AIG 2011 Annual Report Download - page 110

Expected loss ratio methods rely on the application of an expected loss ratio to the earned premium for the
class of business to determine the loss reserves. For example, an expected loss ratio of 70 percent applied to an
earned premium base of $10 million for a class of business would generate an ultimate loss estimate of $7 million.
Subtracting any reported paid losses and loss expense would result in the indicated loss reserve for this class.
‘‘Bornhuetter Ferguson’’ methods are expected loss ratio methods for which the expected loss ratio is applied only
to the expected unreported portion of the losses. For example, for a long-tail class of business for which only
10 percent of the losses are expected to be reported at the end of the accident year, the expected loss ratio would
be applied to the 90 percent of the losses still unreported. The actual reported losses at the end of the accident
year would be added to determine the total ultimate loss estimate for the accident year. Subtracting the reported
paid losses and loss expenses would result in the indicated loss reserve. In the example above, the expected loss
ratio of 70 percent would be multiplied by 90 percent. The result of 63 percent would be applied to the earned
premium of $10 million resulting in an estimated unreported loss of $6.3 million. Actual reported losses would be
added to arrive at the total ultimate losses. If the reported losses were $1 million, the ultimate loss estimate under
the ‘‘Bornhuetter Ferguson’’ method would be $7.3 million versus the $7 million amount under the expected loss
ratio method described above. Thus, the ‘‘Bornhuetter Ferguson’’ method gives partial credibility to the actual loss
experience to date for the class of business. Loss development methods generally give full credibility to the
reported loss experience to date. In the example above, loss development methods would typically indicate an
ultimate loss estimate of $10 million, as the reported losses of $1 million would be estimated to reflect only
10 percent of the ultimate losses.
A key advantage of loss development methods is that they respond quickly to any actual changes in loss costs
for the class of business. Therefore, if loss experience is unexpectedly deteriorating or improving, the loss
development method gives full credibility to the changing experience. Expected loss ratio methods would be slower
to respond to the change, as they would continue to give more weight to the expected loss ratio, until enough
evidence emerged for the expected loss ratio to be modified to reflect the changing loss experience. On the other
hand, loss development methods have the disadvantage of overreacting to changes in reported losses if in fact the
loss experience is not credible. For example, the presence or absence of large losses at the early stages of loss
development could cause the loss development method to overreact to the favorable or unfavorable experience by
assuming it will continue at later stages of development. In these instances, expected loss ratio methods such as
‘‘Bornhuetter Ferguson’’ have the advantage of recognizing large losses without extrapolating unusual large loss
activity onto the unreported portion of the losses for the accident year. AIG’s loss reserve reviews for long-tail
classes typically utilize a combination of both loss development and expected loss ratio methods. Loss
development methods are generally given more weight for accident years and classes of business where the loss
experience is highly credible. Expected loss ratio methods are given more weight where the reported loss
experience is less credible, or is driven more by large losses. Expected loss ratio methods require sufficient
information to determine the appropriate expected loss ratio. This information generally includes the actual loss
ratios for prior accident years, and rate changes as well as underwriting or other changes which would affect the
loss ratio. Further, an estimate of the loss cost trend or loss ratio trend is required in order to allow for the effect
of inflation and other factors which may increase or otherwise change the loss costs from one accident year to the
next.
Frequency/severity methods generally rely on the determination of an ultimate number of claims and an average
severity for each claim for each accident year. Multiplying the estimated ultimate number of claims for each
accident year by the expected average severity of each claim produces the estimated ultimate loss for the accident
year. Frequency/severity methods generally require a sufficient volume of claims in order for the average severity
to be predictable. Average severity for subsequent accident years is generally determined by applying an estimated
annual loss cost trend to the estimated average claim severity from prior accident years. Frequency/severity
methods have the advantage that ultimate claim counts can generally be estimated more quickly and accurately
than can ultimate losses. Thus, if the average claim severity can be accurately estimated, these methods can more
quickly respond to changes in loss experience than other methods. However, for average severity to be predictable,
the class of business must consist of homogeneous types of claims for which loss severity trends from one year to
the next are reasonably consistent. Generally these methods work best for high frequency, low severity classes of
business such as personal auto. AIG also utilizes these methods in pricing subclasses of professional liability.
96 AIG 2011 Form 10-K