck

Keyword(s):

Life Insurance ◽

Real Life ◽

Accurate Estimation ◽

Large Sample Size ◽

Pricing Models ◽

Frequency Model ◽

Discriminatory Ability ◽

Insurance Pricing ◽

Concordance Probability ◽

Definition Of

The concordance probability, also called the C-index, is a popular measure to capture the discriminatory ability of a predictive model. In this article, the definition of this measure is adapted to the specific needs of the frequency and severity model, typically used during the technical pricing of a non-life insurance product. For the frequency model, the need of two different groups is tackled by defining three new types of the concordance probability. Secondly, these adapted definitions deal with the concept of exposure, which is the duration of a policy or insurance contract. Frequency data typically have a large sample size and therefore we present two fast and accurate estimation procedures for big data. Their good performance is illustrated on two real-life datasets. Upon these examples, we also estimate the concordance probability developed for severity models.

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Insurance pricing discrimination and Aristotelian equality: an application to annuity pricing

Journal of Insurance Regulation ◽

10.52227/25094.2021 ◽

2021 ◽

Author(s):

David A. Cather

Keyword(s):

Social Justice ◽

Mortality Rate ◽

Medical History ◽

Data Analytics ◽

Black Americans ◽

White Americans ◽

International Courts ◽

Insurance Pricing ◽

Use Of Data ◽

The Social

International courts often apply the social justice standard of Aristotelian equality—treating like people alike and unlike people differently—to cases involving insurance pricing discrimination. This article examines whether the use of insurance pricing variables like gender and race results in discriminatory pricing categories consisting of heterogeneous policyowners, in violation of Aristotelian equality. This article applies this discrimination standard to the pricing of annuities, drawing from studies investigating the racial mortality crossover, findings that show that the mortality rate of Black Americans falls below the rate of White Americans at advanced ages. Based on the crossover literature, this study demonstrates how race-based annuity pricing would be discriminatory because it results in heterogeneous pricing within racial pricing categories, but that insurers can control for this heterogeneity by using the wider variety of annuity pricing data (e.g., medical history, diseases, and smoking) developed in the enhanced annuity submarket. The article demonstrates how the increased use of data analytics in insurance pricing to control for heterogeneity is consistent with Aristotelian equality.

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