Travelers 2015 Annual Report Download - page 137

Some components of product line reserves are susceptible to relatively infrequent large claims that
can materially impact the total estimate for that component. In such cases, the Company’s actuarial
analysis generally isolates and analyzes separately such large claims. The reserves excluding such large
claims are generally analyzed using the conventional methods described above. The reserves associated
with large claims are then analyzed utilizing various methods, such as:
• Estimating the number of large claims and their average values based on historical trends from
prior accident periods, adjusted for the current environment and supplemented with actual data
for the accident year analyzed to the extent available.
• Utilizing individual claim adjuster estimates of the large claims, combined with continual
monitoring of the aggregate accuracy of such claim adjuster estimates. (This monitoring may
lead to supplemental adjustments to the aggregate of such claim estimates.)
• Utilizing historic longer-term average ratios of large claims to small claims, and applying such
ratios to the estimated ultimate small claims from conventional analysis.
• Ground-up analysis of the underlying exposure (typically used for asbestos and environmental).
The results of such methodologies are subjected to various reasonability and diagnostic tests,
including implied incurred-loss-to-earned-premium ratios, non-zero claim severity trends and
paid-to-incurred loss ratios. An actual versus expected analysis is also performed comparing actual loss
development to expected development embedded within management’s best estimate. Additional
analyses may be performed based on the results of these diagnostics, including the investigation of
other actuarial methods.
The methods described above are generally utilized to evaluate management’s existing estimate for
prior accident periods. For the initial estimate of the current accident year, the available claim data is
typically insufficient to produce a reliable indication. Hence, the initial estimate for an accident year is
generally based on an exposure-based method using either expected losses or a loss ratio projection
method. The loss ratio method uses the earned premium for the current year multiplied by a projected
loss ratio. The projected loss ratio is determined through an analysis of prior periods’ experience, using
loss trend, rate level differences, mix of business changes and other known or observed factors
influencing the current accident year relative to prior accident years. The exact number of prior
accident years utilized varies by product line component, based on the stability and consistency of the
individual accident year estimates.
Management’s estimates
At least once per quarter, certain members of Company management meet with the Company’s
actuaries to review the latest claims and claim adjustment expense reserve analyses. Based on these
analyses, management determines whether its ultimate claim liability estimates should be changed. In
doing so, it must evaluate whether the new data provided represents credible actionable information or
an anomaly that will have no effect on estimated ultimate claim liability. For example, as described
above, payments may have decreased in one geographic region due to fewer claim adjusters being
available to process claims. The resulting claim payment patterns would be analyzed to determine
whether or not the change in payment pattern represents a change in ultimate claim liability.
Such an assessment requires considerable judgment. It is frequently not possible to determine
whether a change in the data is an anomaly until sometime after the event. Even if a change is
determined to be permanent, it is not always possible to reliably determine the extent of the change
until sometime later. The overall detailed analyses supporting such an effort can take several months to
perform as the underlying causes of the trends observed need to be evaluated, which may require the
gathering or assembling of data not previously available. It may also include interviews with experts
involved with the underlying processes. As a result, there can be a time lag between the emergence of a
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