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2004 CAS RATEMAKING SEMINAR PowerPoint Presentation
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2004 CAS RATEMAKING SEMINAR

2004 CAS RATEMAKING SEMINAR

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2004 CAS RATEMAKING SEMINAR

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  1. 2004 CAS RATEMAKING SEMINAR CONSIDERATIONS FOR SMALL BUSINESSOWNERS POLICIES(C-5) Beth Fitzgerald, FCAS, MAAA

  2. Agenda • Definition of Risks • Current Underwriting Process • Issues • Market Needs • Why Scoring? • Model Development • Benefits and Risks of Scoring

  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. Current Underwriting Process • Establish underwriting guidelines for type of risk and size of risk • Review application information • Number of years in business • Financial information • Age/Characteristics of building • Prior loss history

  5. Underwriting Issues • Low average premium does not allow for expensive hands-on underwriting of each risk; Experienced underwriters focused on larger accounts • Small businesses underwritten more as a commodity • Expansion of eligible risks leads to less stringent underwriting of these risks • Result • Lower underwriting expenses • Higher underwriting loss ratios

  6. Market Needs • Efficient use of technology to allow for “hands-off” underwriting • Added intelligence in the policywriting process • Low cost solution for underwriting risks

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

  8. Why Scoring for Small Commercial Risks? • Improve loss predictability of risks • Increase accuracy of pricing decisions • Cost effective, low-touch underwriting • Acceptable use of scoring for personal lines risks

  9. What Makes Scoring Models Possible? • Advanced computer capabilities • Advanced statistical data mining tools

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

  11. Data Mining Process Data Linking Data Gathering Data Cleansing Analyze Variables Evaluation Business Knowledge Determine Predictive Variables Data Mining

  12. Data Mining Techniques Balance good fit with explanatory power • Generalized Linear Models • Classification Trees • Regression Trees • Multivariate Adaptive Regression Splines • Neural Networks

  13. How Do Scoring Models Work? • Access models via: • Easy-to-navigate web-based interface • High-volume batch option for ease of integration into company processing systems • Models return score reflecting future potential loss ratio for individual risks • Models provide reason codes for score

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

  15. Risks of Not Scoring • Lost market share • Higher loss ratios • Greater risk of adverse selection • Increase in acquisition costs • Reduce profitability