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This seminar on ratemaking discusses the reasons why data for large insureds is not fully credible, including factors such as changes in operations, lack of complete data, and adjustments using claim count information.
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Pricing Large Insureds Christopher Claus American Re-Insurance 1999 Seminar on Ratemaking
Reasons Data for Large Insureds is Not Fully Credible(not an exhaustive list) • Lack of Volume/Size of Insured • Changes in Operations/Mergers & Acquis. • Significant Growth/Discontinued Operations • Changes in Limits Profile over Time for Group of Insureds • Long Tailed Lines Lacking Sufficient Historical Experience
Reasons Data for Large Insureds is Not Fully Credible(Continued) • Changes in Technology/Exposures • Lack of Complete Data • Changes in Geographic Spread • Expansion into New Markets/Products • Changes in Claims Handling
Areas Where DataMay Be Blended • LDF’s • Unlimited vs. Limited, Excess vs Ground-up • Changes in Reserving and Payment Patterns • Changes in Underlying Exposures • Selecting Tail Factors • Adjustments Using Claim Count Information • Report Year vs. Accident Year Triangles • Review ALAE Separately • Closed-to-Reported Ratios
Adjust Industry LDFs based on Trended Closed to Reported Ratios
Areas Where DataMay Be Blended • Trend • ISO Sources • WC - Medical Cost Inflation, Wage Inflation, Benefit Levels • Trend Based on Client Data • Annual Trend Rate vs Actual Indication • Remember to Review Frequency Trend
Areas where DataMay Be Blended Data • Severity Distributions • ISO Data • Fit to Client Data • Blending Client Fit with Industry
Example of BlendingSeverity Distributions • Situation: Pricing High Excess Layer • Can Price Lower Layers • But Need Severity Distribution to Price High Layers • Problem: How to Adjust Severity Distribution to Reflect Clients Data
A Report Year Approach • Report Year Approach to Price Accident Year Coverage
Company A - Accident Year Reported Loss+ALAE Development
Company A - Report Year Reported Loss+ALAE Development
Company A - Comparison of Accident Year Development Estimate
Review of Results • Historical Loss Ratios • Historical Pure Premiums