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exam 7 > Siewert > Flashcards

Siewert Flashcards

1
Q

what are five advantages of a high deductible program?

Siewert

A
  • achieves price flexibility while passing additional risk to larger insureds
  • reduces residual market charges and premium taxes
  • gives cash flow advantages to insured (insurer pays claim first and seeks reimbursement from insured)
  • provides incentive for insureds to control losses while protecting them from large losses
  • allows ‘self-insurance’ without demanding state requirements
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2
Q

what do excess losses refer to?

Siewert

A
  • losses excess of a specific deductible

- losses below deductible are retained by insured, losses above deductible paid by insurer - on per occurrence basis

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3
Q

how do aggregate limits work?

Siewert

A

-once aggregate deductible losses reach aggregate limit, insured stops retaining losses and insurer pays for everything else

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4
Q

when is the loss ratio approach for excess losses used?

Siewert

A
  • when no data is available

- for immature years where data is sparse

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5
Q

what does the loss ratio approach for excess losses require?

Siewert

A
  • database of individual accounts and pricing estimates
  • estimate of full coverage loss ratio
  • estimate of excess losses for both occurrence and aggregate limits
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6
Q

with what data/segmentation is the loss ratio determined?

Siewert

A
  • use company experience by state, reflecting the individual account’s premium distribution
  • supplement with industry experience if credibility is a problem
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7
Q

what are approaches for estimating the per occurrence charge?

(Siewert)

A
  • estimating excess ratio based on company experience

- use industry excess ratio

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8
Q

what is used to calculate the aggregate loss charge?

Siewert

A

-NCCI Table M, which reflects size of account, deductible, state severity relativities, prospective rating period and rating plan parameters

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9
Q

what are advantages of the loss ratio approach?

Siewert

A
  • can be used when no data is available or when data is immature
  • loss ratio estimates can be consistently tied to pricing programs
  • relies on more credible pool of company and industry experience
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10
Q

what are disadvantages of the loss ratio approach?

Siewert

A
  • ignores actual emerging experience (not as useful for mature years)
  • may not properly reflect account characteristics - dev. may emerge differently due to exposures written
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11
Q

how does an implied development approach work?

Siewert

A
  • develop full cov. losses to ult
  • dev. deductible losses to ult. by applying dev. factors that reflect inflation-indexed limits
  • determine ultimate excess losses by subtracting limited ult. losses from the full coverage ult. losses
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12
Q

what do “deductible losses” refer to?

Siewert

A

all losses limited by the deductible

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13
Q

what needs to be considered when determining tail factors?

Siewert

A

-make sure full coverage tail factor is consistent with limited loss tail factors -> don’t develop limited losses beyond unlimited losses

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14
Q

why do we index deductible limits for inflation?

Siewert

A
  • keeps proportion of deductible/excess losses constant about the limit from year to year
  • allows us to combine different experience years
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15
Q

what are two ways to determine index value?

Siewert

A
  • fit line to average severities over a long-term history

- use an index that reflects the movement in annual severity changes

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16
Q

what are 3 advantages of the implied development approach?

Siewert

A
  • provides estimate of excess losses at early maturities, even when excess losses have not emerged
  • dev. factors for limited losses are more stable than those determined for excess losses
  • estimating deductible losses helps determine the asset value represented by service revenue
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17
Q

what is a disadvantage of the implied dev. approach?

Siewert

A

doesn’t explicitly recognize excess loss development

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18
Q

how does the direct dev. approach work?

Siewert

A
  • focuses on excess dev. directly
  • given dev. factors for limited and full cov. losses, excess LDFs are calculated so that excess factors combined with limited factors balance back to full coverage factors
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19
Q

what is an advantage of the direct dev. approach?

Siewert

A

-explicitly recognizes excess loss development

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20
Q

what are two disadvantages of the direct dev. approach?

Siewert

A
  • excess factors tend to be overly leveraged and extremely volatile
  • if excess losses have not yet emerged, can’t estimate IBNR (no losses to apply factors to)
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21
Q

how does the Credibility weighting/BF approach work?

Siewert

A

relies on weighting indications based on actual experience (ie direct dev. approach) with expected values (ie loss ratio approach)

22
Q

what are three advantages of the cred. weighting/BF approach?

(Siewert)

A
  • can determine liabilities directly or indirectly
  • ability to tie into pricing estimates for recent years where excess losses have yet to emerge
  • provides more stable estimates over time
23
Q

what is a disadvantage of the cred. wting/BF approach?

Siewert

A

-ignores actual experience to the extent of the complement of credibility (may need to find weights that are more responsive to the actual experience)

24
Q

how do we index limits?

