Author
Listed:
- 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
Download full text from publisher
Citations
Citations are extracted by the
CitEc Project, subscribe to its
RSS feed for this item.
Cited by:
- Laurence Barry & Arthur Charpentier, 2022.
"The Fairness of Machine Learning in Insurance: New Rags for an Old Man?,"
Papers
2205.08112, arXiv.org.
- 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.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:astinb:v:14:y:1984:i:02:p:151-163_00. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/asb .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.