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Some Novel Perspectives on Risk Classification

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  • R Guy Thomas

    (Institute of Mathematics, Statistics & Actuarial Science, University of Kent, Canterbury CT2 7NF, U.K.)

Abstract

This paper considers a number of novel perspectives on risk classification, primarily in the context of life and critical illness insurance. I suggest that the terminology of “adverse selection” is often misleading, because from a public policy viewpoint, adverse selection may not always be adverse. I suggest that public policymakers should consider the criterion of “loss coverage”, and that in many markets a socially optimal level of adverse selection is that which maximises loss coverage. A review of empirical studies suggests that adverse selection is often difficult to observe in practice; this leads to the concept of propitious selection, and various psychological perspectives on risk classification. I suggest that competition between insurers in risk classification can sometimes be characterised as a malevolent invisible hand, and that public policy should direct competition towards areas that are more clearly beneficial to all insurance customers. I also consider the perspectives of risk classification as blame, the conflict between risk classification and human rights, and the fallacy of the one-shot gambler. The Geneva Papers (2007) 32, 105–132. doi:10.1057/palgrave.gpp.2510118

Suggested Citation

  • R Guy Thomas, 2007. "Some Novel Perspectives on Risk Classification," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 32(1), pages 105-132, January.
  • Handle: RePEc:pal:gpprii:v:32:y:2007:i:1:p:105-132
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    Citations

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    Cited by:

    1. Nan Zhang & Heng Xu, 2024. "Fairness of Ratemaking for Catastrophe Insurance: Lessons from Machine Learning," Information Systems Research, INFORMS, vol. 35(2), pages 469-488, June.
    2. R. Guy Thomas, 2008. "Loss Coverage as a Public Policy Objective for Risk Classification Schemes," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 75(4), pages 997-1018, December.
    3. David A. Cather, 2020. "Reconsidering insurance discrimination and adverse selection in an era of data analytics," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 45(3), pages 426-456, July.
    4. David A. Cather, 2018. "Cream Skimming: Innovations in Insurance Risk Classification and Adverse Selection," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 21(2), pages 335-366, September.
    5. Levon Barseghyan & Francesca Molinari & Darcy Steeg Morris & Joshua C. Teitelbaum, 2020. "The Cost of Legal Restrictions on Experience Rating," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 17(1), pages 38-70, March.
    6. J. Francois Outreville, 2014. "Risk Aversion, Risk Behavior, and Demand for Insurance: A Survey," Journal of Insurance Issues, Western Risk and Insurance Association, vol. 37(2), pages 158-186.
    7. Georges Dionne & Casey Rothschild, 2014. "Economic Effects of Risk Classification Bans," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 39(2), pages 184-221, September.
    8. Vijay Aseervatham & Christoph Lex & Spindler, Martin, 2014. "How do unisex rating regulations affect gender differences in insurance premiums?," MEA discussion paper series 201416, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    9. Donatella Porrini, 2015. "Risk Classification Efficiency and the Insurance Market Regulation," Risks, MDPI, vol. 3(4), pages 1-10, September.
    10. Arthur Charpentier, 2022. "Quantifying fairness and discrimination in predictive models," Papers 2212.09868, arXiv.org.
    11. Sylvestre Frezal & Laurence Barry, 2020. "Fairness in Uncertainty: Some Limits and Misinterpretations of Actuarial Fairness," Journal of Business Ethics, Springer, vol. 167(1), pages 127-136, November.

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