Autocalibration by balance correction in nonlife insurance pricing
(2022) , 17 pages
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- Abstract
- By exploiting massive amounts of data, machine learning techniques provide actuaries with predictors exhibiting high correlation with claim frequencies and severities. However, these predictors generally fail to achieve financial equilibrium and thus do not qualify as pure premiums. Autocalibration effectively addresses this issue since it ensures that every group of policyholders paying the same premium is on average self-financing, as demonstrated by Denuit et al. (2021), Ciatto et al. (2022), Lindholm et al. (2022) and Wüthrich (2022). These authors proposed balance correction as a way to make any candidate premium autocalibrated. The present paper further studies the effect of balance correction on resulting pure premiums. It is shown that this method is also beneficial in terms of out-of-sample, or predictive Tweedie deviance, Bregman divergence as well as concentration curves. The paper then derives conditions ensuring that the initial predictor and its balance-corrected version are ordered in Lorenz order. Finally, criteria are proposed to rank the balance-corrected versions of two competing predictors in the convex order.
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Citations
Denuit, M., & Trufin, J. (2022). Autocalibration by balance correction in nonlife insurance pricing (LIDAM Discussion Paper ISBA 2022/41). https://hdl.handle.net/2078.5/106349