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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.

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.

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.

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.

This paper develops and examines a framework for reserve distribution testing and validation and demonstrates its use with real datasets within an enterprise risk management framework.

This paper reviews the Hayne MLE modeling framework using a standard notation and covers a number of practical data issues and addresses the diagnostic testing of the model assumptions.

The fourth volume of the new CAS Monograph series, written by Mark Shapland, is now available.

Mark Shapland writes about his experience representing the Casualty Actuarial Society at a conference given by the Actuarial Society of Kazakhstan.

Incorporating all-prior data into analysis can improve tail factor estimates.