Predictive Modeling for Small Commercial Risks CAS PREDICTIVE MODELING SEMINAR Beth Fitzgerald ISO...
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![Page 1: Predictive Modeling for Small Commercial Risks CAS PREDICTIVE MODELING SEMINAR Beth Fitzgerald ISO October 2006.](https://reader036.fdocuments.in/reader036/viewer/2022062422/56649f355503460f94c52f21/html5/thumbnails/1.jpg)
Predictive Modeling for Small Commercial Risks
CAS PREDICTIVE MODELING SEMINAR
Beth FitzgeraldISO
October 2006
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Agenda
•Definition of Risks
•Evolution/Challenges of Underwriting
•Use of Statistical Modeling
• Implementation of Predictive Models
•Relativity Analysis
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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
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TYPES OF SMALL COMMERICAL RISKS
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Growth in Small Businesses
18,000,000
19,000,000
20,000,000
21,000,000
22,000,000
23,000,000
24,000,000
25,000,000
1992 1997 2000 2003
Establishmentswith less than 10Employees
Source: Office of Advocacy, U.S. Small Business Administration
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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
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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
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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
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What Makes Statistical Modeling Possible?
•Advanced computer capabilities– Processing– Data access
•Advanced statistical data mining tools
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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
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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
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Modeling Process
BusinessKnowledge
Data Linking
Data Cleansing
Analyze Variables
Determine Predictive Variables
Evaluation
Data Gathering
Modeling
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Scoring vs. Rating Manual
•Evaluation of scoring variables relative to rating factors
•More refined detail than rating manual
•Factors not included in rating manual
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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
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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
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Implementation of Model
Workflows:
• Underwriting– New Business– Renewal business
• Rating– Pricing– Coverage Adjustment
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Business Implementation of Model
• Strategic Plan - need management
involvement
• Prepare Announcement/Training Material
for Internal & External Customers
•Coordinate Implementation
•Monitor Feedback/Adjust Implementation
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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
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Risks of Not Scoring
• Lost market share
• Greater risk of adverse selection
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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
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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
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Multivariate Analysis for Amount of Insurance Relativities
•Variables used for Property– Occupancy– Sprinklered–rating identifier– Protection– Construction
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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