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Rate Making and Society's Sense of Fairness

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  • de Wit, G. W.
  • van Eeghen, J.

Abstract

Actuaries have always been in search of ways to determine premiums which match the risks insured as closely as possible. They do this by differentiating between them on the basis of observable risk factors. In practice, many examples of such risk factors are being used: age and sex for life insurance; location, type of building etc. for fire insurance. Motor insurance is perhaps the most characteristic branch with respect to this phenomenon: in tariffs we find factors like weight, price or cylinder capacity of the car, age of the driver, area of residence, past claims experience (Bonus/Malus), annual mileage etc.Outsiders may not always be very positive about such a refined premium differentiation. The basis of insurance, they say, should be solidarity among insureds; premium differentiation is basically opposed to this. Another statement heard in the field is: “Premium differentiation ultimately results in letting every individual pay his own claims, it is the end of insurance†.Much confusion arises during discussions about this subject, especially between actuaries and non-actuaries. We will therefore first give a mathematical definition of solidarity, (Section 2), followed by a brief description of certain trends in society which might bring insurers to deliberately drop certain risk factors from their tariffs in order to increase solidarity (Section 3). The consequences of doing so are examined and it is shown that increased solvency requirements will in the end prove to be ineffective. A possible solution is a voluntary transfer of premium between companies (Section 4). The situation is illustrated by an example of health insurance in the Netherlands, where proposals to arrive at such transfeis are presently being discussed.

Suggested Citation

  • de Wit, G. W. & van Eeghen, J., 1984. "Rate Making and Society's Sense of Fairness," ASTIN Bulletin, Cambridge University Press, vol. 14(2), pages 151-163, October.
  • Handle: RePEc:cup:astinb:v:14:y:1984:i:02:p:151-163_00
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    Cited by:

    1. Laurence Barry & Arthur Charpentier, 2022. "The Fairness of Machine Learning in Insurance: New Rags for an Old Man?," Papers 2205.08112, arXiv.org.
    2. 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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