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Predictive Modeling for Small Commercial Risks

Predictive Modeling for Small Commercial Risks. CAS PREDICTIVE MODELING SEMINAR Beth Fitzgerald ISO October 2006. Agenda. Definition of Risks Evolution/Challenges of Underwriting Use of Statistical Modeling Implementation of Predictive Models Relativity Analysis. Small Commercial Risks.

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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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