Abstract
In this chapter, we introduce basic insurance concepts used throughout this book. We make crucial distinctions between
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technical price and commercial premiums,
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risk factors and rating factors,
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prediction and ratemaking,
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a priori and a posteriori risk classification.
Topics treated in this book are carefully explained, referring to the appropriate literature for related problems not covered here. The nature of data available to perform insurance studies is also discussed, stressing the inherent limitations in the interpretation of conclusions drawn from observational studies.
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Notes
- 1.
The term “factor” should not be confused with its statistical meaning for categorical features: an insurance risk factor may well be continuous, such as age or power of the car.
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Notice that enhanced annuities sold in the United States and United Kingdom offer higher benefits in case of impaired health status.
- 3.
Technical pricing now tends to become even finer, with premiums sometimes varying by street or district within each postcode.
References
Denuit M, Dhaene J, Goovaerts MJ, Kaas R (2005) Actuarial theory for dependent risks: measures, orders and models. Wiley, New York
Hainaut D, Trufin J, Denuit M (2019) Effective statistical learning methods for actuaries—neural networks and unsupervised methods. Springer Actuarial Series
Kaas R, Goovaerts MJ, Dhaene J, Denuit M (2008) Modern actuarial risk theory using R. Springer, New York
Meyers G, Cummings AD (2009) “Goodness of Fit” vs. “Goodness of Lift”. Actuarial Rev 36:16–17
Pechon F, Denuit M, Trufin J (2019) Preliminary selection of risk factors in P&C ratemaking. Variance, in press
Trufin J, Denuit M, Hainaut D (2019) Effective statistical learning methods for actuaries—tree-based methods. Springer Actuarial Series
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Denuit, M., Hainaut, D., Trufin, J. (2019). Insurance Risk Classification. In: Effective Statistical Learning Methods for Actuaries I. Springer Actuarial(). Springer, Cham. https://doi.org/10.1007/978-3-030-25820-7_1
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DOI: https://doi.org/10.1007/978-3-030-25820-7_1
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