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CAS Predictive Modeling Seminar

CAS Predictive Modeling Seminar. Predictive Modeling for a Commercial Insurance Company with Little or No Data Scott Bronstein October 5, 2004. Discussion points. Data available for predictive modeling Models in use Sample results Summary.

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CAS Predictive Modeling Seminar

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  1. CAS Predictive Modeling Seminar Predictive Modeling for a Commercial Insurance Company with Little or No Data Scott Bronstein October 5, 2004

  2. Discussion points • Data available for predictive modeling • Models in use • Sample results • Summary

  3. Modeling guidelines with minimal data • Know your target population • Tap into business data - even minimal amount of business performance data can be predictive • Use consumer data as appropriate • Segment the population • Validate at logical intervals - can recalibrate if necessary

  4. Twenty-two million businesses Source: Statistical Abstract of the United States Composition of U.S. businesses Publicly held<1% Partnerships 15% Privately held 7% Sole proprietorships 77% 190 million consumers

  5. Experian Business Information Solutions • No other company houses these assets under a single roof Integrated Information Solutions Business Public Record Database National Business Credit Database National Business Database National Consumer Credit Database

  6. Credit Database Sources Marketing database Public record Experian Business Credit Database Firmographics Trade payment Banking, insurance, leasing Collections Standard & Poors

  7. Marketing Database Business White Pages Credit Database National Business Database Data Vendors DBA/FBN (new business) Televerified Data National Yellow Pages

  8. Targeting Acquiring • Score portfolio for targeted pre-qualified & cross-sell efforts • Select pre-qualified names from BIS marketing list • Process new applications using internet, online software or CPU access. • Bulk portfolio purchases Managing Maximizing • Score portfolio to: • Monitor application policies • Track ongoing customer risk • Prioritize / expedite collections • Determine gross measures of portfolio risk Score portfolio to: • Help process renewal • Identify cross-sell and up-sell opportunities • Adjust credit limits Scores Offer Solutions Across Customer Lifecycle

  9. Intelliscore overview • Used primarily in small business lending, commercial card, leasing, telecommunications, and business services • Commercial Intelliscore • Utilizes commercialcredit, business demographics, public record and legal information • Small Business Intelliscore • Utilizes commercial and consumer credit, business demographics, public record and legal information

  10. Intelliscore models are segmented scoring systems • Businesses within a modeling sample are clustered or segmented by common characteristics such as • Size of business • Credit history • Data type availability • When predictors behave differently between clusters, the file should be segmented so the clusters can be modeled independently to capture subtle nuances in payment behavior

  11. Model segment integration • Scoring system integrates model segments into one uniform score • Each Intelliscore model solution consists of several modeled segments each with its own set of variables and raw score Model Segment 3 score Model Segment 2 score Model Segment 1 score Transformation Uniform 0 - 100 score

  12. Commercial Intelliscore Report • Key features Commercial credit and business demographic information for up to fifteen elements Credit score and percentile Action Score factors

  13. Commercial Intelliscore18% to 60% lift in predictiveness

  14. Small Business Intelliscore12% to 35% lift in predictiveness

  15. Summary • Wide array of business data is available • Familiarity with how to use the data is critical • Small business is broadly defined and data availability will vary • Segment the population • Commercial risk scores are predictive across several industries - and probably others

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