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Adverse Selection
- 1. Company fails to segment business based on meaningful characteristic used by other insurers or does not charge the appropriate diff erential 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 di fferentiate
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Adverse Selection Process will continue until
- Company improves rate segmentation
- Becomes insolvent
- Decides to focus on high-risk insureds and price accordingly
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Speed and severity of process depends on various factors
- Whether insureds have full and accurate knowledge of competitor rates
- How much price alone influences purchasing decisions
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Discuss "skimming the cream" to gain competitive edge
- If an insurer notices a positive characteristic that is not used in their rating structure (or in competitors), the insurer can market those with the positive characteristic and try to write more of them (skim the cream).
- The insurer will then benefit from lower loss ratios and better profilability,
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Criteria for evaluating Rating Variables
- 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: Aff ordability, Causality, Controllability, Privacy
- 4. Legal Criteria - laws and regulations: Statutes, Regulations
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Distortion of Pure Premium approach to calculate relativities
- 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 e ffect
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Loss Ratio Approach
- Di fferences from Pure Premium method:
- LR approach uses premium instead of exposure
- LR approach calculates an adjustment to the current relativity
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Adjusted Pure Premium Approach to calculate relativities
- 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
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