Werner Ch 9

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Werner Ch 9
2010-04-28 12:56:31
Exam TIA Werner

Exam 5 TIA Werner ch 9
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  1. 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
  2. Adverse Selection Process will continue until
    • Company improves rate segmentation
    • Becomes insolvent
    • Decides to focus on high-risk insureds and price accordingly
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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