- TIA EXAM 5 - WERNER CH 09-2.txt

1. Company fails to segment business based on meaningful characteristic used by other insurers or does not charge the appropriate differential when others do:

High-cost insureds select a company due to that company not differentiating these risks from low-cost risks

2. Results in distributional shift toward higher-risk insureds for company that doesn't differentiate

• Company improves rate segmentation
• Becomes insolvent
• Decides to focus on high-risk insureds and price accordingly

• Whether insureds have full and accurate knowledge of competitor rates
• How much price alone influences purchasing decisions

Company identifies a characteristic that differentiates risk that other companies are not using

• Use this information to:
• 1. Implement a new rating variable
• 2. Use for risk selection, marketing or agency management (may use characteristics to identify, attract & select lower-risk insureds)

• 1. Statistical Criteria - rating variables should reflect the variation in expected costs among diff groups:
• Statistical significance, Homogeneity, Credibility

• 2. Operational Criteria - must be practical to use in rating algorithm:
• Objective, Inexpensive to administer, Verifiable

• 3. Social Criteria - social acceptability of using a particular risk characteristic:
• Affordability, Causality, Controllability, Privacy

• 4. Legal Criteria - laws and regulations:
• Statutes, Regulations

• 1. Statistical significance:
• Expected cost estimates should vary for different levels of rating variables
• Estimated differences should be within an acceptable level of confidence & be relatively stable over time

• 2. Homogeneity:
• Levels should represent distinct groups of risks with similar expected costs (homogeneous within group, heterogeneous between groups)

If grp contains materially diff risks, further subdivide

KEY for class analysis is to ID and grp risks with similar expected costs

• 3. Credibility
• Group should be large enough to measure with sufficient accuracy

• 1. Objective Definition
• Little ambiguity
• Class definitions should be mutually exclusive & exhaustive

• 2. Inexpensive to administer
• If cost outweighs potential benefit, doesn't make sense to use

• 3. Verifiability
• Insureds may lie if reduces premium => honest insured end up paying more than they should

Consider cost to verify vs. cost of inaccuracy

• 1. Affordability
• Especially important when insurance is required
• For extreme high-level risks, may result in unaffordable premium

• 2. Causality
• Implies intuitive relationship to insurance costs
• Preferable from social perspective that rating variables based on characteristics that are causal in nature

• 3. Controllability
• Insured can control this variable
• Insured motivated to improve risk characteristic to decrease rate

• 4. Privacy
• Affects accuracy, verifiability and administrative cost

• 1. Statutes
• Can impose restrictions on insuers, vary by state
• e.g. Rates cannot be excessive, inadequate or unfairly discriminatory

• 2. Regulations
• Include details about what can & can't be in risk classification
• Company must follow the laws & regulations of jurisdiction of where writes business

• 1. Indicated Pure Prem = Loss / Exposure
• 2. Indicated Relativity = (1)/(1tot)
• 3. Ind Relative to Base = (2)/(2base)

Assumes uniform distribution of exposures across all other rating variables

• By ignoring correlation between territory and class, loss experience of various classes can distort the indicated territory relativities:
• Results in a double-counting effect

• LR approach uses premium instead of exposure
• LR approach calculates an adjustment to the current relativity

• 1. Loss Ratio = Loss & LAE/Premium @ CRL
• 2. Ind Rel Change Factor= (1)/(1tot)
• 3. Current Relativity (given)
• 4. Indicated Relativity = (2)*(3)
• 5. Ind Rel to Base = (4)/(4tot)

Loss ratio is better than Pure Premium method but still not correct relativities

• *PP relies on exposure so each risk is treated equal regardless of class
• *LR uses prem which reflects class

Remaining distortion reflects the variation for class relativities being charged instead of true variation

• Adjustment made to Pure Premium approach to minimize impact of any distributional bias
• Use exposures adjusted by the exposure-weighted average relativity of all other variables
• Makes results more consistent with LR method

• 1. Adjust exposures by exposure-weighted avg relativity of all other variables
• 2. Indicated PP = Loss & LAE/(1)
• 3. Indicated Relativity = (2)/(2tot)
• 4. Ind Rel to Base = (3)/(3base)

Same as LR method since current class rels used for adj making indicated relativities equal

If true class rels used to determine exposure-weighted average relativities, then method would produce correct relativities