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The Discriminating (Pricing) Actuary

Frees, Edward (Jed) W., Huang, Fei·2020
DOI 10.2139/ssrn.3592475
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Abstract

The insurance industry is built on risk classification, grouping insureds into homogeneous classes. Through actions such as underwriting, pricing and so forth, it differentiates, or discriminates, among insureds. Actuaries have responsibility for pricing insurance risk transfers and are intimately involved in other aspects of company actions and so have a keen interest in whether or not discrimination is appropriate from both company and societal viewpoints. This paper reviews social and economic principles that can be used to assess the appropriateness of insurance discrimination. Discrimination issues vary by the line of insurance business and by the country and legal jurisdiction. This paper examines social and economic principles from the vantage of a specific line of business and jurisdiction; these vantage points provide insights into principles. To sharpen understanding of the social and economic principles, this paper also describes discrimination considerations for prohibitions based on diagnosis of COVID-19, the pandemic that swept the globe in 2020. Insurance discrimination issues have been an important topic for the insurance industry for decades and is evolving in part due to insurers' extensive use of *Big Data*, that is, the increasing capacity and computational abilities of computers, availability of new and innovative sources of data, and advanced algorithms that can detect patterns in insurance activities that were previously unknown. On the one hand, the fundamental issues of insurance discrimination have not changed with Big Data; one can think of credit-based insurance scoring and price optimization as simply forerunners of this movement. On the other hand, issues regarding privacy and use of algorithmic proxies take on increased importance as insurers' extensive use of data and computational abilities evolve.

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  68. Mod1Fits <-AmtGamma1$fitted.values SumM1Fits <-sum(AmtGamma1$fitted.values), 10.1201/b12208-18 · DOI
  69. <- Mod2fits, AmtGamma2$fitted.values/sum(AmtGamma2$fitted.values) ) * SumM1Fits
  70. Mod3Fits <-( AmtGamma3$fitted.values/sum(AmtGamma3$fitted.values) ) * SumM1Fits, 10.1201/b12208-18 · DOI
  71. <- Mod4fits, AmtGamma4$fitted.values/sum(AmtGamma4$fitted.values) ) * SumM1Fits
  72. <- Mod5fits, AmtGamma5$fitted.values/sum(AmtGamma5$fitted.values) ) * SumM1Fits
  73. ClaimsData$Female, mean)[,2] temp3q<-summarize(Mod3Fits,ClaimsData$Female, mean)[,2] temp4q<-summarize(Mod4Fits,ClaimsData$Female, mean)[,2] temp5q<-summarize(Mod5Fits,ClaimsData$Female, mean)[,2] temp6q<-summarize(Mod6Fits, Mod6Fits <-( PSFitted/sum(PSFitted) ) * SumM1Fits library(Hmisc) temp1q<-summarize(Mod1Fits,ClaimsData$Female, mean)
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Authors
Frees, E. (. W., & Huang, F.
ORCID
Publisher
Elsevier BV
Published
2020
DOI
10.2139/ssrn.3592475
Cited by
2
References
1
Type
Preprint

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Frees, E. (. W., & Huang, F. (2020). The Discriminating (Pricing) Actuary. https://doi.org/10.2139/ssrn.3592475