Because no single method is necessarily better in all cases, the actuary should consider the use of more than one method to assess the reasonableness of results. The actuary should evaluate the method(s) chosen and the results obtained in light of the purpose, constraints, and scope of the assignment. The actuary should consider the reasonableness of the assumptions underlying each method used, and should consider the sensitivity of the incurred claim estimates to the use of reasonable alternative assumptions. The actuary should also consider the effect of trends both in previous periods and the current period for estimating incurred claims. The actuary should choose the outcome that, in the actuary’s professional judgment, is the most reasonable provision for incurred claims, whether from a single method or a combination of several methods. Sections 3.4.1–3.4.3 below discuss some of the more common methods for estimating incurred claims.
3.4.1 Development Method
This method is appropriate and widely used for shortterm benefits with claims subject to processing and payment (i.e. not capitation) and may also be appropriate for claims associated with long-term products. The actuary should consider using metrics to assess the reasonableness of results for periods where historical development patterns are less credible. For example, the actuary might evaluate the ratio of estimated incurred claims to earned premiums or exposure units for reasonableness.3.4.2 Projection Methods
Projection methods may be used to estimate incurred claims when the incidence of claims or volume of available data is limited or not sufficiently credible for other estimation methods, to supplement the development method for the most recent incurral months, or as a reasonableness check for other estimation methods. This method starts with the development of a historical claim metric (for example, cost per claim, cost per member per month, loss ratio) and then multiplies this value times the appropriate base for the period being estimated (for example, claim volume, member exposure units, earned premium, respectively.) The actuary may adjust the historical claim metric when appropriate, for example as a result of trend. The actuary may use utilization metrics (for example, authorized days per thousand members) to improve the projected cost levels for recent months, and to adjust for the impact of catastrophic claims. The actuary may also consider using risk adjustment techniques or other indicators such as pharmacy claims to help project shifts in the morbidity of the block.3.4.3 Tabular Method
The tabular method is generally used for long-term products for which a reported claim event triggers an expected series of payments. This method applies factors to items such as individual claims, waived rates, or other volume measures based on previous experience in order to estimate the unpaid claims liability for known claims. The factors are based on items such as the age and gender of the insured, elimination period, cause of claim, length of disablement on the valuation date, and remaining benefit period, as appropriate to the coverage.When using the tabular method, the actuary should take into account specified benefit changes throughout the lifetime of the claim and the assumptions used to develop the factors, and should select the appropriate factors to estimate the unpaid claims liability given the risk characteristics of the policy.
The actuary should recognize the specific impacts that recovery, mortality, and government offsets may have on tabular factors.
The tabular method is not appropriate by itself for estimating unreported claims.
When the tabular method is used, the actuary should consider whether an additional adjustment is necessary to reflect unreported incurred claims.
Greater availability of data and advances in computing power have resulted in alternative approaches that the actuary may consider to estimate incurred claims. These include (but are not necessarily limited to) regression, time series, and other statistical and econometric models, as well as different approaches to categorizing and aggregating data (for example, summarizing by weekly data cells or estimating the cost of reported claims separately from incurred but not reported claims.)
