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W Loss Rating Models: Challenges and Opportunities Brian Ingle, FCAS, MAAA WC-3 Perspectives on Pricing Large Accounts 2007 CAS Ratemaking Seminar.

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Presentation on theme: "W Loss Rating Models: Challenges and Opportunities Brian Ingle, FCAS, MAAA WC-3 Perspectives on Pricing Large Accounts 2007 CAS Ratemaking Seminar."— Presentation transcript:

1 W Loss Rating Models: Challenges and Opportunities Brian Ingle, FCAS, MAAA WC-3 Perspectives on Pricing Large Accounts 2007 CAS Ratemaking Seminar

2 W 1 Large Account Pricing: Current Practice Loss Rating Models Play Dominant Role Sole Pricing Engine for Some Dramatic Differences in Model Sophistication Among Insurers

3 W 2 Loss Account Pricing Perspective Workers Compensation Pricing Continuum: Manual RatingExperience RatingLoss Rating SmallAccount SizeLarge

4 W 3 Loss Rating Defined Pricing Model With: Full Credibility Assigned to Account Experience within “Primary” Occurrence Layer Excess Losses Often Derived from “Primary” via industry ELFs Often Totally Divorced from Bureau Loss Costs Final Output: Loss Cost per Unit of Exposure by Layer

5 W 4 Loss Rating Challenges Mission Creep and Credibility Every Account is “Special” The Client Wants to be Rated on its Own Experience Except for that One Year

6 W 5 Loss Rating Challenges Excessive Subjectivity Selecting “Primary” Loss Limit (100% Credibility) Weighting Loss Rates Difficult to Preserve Value as Benchmark

7 W 6 Loss Rating Challenges Poster Child for Credibility and Subjectivity Issues Avg Annual Prem. $80K!

8 W 7 Loss Rating Challenges Significant Resource Commitment Programming/Testing of Model Maintenance/Updating of Factors Data Verification, Input and Calculation Education and Control

9 W 8 Addressing the Challenges: Thoughts Mission Creep Define Minimum Size Client Based on Industry Standard - E.g. minimum 60% credibility under NCCI X-Mod Rating Use Loss Rating Only When Credible Client Specific Development Triangles Available Manage Internal Expectations

10 W 9 Addressing the Challenges: Thoughts Subjectivity Cap Loss Limitation at 10% State Reference Point Capture Average Loss Indication as Well as Selected for Management Reports Compare Output to Industry Loss Cost

11 W 10 Addressing the Challenges: Thoughts Resource Control Mission Creep Automate Mechanical Controls Limit Users

12 W 11 Loss Rating Opportunities Remedy Experience Rating Credibility Limitation Client Specific Loss Development. Capture more years than Experience Rating. Capture Non-Standard Severity Curve Greater insight into insured operations.

13 W 12 X-Mod Credibility Limitations I.e. 25 x State Avg. Claim Cost

14 W 13 Loss Rating: Company Example Credibility: Actuary Determines Expected Claim Count Yielding 100% Credibility Only Accounts with 100% Credibility in 1 st $10K Qualify ELPPF Curves Determine Expected Count by Layer Expected Claim Count Determines Credibility in Layer Subjectivity: Loss Limitation Set Where 100% Credibility Ends

15 W 14 Loss Rating Opportunities: Loss Rating Adds Most Value Where: XMod Credibility Inappropriate Severity Curve Can be Shown to Differ Materially From ELPPFs FIGHT THE CREEP

16 W 15 Disclaimer: In preparing this Presentation, Willis Re has relied upon data provided by external data sources. No attempt has been made to independently verify the accuracy of this data. Willis Re does not represent or otherwise guarantee the accuracy or completeness of such data, nor assume responsibility for the result of any error or omission in the data or other materials gathered from any source in the preparation of this Presentation. Willis Re shall have no liability in connection with results stemming from the analysis including but not limited to any errors, omissions, inaccuracies, or inadequacies associated with the data. Willis Re expressly disclaims any and all liability to any third party in connection with this Presentation. In preparing this Presentation, Willis Re has used procedures and assumptions that Willis Re believes are reasonable and appropriate. However, there are many uncertainties inherent in actuarial analyses. These include, but are not limited to, issues such as limitations in the available data, reliance on client data and outside data sources, the underlying volatility of loss and other random processes, uncertainties that characterize the application of professional judgment in estimates and assumptions, reinsurance collectability, etc. Ultimate losses, liabilities and claims depend upon future contingent events, including, but not limited to, unanticipated changes in inflation, laws, and regulations. As a result of these uncertainties, the actual outcomes could vary significantly from Willis Re’s estimates in either direction. Willis Re makes no representation about and does not guarantee the outcome, results, success, or profitability of any insurance or reinsurance program or venture, whether or not such program or venture applies the analysis or conclusions contained herein. Please consult your own independent professional advisors before making any decisions related to any information contained herein. This Presentation is provided for informational purposes only; it is not intended to be relied upon, and is not intended to be a complete actuarial communication. A complete communication can be provided upon request. Willis Re actuaries are available to answer questions about this Presentation. The statements and opinions included in this Presentation are those of the individual speakers and do not necessarily represent the views of Willis Re or its management.

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