Addressing an Imbalanced Fund Ins Eqn

Question 1

Some options to addressing imbalanced eqn:

Answer 1

• do nothing: if profit targets are being met and rate is still competitive
• change UW profit target: profit could be lowered or longer-term view on profitability
• change UW expenses: changing commission rates or layoffs
• change LAE: more or less aggressive approach to defending claims
• change losses: coverage could be reduced or UW guidelines changed
• change premium: rates could be changed or exposures re-stated

*rate changes are generally preferred way of addressing imbalanced equation

Question 2

reasons why equation is imbalances

Answer 2

could be UW profits are too high (insurer’s rates are too high to be competitive) or too low (insurer is not meeting its profit targets)

Question 3

statement of principles RM

Answer 3

1. Rate is an estimate of expected value of future costs
2. Rate provides for all costs associated with transfer of risk
3. Rate provides for all costs associated with an individual risk transfer
4. Rate is reasonable and not excessive, inadequate, or unfairly discriminatory if it is an actuarially sound estimate of the expected value of all future costs associated with an individual risk transfer

Question 4

types of adjustments to historical data

Answer 4

1. large events and anomalies: replace ind large claims and CATs in hist losses with longer term average loading
2. one-time changes: adjust hist data for RCs, law changes, coverage changes, and benefit changes
3. continuous changes: adjust for any trends impacting premium, exposures, and losses over time
4. development: adjust to ultimate levels
5. load for UW expenses and LAE
6. setting an UW profit target
7. reinsurance costs: historical data may be used net of reinsurance or may be gross and expected cost for reins will be loaded with expenses
8. credibility: if historical data is not credible on its own, cred-weighted average may be taken with another set of related data

Question 5

why do we adjust historical data

Answer 5

so that it is appropriate to use for future period

adjs should be made to reflect any changes between hist period and future period that impact costs

Question 6

anomalies in loss data

Answer 6

• anomalies are large losses from individual losses AKA shock loses or CAT losses stemming from many claims
• shock loss definitions vary by insurer and LOB and part of this difference is related to size of BOB

Question 7

if you do not adjust for shock losses or CATs,

Answer 7

overestimate future losses when these events are in dataset and underestimate when events are not in dataset

Question 8

goal in pricing for shock losses and CATs

Answer 8

to produce rates that cover these costs over a longer period of time and don’t overreact to lucky or unlucky years

Question 9

Common options in adjusting data:

Answer 9

1. Cap losses @ basic limits
   - expected loss estimate will be only for basic limits losses and will need to separately derive rates to price for losses above basic limits (ILFs)
   - if use this to calc estimated LRs, need to adjust historical prem to basic limit rates
   - doesn’t work for WC since they do not have policy limits
2. Cap losses and apply and excess loss loading
3. Remove group-up shock losses and apply shock loss loading

Question 10

Choosing cap level

Answer 10

goal is to balance including as many losses as possible below cap and minimizing volatility of losses under cap

Question 11

excess loss loading most common choice and considerations

Answer 11

• most common is to use long term average and ratio is then applied to losses below cap from hist data used in pricing
• choice of yrs: balance stability of average and responsiveness to changes
• changes in average severity of claims over time should be considered -> use cap level based on future policy period cost levels and trending hist losses to this level and calc ratio of trended excess losses to non-excess losses OR indexing cap level to reflect changing cost levels so cap level varies for each year

Question 12

most common approach for CAT losses

Answer 12

• common approach is to remove all CAT losses from data and add expected loading back in for those losses
• often split into modeled and non-modeled components

Question 13

non-modeled CAT

Answer 13

events that occur relatively frequently compared to larger CATs like hurricanes

-usually long-term average is used, exposure growth in CAT prone areas over time should be considered

Question 14

modeled CAT

Answer 14

-CAT models are generally used to estimate losses from events like EQ when long term average doesn’t have enough data to provide reliable estimate; uses actual BOB info along with simulated CAT events to generate loss distributions and expected losses

Question 15

no such thing as average CAT year

Answer 15

idea is 50 years worth EQ prem would cover losses for single 1-50 year EQ

Question 16

non-pricing actions for CATs

Answer 16

restricting writings in high risk areas, requiring higher deductibles in high risk areas and purchasing reinsurance