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Abstract:
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Insurance claims have deductibles, which must be considered when pricing for insurance premium. The deductible may cause censoring and truncation to the insurance claims. In this talk, an overview of deductible ratemaking will be provided, and the pros and cons of two deductible ratemaking approaches will be compared; the regression approach, and the maximum likelihood approach. The regression approach turns out to have an advantage in predicting aggregate claims, while the maximum likelihood approach has an advantage when calculating theoretically correct relativities for deductible levels beyond those observed by empirical data. A comparison of selected models show that the usage of long-tail severity distributions may improve the deductible rating, while the 01-inflated frequency model may have limited advantages due to estimation issues under censoring and truncation. For demonstration, loss models fit to the Wisconsin Local Government Property Insurance Fund (LGPIF) data will be illustrated, and examples will be provided for the ratemaking of per-loss deductibles offered by the fund.
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