Siewert

A
  • for current year, use limits as stated

- for first prior year, adjust current year limits downward by an indexing factor

25
Q

what might we use as a tail factor?

Siewert

A

-inverse power curve with three parameters - fit on unlimited age-to-age factors, truncate

26
Q

what is the beauty of using the inverse power curve to select tail factors?

(Siewert)

A

-consistent for each limit and produces uniformly decreasing tail factors

27
Q

what is a problem with using the inverse power curve to select tail factors?

(Siewert)

A

-bias exists due to extending each limit to the same maturity -> lower limits should fully develop much sooner than higher limits

28
Q

what is the motivation behind the formulas for unlimited, limited and excess development factors?

(Siewert)

A

-partition loss dev. between limited and excess dev. in a consistent fashion -> ensure smaller limits have lower dev. factors than higher limits

29
Q

how does the distributional approach model the development process?

(Siewert)

A

-determines severity distr. params that vary over time (i.e. separate distributions for each dev. period)

30
Q

how might we estimate params for the distribution model?

Siewert

A
  • method of moments
  • MLE
  • Siewert’s approach
31
Q

what is Siewert’s approach to estimating params for the distributional model?

(Siewert)

A

-minimize chi-square between actual and expected severity relativities around a particular ded. size

32
Q

what are two advantages of using a distributional model?

Siewert

A
  • helps tie relativities to severities and provides consistent LDFs
  • allows for interpolation among limits and years
33
Q

what are two challenges in determining development for aggregate limits?

(Siewert)

A
  • difficult to determine LDFs for losses excess of aggregate limits
  • data tends to be sparse and not very credible
34
Q

how does collective risk modeling estimate development?

Siewert

A

-relies on loss distributions described for ded. limits in conjunction with claims frequency distribution

35
Q

what distributions does Siewert use to model severity and claim counts?

(Siewert)

A
  • Weibull = severity

- Poisson = claim counts

36
Q

how does dev. for losses excess of aggregate limits move with deductible size?

(Siewert)

A

-dev. decreases more rapidly over time with smaller deductibles than larger ones -> most later dev. occurs in layers above the per-occurrence deductible

37
Q

what losses contribute to aggregate limit?

Siewert

A

-only losses below the per-occurrence deductible (NOT “excess loss” - layers above the per-occurrence deductible)

38
Q

what method does Siewert recommend using to determine the final estimate of ultimate aggregate excess of loss and why?

(Siewert)

A

-BF method, due to volatility of losses excess of aggregate limits

39
Q

why can we use NCCI Table M to price large deductible policies?

(Siewert)

A
  • Table M is normally used to price retro policies

- large deductible with aggregate limit looks very similar to a retro policy with a maximum premium and per loss limit

40
Q

what is a benefit of using the NCCI Table M approach instead of collective risk modeling to estimate development?

(Siewert)

A

more practical

41
Q

what is the key to the NCCI procedure?

Siewert

A

adjusting expected losses to reflect loss limits

42
Q

what is the intent of increasing expected losses by the adjustment factor for the use of a per occurrence limit (within the NCCI procedure)?

(Siewert)

A

utilize a less dispersed loss ratio distribution -> smaller insurance charge

43
Q

how does Siewert suggest calculating insurance charges at early maturities, and what is a benefit?

(Siewert)

A
  • relate undeveloped limited losses to ultimate unlimited losses
  • benefit: reflects impact of the limit and captures dev.
44
Q

what is service revenue?

Siewert

A

revenue associated with servicing claims under a high deductible program

45
Q

how do we generally generate the service revenue?

Siewert

A

factor (loss multiplier) is applied to deductible losses, limited by an aggregate, to cover expenses that vary with those claims

46
Q

how is service revenue usually collected?

Siewert

A

as losses are paid (could also be gathered as a function of case incurred losses)

47
Q

what are two ways to handle ALAE under a high ded. program?

Siewert

A
  • account manages expense itself (ALAE not covered)

- ALAE is treated as loss and subjected to applicable limits

48
Q

what do we assume when ALAE is treated as loss?

Siewert

A

-assume expenses are equivalent to additional loss dollars

49
Q

what is the best way to split loss and ALAE for financial reporting?

(Siewert)

A

-split proportionally based on full coverage counterparts (more actuarially sound approaches may not be cost justifiable)

50
Q

what are four improvements to Siewert’s methods?

Siewert

A
  • obtain longer histories of experience
  • derive params that provide better fits to the actual data
  • determine better tail factors
  • develop more advanced approaches to index loss limits

exam 7 (21 decks)

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