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2008 CAS SPRING MEETING PROJECT MANAGEMENT FOR PREDICTIVE MODELS

This project management framework aims to enhance predictive modeling practices in the insurance sector. It outlines key inward and outward facing goals, focusing on collaboration with the IIA to develop models using insurer data for personal auto, homeowners, and commercial lines. Emphasizing the importance of simplicity, effective data management, and monitoring model effectiveness with tools like Gini index and decile plots, the framework also stresses the need for continuous updates and communication between modelers and regulatory experts to maintain compliance and accuracy.

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2008 CAS SPRING MEETING PROJECT MANAGEMENT FOR PREDICTIVE MODELS

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  1. JOHN BALDAN, ISO 2008 CAS SPRING MEETINGPROJECT MANAGEMENT FOR PREDICTIVE MODELS

  2. Where Does Modeling Fit In?

  3. Modeling Division – Outward-Facing Goals • Work with IIA to develop predictive models for the major lines of insurance, using insurer data. • Personal Auto • Homeowners • Commercial Lines

  4. Modeling Division – Inward Facing Goals • Make greater use of predictive modeling techniques within areas of traditional ISO ratemaking: • Loss costs • Classifications • Disseminate modeling knowledge and expertise to pricing actuaries via training, modeling projects.

  5. Developing Resources • Staff Knowledge • Good sources re: GLMs: • “A Practitioner’s Guide to Generalized Linear Models” – Anderson, Feldblum, et. al. • “Generalized Linear Models” – McCullagh and Nelder • Generalized Linear Models for Insurance Data – de Jong and Heller • “A Systematic Relationship Between Minimum Bias and Generalized Linear Models” – Mildenhall

  6. Developing Resources • Software • PC SAS • Interactive aspect: advantage AND disadvantage • Graphics (no more +-----) • Avoid divisional chargebacks! • R (used for MARS, for example) • Incredibly flexible, since object oriented • Special purpose modules available on Web • Widely adopted in statistical, academic world • Free! • Other software packages, as needed

  7. Avoid Scope Creep!

  8. Avoid Scope Creep! • Firm Project Management • Consolidated development platform

  9. Building Predictive Models • Know your data • Get data in common format, at level of individual risk. • Interface with insurer to: • Understand their database structure • Examine univariate distributions to clarify the meanings of data elements, unclear codes and missing values.

  10. Predictive Models • Know your model input • Not all potential model variables will be available at the time of deployment. Determine which ones will be. Considerations: • Simplicity of input • Rating variables specified as offsets • Lookup time • Nature of model (marketing models)

  11. Predictive Models • Know your model output • What are you producing? • Frequency vs. severity vs. pure premium vs. loss ratio • Do you want a relativity to a specified base risk?

  12. Measuring Model Effectiveness • Measures of Lift • Decile plot • Gini index What these share is an ordering of risks, against which experience is evaluated. • Lift is measured against the rating system currently in place. Define sort order by: • Modeled loss cost to current loss cost; or • Modeled loss cost to average modeled loss cost for risks currently rated identically (for example, in a location model, risks in same territory)

  13. Measuring Lift – Decile Plot

  14. Measuring Lift – Gini Index

  15. Model Diagnostics

  16. Models and Regulation • Establish dialogue between modelers and legal/regulatory experts • Modeling ground rules • Restricted modeling variables • Model variable creation techniques • Interpretability of final model form • Model Smoothing • Reason codes • Diagnostics

  17. Updating the Model • Know your product life cycle • Input updates • Insurance data • Third-party data • Output/Model updates • Recalculation • Re-estimation • Rebuilding

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