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2006 CAS RATEMAKING SEMINAR

2006 CAS RATEMAKING SEMINAR. CONSIDERATIONS FOR SMALL BUSINESSOWNERS POLICIES (COM-3) Beth Fitzgerald, FCAS, MAAA. Agenda. Definition of Risks Market Needs Use of Statistical Modeling Scoring Model Development Amount of Insurance Relativity Factors. Underwriting Small Commercial Risks.

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2006 CAS RATEMAKING SEMINAR

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  1. 2006 CAS RATEMAKING SEMINAR CONSIDERATIONS FOR SMALL BUSINESSOWNERS POLICIES(COM-3) Beth Fitzgerald, FCAS, MAAA

  2. Agenda • Definition of Risks • Market Needs • Use of Statistical Modeling • Scoring Model Development • Amount of Insurance Relativity Factors

  3. Underwriting Small Commercial Risks Eligible for Businessowners • Size • Area • Gross sales • Type of risk • Office, apartments, retail, service • Contractors, restaurants, motels, self-storage facilities • Light manufacturing • Rating • Class-rated • Low average premium

  4. Growth in Small Businesses Source: Office of Advocacy, U.S. Small Business Administration

  5. Market Needs • Efficient use of technology to allow for faster, more consistent underwriting decisions • Add intelligence to the policywriting process • Low-cost solution due to low premium size

  6. What Makes Statistical Modeling Possible? • Advanced computer capabilities • Advanced statistical data mining tools

  7. Uses of Statistical Modeling • Scoring of small commerical risks • Improve loss predictability of risks • Increase accuracy of pricing decisions • Cost effective, consistent underwriting • Improve manual rating of risks

  8. Development of Scoring Models • Analyze historical policy and loss data • Link policy and loss data with external data: • Business financial data • Weather • Demographics • Use statistical data mining software and techniques

  9. Modeling Process Data Linking Data Gathering Data Cleansing Analyze Variables Evaluation Business Knowledge Determine Predictive Variables Modeling

  10. Statistical Modeling Techniques Balance good fit with explanatory power • Generalized Linear Models • Classification Trees • Regression Trees • Multivariate Adaptive Regression Splines • Neural Networks

  11. Benefits of Scoring Model • Fast, cost-effective tool to help you determine which risks to insure • More accurate pricing decisions • Reduce underwriting expense through automated scoring process efficiencies • Expand your markets

  12. Risks of Not Scoring • Lost market share • Greater risk of adverse selection

  13. Use of Statistical Modeling in Manual Rating • Improve rating relativities of current rating factors • Add new rating factor to manual using a multi-variate statistical model

  14. Amount of Insurance Relativities • Amount of Insurance identified as important variable in BOP Scoring analysis • Partially handled by insurers • Decision to include as variable in manual and not in scoring model

  15. Property BuildingsOne Dimensional

  16. Current Rating for BOPProperty • Base loss costs by state/territory for buildings & personal property • Multi-state Relativities • Rate number • Sprinkler • Protection • Construction

  17. Current Rating for BOPLiability • Base loss costs by state/territory for occupants & lessors • Occupants vary by AOI, Payroll or Sales exposure base • Multi-state rating relativities • Class group

  18. Multivariate Analysis for Amount of Insurance Relativities • Variables used for Property • Rate number • Sprinkler • Protection • Construction • Variables used for Liability • Class group

  19. BOP Implementation of AOI Relativities • Incorporation into manual • Definition of base amount of insurance • Building - vary by state/region • Timeline • 12 month lead time • Interaction with other possible changes • Filing late 2006

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