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In this paper, we present a stochastic loss development approach that models all the core components of the claims process separately. The benefits of doing so are discussed, including the providing of more accurate results by increasing the data available to analyze. This also allows for finer segmentations, which is very helpful for pricing and profitability analysis.
Current approaches to measuring uncertainty in an unpaid claim estimate often focus on parameter risk and process risk but do not account for model risk. This paper introduces simulation-based approaches to incorporating model error into an actuary’s estimate of uncertainty. The first approach, called Weighted Sampling, aims to incorporate model error into the uncertainty of a single prediction.
Current approaches to measuring uncertainty in an unpaid claim estimate often focus on parameter risk and process risk but do not account for model risk. This paper introduces simulation-based approaches to incorporating model error into an actuary’s estimate of uncertainty. The first approach, called Weighted Sampling, aims to incorporate model error into the uncertainty of a single prediction.
PEBELS is a method for estimating the expected loss cost for each loss layer of an individual property risk regardless of size. By providing maximum resolution in estimating layer loss costs, PEBELS facilitates increased accuracy and sophistication in many actuarial pricing applications such as ratemaking, predictive modeling, catastrophe modeling, and reinsurance pricing.
When building statistical models to help estimate future results, actuaries need to be aware that not only is there uncertainty inherent in random events (process risk), there is also uncertainty inherent in using a finite sample to parameterize the models (parameter risk).
