errata Flashcards
2 step trending
current trend factor = latest avg WP @ curr/hist avg EP @ curr
2012 trended OLEP = 2012 EP @ curr*current trend factor*prospective TF
=(2012 EE * 2012 avg EP @ curr)*current trend factor*prospective TF
=2012 EE*latest avg WP @ curr*prospective TF
**prospective TF is WD to WD
loss costs
loss & LAE
no UW exp, profit, variable expenses, fixed expenses
just costs due to losses
company change using ind LC change from industry
and proposed deviation
company change = proposed dev/current dev * (1+ind LC change from industry)
proposed dev = (expected LC diff+1)/(1-V-Q)
fixed expense trend with indices
fixed exp trend = %*ECI + (1-%)*CPI
employee cost index
consumer price index
APPA: why is 1st iteration higher than convergence value
disproportionate exposure is concentrated i higher and lower cost areas
*iterative method is ued to adjust for unequal distributions of underlying exposures
calculating new base rates and new relativities using LRA
calc credibility weighted change factors
calc new base rate = inforce base*cred weighted change factor/credibility weighted change for total
***total factor is weighted using inforce premium
new relativities = new base rate/BL new base rate
ISO experience rating: CSCL and expected development
CSLC = BL prem * ELR * detrend
expected dev = CSLC * %unrptd * EER
nonmodeled CAT PP calculation
need all yr avg for CAT-to-AIY ratio
need to load for ULAE
need to multiply this by AOI/exposure that corresponds with future avg ED
**if avg ED =1/1/16 then equal weight for CY 2015 & 2016
nonmodeled CAT = avg AIY/exposure*non-modeled CAT prov/AIY
why is using AIY useful for nonmodeled CAT PP
using AIY is simple way to adjust ratio for inflation
if PP was based on non-modeled/house years, ratio would increase over time due to inflation
residual indication and net trend and trended present rates indication
residual indication =(1+latest ind RC)/(1+last RC taken) -1
net trend = (1+proj loss trend)/(1+proj prem trend)-1
trended present rates indication = (1+residual)(1+net trend)^period-1
trending: why adjust for one-time changes
using prem @ historical rate levels is inappropriate when selecting trends since one time changes would be picked up by trend even though we don’t expet these to continue in future
overfitting
estimates reflect noise in addition to true signal
will replicate historical data well but less reliable for future data
why is PY the best match of loss and exposure?
provides the best match between premiums and losses since the losses come from the same policies from which premium is earned
PY, CY, AY losses
PY: Losses are summarized by the years the policies containing those losses were written.
CY: Losses are summarized for loss transactions occurring during the CY.
AY: Losses are summarized by the years in which the losses occurred.
adverse selection
Adverse selection occurs when an insurer doesn’t use a risk characteristic that is being used by other insurers. Since other insurers will attract the lower risk insureds based on this risk characteristic, the insurer not using the risk characteristic will be left with a higher than proportional share of the higher risks, for which it does not accurately price or underwrite. As such, the insurer will have a higher loss ratio.
skimming the cream
Skimming the cream occurs when an insurer uses a risk characteristic in underwriting or marketing to attract lower cost risks without lowering the price. Since it is not lowering the price for these risks, the insurer will have a lower loss ratio.
RY diagram for CMP and OP
OP: A
CMP: B, C, D, E
credit vs. indicated relativity for deductibles
credit = LER
indicated relativity = 1- LER
LER = loss below d/ground up loss
asset share pricing vs traditional RM techniques
Traditional ratemaking techniques only consider the experience of a single period of time. As such, they fail to consider differences in persistency between risks. Persistency can have a significant impact due to loss and expense differences between new and renewal business. The asset share pricing model accounts for this by introducing multiple periods, persistency, and different assumptions for new and renewal business.
why would insurer and insured perfer retrospective rating
Retrospective rating reduces the pricing risk of the insurer, so an underwriter may only accept a questionable risk if it is retrospectively rated.
If an insured expects to have very favorable loss experience, then it can save on premium by choosing retrospective rating.
questions to ask UW r to help identify the source of the change in the loss data
Have there been any changes in underwriting guidelines in an effort to grow the business? Have we been writing higher policy limits or lower deductibles?
off-balance factors
- relativity change factor for BL
- avg current relativity/avg proposed relativity
*if using premium for weighting, need OLP @ base = prem/(curr rell)
- 1/avg rel change factor
*if using prem, use OLP
asset share pricing: calculating profit
need PV of profit and PV of prem to calculate profit as proportion of prem
***need to use cumulative persistency
capping RC: 2 practical issues and 1 reason why insurer would cap
Capping rate changes on some insureds may lead to the total rate change being different than the targeted rate change of 6%. In that case, the rates may be inadequate (or excessive).
The capping rule would need to be programmed into the insurer’s computer systems, which can get quite complicated
may implement the rule to try and prevent policyholders that would otherwise receive a larger rate increase from shopping for insurance with other insurers. The insurer would be trying to maximize policyholder retention.
C/O Dev factor calculates what estimate
C/O Dev factor * C/O = Unpaid claims
expected emergence calculations
(Ult-cumulative rptd)/(1-%rptd) * (%rptd@t+1 - %rptd@t)
=(current IBNR)*portion of IBNR expected to emerge
-preserves current IBNR when calc expected development -> if you let t+1 be ultimate, you get back current IBNR
cumulative rptd * (LDF t to t+1 -1)
Ult * (%rptd@t+1 - %rptd@t)
- the last 2 methods would result in different total IBNR if you let t+1 be ultimate
- all 3 will be equal if ultimate claims were determined on cumulative reported claims and selected development pattern
Kittel: how to
Need to calc Incurred claims and avg paid&incd for each CY
incd = paid+change(case)+change(IBNER)
then calc ratio of ULAE to avg paid & incd = W*
estimate unpaid ULAE = W* (pure IBNR+50%*case)
*if CMP, can assume IBNR is IBNER and not pure IBNR
*Kittel recognizes that ULAE is incurred as claims are reported even if no claims payments have been made -> more accurate for growing/shrinking
