Under Solvency II, reserving risk takes on a different meaning, based on the change in the estimated ultimate loss over a one-year time horizon, which accounts for the payments during the one-year time horizon and the consequences for future payments (i.e., the change in reserves) after the one-year time horizon. A number of models—most notably those developed by Mack in 1993 and later refined by Merz and Wüthrich—have provided insurers well thought out and documented approaches for determining reserve variability and estimating unpaid claims on an ultimate time horizon and one-year time horizon, respectively. This article by Milliman consultant Mark Shapland offers perspective.
This article was originally published in The European Actuary, November 2019.
Since the advent of Solvency II, insurers are faced with a number of challenges that can have a potential impact on determining the economic value of their liabilities. These challenges start with an insurer’s modeled uncertainty with respect to the timing and amount of future cash flows, which sets the stage for nearly every other element of the risk margin. Milliman actuary Mark Shapland offers some perspective in this paper.
Traditional development pattern benchmarks have provided some support in estimating fundamental liabilities, but even here, the process has long been a one-dimensional exercise, at least until now. A recently developed benchmarking tool, which includes percentiles at all stages of development, allows for the calibration of a benchmark that better resembles your portfolio. As such, this rigorously back-tested tool can provide actuaries an added level of confidence in the reasonableness of an entity’s reserve ranges. The next generation benchmarking tool, known as claim variability guidelines, is derived from extensive testing that involved all long-tail Schedule P lines of business and more than 30,000 data triangle sets. Milliman’s Mark Shapland provides perspective in this article.
Much like the decisions about a central estimate, quantifying the uncertainty (i.e., determining a loss distribution) is prone to many of the same vulnerabilities of subjectivity and method/model error. The introduction of the claims variability guidelines is part of an evolutionary process that began with deterministic and statistical models aimed at understanding an insurance entity’s risk. The advent of substantial computing power allowed actuaries to move closer to a reasonable depiction of an entity’s risk with the development of sophisticated models that simulate millions of possible outcomes. From there, distributions of the possible outcomes can be used to identify a central estimate and to quantify worst case scenarios. Milliman’s Mark Shapland offers some perspective in this article.
Confined by limited data, the aggregation process is typically riddled with volatility that can skew the view of an entity’s risk and capital needs. What has long been missing, at least until now, is a reliable benchmark for identifying and quantifying the risk dependencies between segments that underlie the loss aggregation process. Understanding risk dependencies between segments is a fundamental part of the process in forming conclusions about the interaction of loss distributions. With the introduction of new claims variability guidelines, actuaries can gauge the reasonableness of their correlations against benchmark correlations. Milliman’s Mark Shapland offers some perspective in this article.
The ability to benchmark an entity’s results against others in the industry and the industry as a whole can provide significant insights into both actuaries’ daily work and their strategic planning. Using the most advanced benchmarks available can help to ensure a more efficient integration of reserve variability analysis into enterprise risk management processes and enhance an entity’s strategies. Milliman consultant Mark Shapland offers some perspective in this article.
The article was originally published in the March/April 2018 issue of Contingencies.