Tag Archives: Mark Shapland

A quantum leap in benchmarking P&C unpaid claims

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.

Milliman launches innovative benchmarking tool for assessing the variability in unpaid claim estimates

Milliman has announced the launch of its latest InsurTech offering, an innovative casualty benchmarking tool that provides a new industry standard and a better, more efficient way of assessing variability in unpaid claim estimates. Milliman’s Claim Variability Guidelines™, debuting at the Casualty Loss Reserve Seminar in Philadelphia, are new industry benchmarks to help evaluate the quality of stochastic unpaid claim distributions used for enterprise risk management (ERM) and dynamic financial analysis (DFA), including correlations for aggregate distributions. The Guidelines also stochastically support deterministic ranges used for reserving.

Milliman’s Claim Variability Guidelines are like version 2.0 of the standard benchmarks that are currently used industry-wide—they’re a modernized, robust, and efficient set of tools that can help insurers better understand their unpaid claim reserves. Being able to gauge the quality of unpaid claim variability estimates is a key metric in any risk management strategy, and allows insurers to more accurately price their products.

Key features of the tool include automatically adapting results based on company size, as well as the flexibility to adjust for different development patterns, currencies, and variance assumptions. For more information, Mark will be presenting on benchmarking unpaid claim estimates, as well as integrating reserve variability into ERM, at the Casualty Loss Reserve Seminar this week in Philadelphia; or click here.

Milliman actuary develops CAS training courses

Milliman consultant Mark Shapland and Louise Francis, of Francis Analytics and Actuarial Data Mining, developed two online courses that the Casualty Actuarial Society (CAS) is now offering to its members: Introduction to Statistics & Simulation and Introduction to Modeling Statistics. The courses are an offshoot of the Limited Attendance Seminars on Reserve Variability that Mark and Louise periodically run for the CAS and are part of the CAS’s ongoing efforts to expand its professional education programs for members. For more information, visit the CAS website.

CAS awards Milliman actuary honorarium

Milliman’s Mark Shapland received an honorarium from the Casualty Actuarial Society’s Monograph Editorial Board (MEB) for his paper on stochastic reserving entitled “Using the ODP bootstrap model: A practitioner’s guide.”

Mark’s paper explores practical issues and solutions for dealing with the limitations of over-dispersed Poisson (ODP) bootstrapping models, including practical considerations for selecting the best assumptions and the best model for individual situations. The paper illustrates the diagnostic tools that an actuary needs to assess whether a model is working well.

The actuary and enterprise risk management: Integrating reserve variability

The first step in managing reserve risk is measuring that risk. Risk management is linked to risk monitoring, measurement, and reporting. The quality of measurement and reporting often determines to what extent monitoring is possible.

Routinely assessing reserve variability, as part of the regular reserve analysis process, can greatly benefit the risk management process. Integrating elements of reserve risk measurement within a continuously monitored enterprise risk management (ERM) framework can offer a number of advantages to your organization, including, but not limited to:

1. Ensuring that reserving assumptions are tracked and validated over time and that changes in those assumptions are justified relative to performance.

2. Formalizing the governance around the process (i.e., clear assignment of risk ownership and consistent, accurate, and auditable controlling of deterministic methods, stochastic models, and actuarial methodology, etc.).

3. Providing a framework that allows actuarial resources to assess the effectiveness of the distributions of possible outcomes resulting from the reserve variability analyses (e.g., approximately 10% of observations as of each valuation date should fall within the highest and lowest 5% of the distribution of possible outcomes).

4. Providing a framework that includes an early warning system that translates actual outcomes of paid and outstanding loss into likely reserve estimate changes prior to any analysis.

5. Enabling management to use key performance indicators (KPIs) to anticipate the results of future actuarial analyses and better understand and assess how prior assumptions have held up.

6. Providing a framework that allows both managers to efficiently allocate actuarial resources (e.g., assigning the most experienced resources to the most challenging segments) and actuarial resources to hypothesize whether deviations are the result of a mean estimation error, a variance estimation error, or a random error.

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Risk management frontiers: The quest for more reserve information

Insurance risk managers and other corporate decision makers are about to discover a new frontier of reserving information. This frontier marks the increasingly dynamic boundary between risk and reserves, and those who learn to master it will find new ways to improve upon yesterday’s results.

A recent article for Risk Management Magazine by Milliman consultant Mark Shapland takes the measure of the new frontier. Read the full article here.