W&M Ch 9

Question 1

Adverse Selection

Answer 1

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 the higher-risk insureds for company that doesn’t differentiate

Question 2

Process will continue until

Answer 2

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

Question 3

Speed and severity of process depends on various factors

Answer 3

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

Question 4

Criteria for evaluating Rating Variables

Answer 4

1. Statistical Criteria - rating variables should reflect the variation in expected costs among different groups
   - Statistical significance, 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

Question 5

Distortion of Pure Premium approach to calculate relativities

Answer 5

Assumes uniform distribution of exposures across all other rating varibles

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

Question 6

Loss Ratio Approach

Answer 6

Differences from Pure Premium method

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

Question 7

Adjusted Pure Premium Approach to calculate relativities

Answer 7

Adjustment made to the 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