1999 CLRS September 1999 Scottsdale, Arizona Basic Track II 1999 CLRS September 1999 Scottsdale, Arizona

Introduction Topics Covered Comparison of Results from Paid and Incurred LDMs Reasonableness Checks Historical Ratios Ultimate Levels Current Year Sensitivity Analysis Rate Level Adequacy Claim Frequency & Severity Claim Closure Rate Adequacy of Case Reserves Importance of Tail Factors

Comparison of LDM Projections

Comparison of Loss Development Methods Underlying Assumptions PLDM: No changes in the payment pattern ILDM: No changes in case reserve adequacy PLDM: “Hard” data; no estimates involved ILDM: Uses all the available information Pro PLDM: May generate large, volatile loss development factors & take longer to develop to ultimate ILDM: Uses case reserves, which are estimates, to develop estimates of ultimate losses Con

Key Assumptions & Potential Problems Sample Problems Claims settlement patterns unchanging Case reserving practices & philosophies unchanging No claim processing changes Policy limits have no impact on loss development Loss development unaffected by changing loss cost trends No change in mix of business No cyclical loss development No data anomalies Increasing delays in claim closing rates Conscious effort to improve case reserve adequacy; Introduction of new case reserving procedures Change in data processing; Revised claim payment recording Increasing frequency of full policy limits claims; Changing policy limits Surges in inflation; Increased litigation; Diminished policy defenses Changes in reinsurance coverages; Increased long-tail exposures; Introduction of new or revised coverages Claims settlement or reserving impacted by business underwriting cycles Catastrophic or unusual losses reflected in loss experience; Unusual claim settlement/reporting delays

Comparison of Estimated Reserves

Formulas to Derive IBNR Reserves Once an estimate of ultimate loss has been obtained, the arithmetic of IBNR is simple. Ultimate Losses Minus Paid Losses Case Reserves Ultimate Losses Minus Reported Losses Unpaid Losses Minus Case Reserves

Other Reserving Methods Tested Discussed in subsequent CLRS sessions Expected Loss Technique Bornhuetter-Ferguson Method Severity/Frequency Method Many, many others Note that development method may also be applied to claim counts.

Development Method on Claim Counts

Reported Counts DM: RTR Factors Selected

Reported Counts DM: Projection

Reasonableness Ultimate losses should be measured for reasonableness against relevant indicators: premium loss ratios exposures or number of policies frequency, pure premium claim counts severity Assumptions & methods should be documented and subjected to sensitivity analysis.

Reasonableness Checks: Ultimate Loss Ratios

Reasonableness Checks: Frequency & Severity

Reasonableness Checks: Pure Premium

Sensitivity Analysis: Current Year Analysis Improvements in results may stem from: Higher rates Lower claim frequency Lower claim severity Better results would appear to be present if: Claims were being processed or paid more slowly Case reserves were less adequate Mix of business is different

Sensitivity Analysis: Ratios Review historical relationships Losses Reported losses to paid Claim counts Settlement rate Ratio of claims closed with no payment to total closed claims Losses and Claim Counts Severities or average values

Sensitivity Analysis: Ratios - Examples

Sensitivity Analysis: Rate Level Adequacy Increases in average premium are primarily due to: Changes in the mix of business. Rate increases. If the changes in average premium in the latest two years are due to rate increases, then that would explain much of the improvement in loss ratios. If the changes are due to shifts in the mix of business, then the improvement in the loss ratios may or may not be real. Further investigation would be needed to understand what the shift was and whether the different business types have varying loss development characteristics.

Sensitivity Analysis: Claim Severity There is no consistent pattern in severity, except that it has generally increased over the years. This is typical, as we expect severity to increase due to inflation. The very small increase in severity that is forecast for the current year is unusual. In the same year, claim frequency has increased. Perhaps there is an increase in the number of small dollar claims? This would be a good question to ask the Claim Department.

Sensitivity Analysis: Claim Closure Rate In the past few years, claims have been closing more rapidly. This would imply that claims are being paid more rapidly and that the paid loss development factor is probably too high. One of the major assumptions of the PLDM (consistent payment patterns) has been violated.

Sensitivity Analysis: Case Reserve Adequacy

Sensitivity Analysis: Case Reserve Adequacy In general, we expect increasing numbers: 1. Across the rows because smaller claims settle more quickly; and 2. Down the columns due to inflation. It is important to understand the company’s case reserving philosophy and procedures to be able to interpret trends in the data. Many changes in case reserve procedures can be monitored by talking to the Claims Department. Changes in case reserve adequacy affect incurred loss development patterns. For example, if case reserves were less adequate in the current accident year, greater future development would be expected for those accidents than was typical in the past. Use of historical loss development factors in this situation would underestimate future development and lead to inadequate overall reserve estimates.

Sensitivity Analysis: Case Reserve Adequacy The fit of the average case reserves @ 12 months implies an annualized trend rate of 19%! This rate is substantially higher than industry trend rates for private passenger automobile liability, which are in the range of 8% to 10%.

Frequency/Severity Projection Method A line or another curve can be fitted through actual values for accident years 1993 through 1997. The fitted points for the current year can be used as estimates for the ultimate frequency and severity. R-squared is a measure of how well a fitted curve matches the data. The value can range from 0 to 1.00, where 1.00 indicates a perfect fit.

Frequency/Severity Projection Method

Selection of Tail Factors How much difference does the tail factor selection make?

Selection of Tail Factors Ultimate losses increase by 2% or $1.8 million. Loss reserves also increase by $1.8 million; however, the 2% increase in the tail factor represents a 7% increase in overall reserve levels! IBNR reserves are increased by an even higher percentage as a result of an increase of 2% in the tail factor