B.6. Aggregate excess loss cost estimation Flashcards
Advantages of using vertical or horizontal slices to calculate areas
The vertical slice approach is more intuitive, but the horizontal slice approach is faster if we need to calculate expected XS losses for multiple limits.
Definition and purpose of entry ratios
Entry ratios are ratios of actual to expected loss.
Allows us to apply aggregate distributions to risks of different sizes.
Definition of Table M charge and savings
Steps to build Table M
Why a separate Table M charge is needed for different risk size groups
Because aggregate loss distributions vary by risk size, with larger risks having more stable results.
How Table M can account for inflation
After risks become larger due to inflation, they can be mapped to different existing Table M columns that are more appropriate for their new risk size. The table M values themselves don’t necessarily need to be recalculated.
Table M graph
Value of Table M and L charges and savings as entry ratios go to 0 and infinity
1st and 2nd derivatives of Table M charge and savings
Graph and formula for Table M charge for infinite risk size
Graph of Table M charge by risk size and entry ratio
How an overestimation of expected losse impacts insurange charge
- We might incorrectly use the Talbe M curve for a larger risk size, which would result in a lower charge for a fixed entry ratio.
- We might incorrectly use too low an entry ratio (same aggregate limit / overestimated expected loss), which would result in a higher charge for a fixed Table M curve.
Fisher’s example assumes the Table M curbe doesn’t change, in which case only the 2nd item above happens.
In what cases are percentage and dollar error in insurance charges greatest?
- The percentage error in insurance charges is greatest for large policies with high entry ratios.
- The dollar error in insurance charges is the greatest for large policies with low entry ratios.
Graph of Table M savings by risk size and entry ratio
Formula and purpose of Adjusted Expected Loss for which curve to use
Some policies of even the same size are riskier than other policies, so we can adjust the expected losses for a risk to match those of a risk with a different size but similar variance in their aggregate loss distribution (the same distrubtion shape).
Adjusted expected loss = E[A] x State HG differential
The state HG differentiel adjusts for :
- RIskiness: Some classes have a higher proportion of larger claims
- Location: Differences in medical care costs, benefits, court verdicts, etc.
Impact of NCCI move to Adjusted expected claim count
This will mean the NCCI will no longer need to adjust the mapping between expected losses and risk size group (Table M column) due to inflation.
The NCCI will continue to apply a state / HG factor to the claim counts, such that riskier states / HG’s will lower the risk’s expected claim counts, which means the variance of the aggregate loss distribution will be higher. This new approach should work well if severity distributions differ in size but not in shape, otherwise we would also need to adjust for differences in the severity distribution. This need to adjust for severity distributions is more likely to exist for General Liability (products liability in particular) and excess of loss reinsurance.
General formula for retro premium without per occurence limit but with max and min ratable loss