1. June 2006 CARE Robert Eramo - Risk Assessment & Strategies,Inc. and Representative of Insureware

2. Method & Background Measure Process & Parameter Risk Use Insureware’s ICRFS Apply to Individual Triangles First Compare Triangles to Find Parameter Correlations

3. Process & Parameter Risk Coin Flip Example Variability of # of Heads due to the basic process and Knowing Fairness of Coin Triangles likewise have similar contributors to Variability of Outcome

4. Loss Outcome Variability Size of Book – Main Source Of Process Risk Relative Variance Higher For Book of Claims With 100 Expected Claims vs. 1000 Expected Claims Trends in Development and Calendar Inflation are Key parameters Knowledge Of Parameters Uncertain

5. Triangle Parameter Risk Usually Not dependent of co.’s size of book Therefore Increased Size Does Not Diversify Way to Improve Knowledge of Parameters ICRFS Example

6. Large Company Two Major Subsidiaries BOT POT Can Analyzing Both Simultaneously Improve Knowledge of Parameters First Note Initial Separate Models Look at Parameters For BOT Explicitly

7. Separate Models BOT POT Note t-statistics of development and calendar yr. parameters Specifics

8. Combined Model Benefits Note New Model Displays T-statistics specifics Comparison of Independent Models

9. Pot Development Parameter & t-Statistics Modeled Alone Dev Period 12-24 Dev Period 24-72 Dev Period 72-120 Trend-.5753-.4864-.2867 T-Statistic -16.87 -21.00 -5.075

10. Pot Development Parameter & t-Statistics Modeled in Composite Dev Period 12-24 Dev Period 24-72 Dev Period 72-120 Trend-.6255-.5102-.3809 T-Statistic -18.90 -49.54 -14.16

11. Pot Calendar Parameter & t-Statistics Modeled Alone Cal Period 90-93 Cal Period 97-99 Trend.1827 T-Statistic10.09

12. Pot Calendar Parameter & t-Statistics Modeled in Composite Cal Period 90-93 Cal Period 97-99 Trend.1792 T-Statistic 10.18

13. Application to Company vs. Statewide Experience Model Company & State Separately If there are reasonable correlations parameter uncertainty for company A model can be reduced

14. Application to Excess Layers Model Layers Separately Improve Knowledge of Parameters in XS Pricing