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Predictive Modeling for Small Commercial Risks

Learn about the evolution of underwriting challenges, statistical modeling implementation, and the benefits of predictive models for small commercial risks. Discover how scoring models can enhance pricing decisions, obtain actionable business insights, and navigate the implementation process effectively.

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Predictive Modeling for Small Commercial Risks

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  1. Predictive Modeling for Small Commercial Risks CAS PREDICTIVE MODELING SEMINAR Beth Fitzgerald ISO October 2006

  2. Agenda • Definition of Risks • Evolution/Challenges of Underwriting • Use of Statistical Modeling • Implementation of Predictive Models • Relativity Analysis

  3. Small Commercial Risks • Size • Area • Gross sales • Low premium • Type of risk • Office, apartments/condominiums, retail, service • Contractors, restaurants, motels, self-storage facilities • Light manufacturing • Rating • CPP vs. BOP

  4. TYPES OF SMALL COMMERICAL RISKS

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

  6. Small Business Underwriting Challenges • Low average premium • Doesn’t warrant expensive hands-on underwriting. • Underwritten more as a commodity • Experienced underwriters focused on larger accounts

  7. Underwriting Small Commercial Risks • Establish underwriting guidelines for type and size of risk • Review application information • Numbers of years in business • Financial information • Location information • Building characteristics

  8. Market Research – What the market says it needs • Fast and consistent small business underwriting process • Take advantage of technology • Add intelligence to the policy writing process

  9. What Makes Statistical Modeling Possible? • Advanced computer capabilities • Processing • Data access • Advanced statistical data mining tools

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

  11. Development of Scoring Models • Analyze historical policy and loss data • Link policy and loss data with internal & external data: • Business operational & financial data • Location data – demographic, weather • Other – building, agency • Use statistical data mining software and techniques

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

  13. Scoring vs. Rating Manual • Evaluation of scoring variables relative to rating factors • More refined detail than rating manual • Factors not included in rating manual

  14. Modeling Issues with Small Commercial Risks • Less homogeneous risks than with personal lines risks • Variable selection varies by peril and type of risk • Business operational and financial data not always available

  15. Implementation of Model Solution focus/usage: • Suitability of risk for underwriting decision • Source for additional pricing factors • Consistency in underwriting/pricing decisions • Compliance with regulations based on implementation decision • Consider model alone or model with other information available from application

  16. Implementation of Model Workflows: • Underwriting • New Business • Renewal business • Rating • Pricing • Coverage Adjustment

  17. Business Implementation of Model • Strategic Plan - need management involvement • Prepare Announcement/Training Material for Internal & External Customers • Coordinate Implementation • Monitor Feedback/Adjust Implementation

  18. Benefits of Scoring Model • Reduction of underwriting expense through automated scoring process efficiencies • Fast, cost-effective tool to help you determine which risks to insure • More accurate pricing decisions • Expansion into new markets

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

  20. 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

  21. Amount of Insurance Relativities • Amount of Insurance identified as important variable in BOP Scoring analysis • Decision to include as variable in manual and not in scoring model

  22. Multivariate Analysis for Amount of Insurance Relativities • Variables used for Property • Occupancy • Sprinklered–rating identifier • Protection • Construction

  23. Predictive Modeling for Small Commercial Risks • Increased implementation of models in underwriting/pricing of risks • Account view vs. individual line of business view – BOP, CPP, CA, WC • Set of risk component variables in addition to overall score • Additional data sources • Refinement in manual rating

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