1. Glenn Meyers ISO Innovative Analytics 2007 CAS Annual Meeting Estimating Loss Cost at the Address Level

2. Territorial Ratemaking Territories should be big –Have a sufficient volume of business to make credible estimates of the losses. Territories should be small –“You live near that bad corner!” –Driving conditions vary within territory.

3. Some Environmental Features Related to Auto Accidents Proximity to Business Districts –Workplaces Busy at beginning and end of work day –Shopping Centers Always busy (especially on weekends) –Restaurants Busy at mealtimes –Schools Busy and beginning and end or school day

4. Weather –Rainfall –Temperature –Snowfall (especially in hilly areas) Traffic Density –More traffic sharing the same space increases odds of collision Others Some Environmental Features Related to Auto Accidents

5. Combining Environmental Variables at a Particular Garage Address Individually, the geographic variables have a predictable effect on accident rate and severity. Variables for a particular location could have a combination of positive and negative effects. ISO is building a model to calculate the combined effect of all variables. –Based on countrywide data – Actuarially credible

6. View as Case Study in Model Development Reduction in number of variables –Necessary for small insurers Special circumstances in fitting models to individual auto data. Diagnostics –Graphic and Maps Economic value of lift

7. Data Used in Building Model Obtained loss, exposure, classification and address for individual policies from cooperating insurers ISO Statistical Plan data Third-Party Data –Traffic –Business Location –Demographic –Weather –etc Approximately 1,000 indicators

8. Environmental Module Examples Weather: –Measures of snowfall, rainfall, temperature, wind and elevation Traffic Density and Driving Patterns : –Commute patterns –Public transportation usage –Population density –Types of housing Traffic Composition –Demographic groups –Household size –Homeownership Traffic Generators –Transportation hubs –Shopping centers –Hospitals/medical centers –Entertainment districts Experience and trend: –ISO loss cost –State frequency and severity trends from ISO lost cost analysis  Comprised of over 1000 indicators

9. Techniques Employed in Variable Reduction Variable Selection – univariate analysis, transformations, known relationship to loss Sampling Sub models/data reduction – neural nets, splines, principal component analysis, variable clustering Spatial Smoothing – with parameters related to auto insurance loss patterns

10. In Depth for Weather Component Coverage Frequency Traffic Generators Traffic Composition Weather Neural Net Weather Model 1 Weather Severity Scale 1 Temperature Model Weather Summary Variables 35 Years of Weather Data Weather Severity Scale 2 Neural Net Weather Model 2 Traffic Density Experience and Trend Severity Environmental Model Loss Cost by Coverage Frequency × Severity Causes of Loss Frequency Sub Model Data Summary Variable Raw Data

11. Environmental Model

13. Separate Models by Coverage –Bodily Injury Liability –No-Fault –Property Damage Liability –Collision –Comprehensive

14. Constructing the Components Frequency Model as Example

15. “Other Classifiers” reflect driver, vehicle, limits and deductibles. Model output is deployed to a base class, standard limits and deductibles.

16. Problems in Fitting Models Sample records with no losses –Most records have no losses –Attach sample rate, s i, to retained records –Lore is to have equal number of loss records and no loss records in the sample. Policy exposure, t i, varies –Most are 6 month or 12 month policies Need to account for sampling and exposure in building model

17. Sampling and Exposure in Logistic Regression p i = annual probability n i = 1 if claim, 0 if not t i = policy term s i = sample rate For p i <<1

18. Sampling and Exposure in Logistic Regression Set w i = s i if n i = 1 Set w i = t i s i if n i = 0

19. Overall Model Diagnostics Results are preliminary Sort in order of increasing prediction –Frequency & Severity Group observations in buckets –1/100 th of record count for frequency –1/50 th of the record count for severity Calculate bucket averages Apply the GLM link function for bucket averages and predicted value –logit for frequency –log for severity Plot predicted vs empirical –With confidence bands

20. Overall Diagnostics - Frequency

21. Overall Diagnostics - Severity

22. Component Diagnostics Frequency Example Sort observations in order of C i Bucket as above and calculate –C ib = Average C i in bucket b –p ib = Average p i in bucket b –Partial Residuals Plot C ib vs R ib – Expect linear relationship

23. Component Diagnostics Experience and Trend

24. Component Diagnostics Traffic Composition

25. Component Diagnostics Traffic Density

26. Component Diagnostics Traffic Generators

27. Component Diagnostics Weather

28. Comparing Model Output to Current Loss Costs Model output is deployed to a base class, standard limits and deductibles. –Similar to current loss cost, but at garaging address rather than territory. Define: Relativity is proportional to premium that could be charged with “refined loss costs” using the model output.

29. Relativities to Current Loss Costs

30. Newark NJ Area Combined Relativity

31. Evaluating the Lift of the Environmental Model Demonstrate the ability to select the more profitable risks Demonstrate the adverse effect of competitors “skimming the cream” Calculate the “Value of Lift” statistic Once insurers see the value of lift other actions are possible –Change prices (etc)

32. Effect of Selecting Lower Relativities

33. Effect of Competitors Selecting Lower Relativities

34. Assumptions of The Formula Value of Lift (VoL) Assume a competitor comes in and takes away the business that is less than your class average. Because of adverse selection, the new loss ratio will be higher than the current loss ratio. What is the value of avoiding this fate? VoL is proportional to the difference between the new and the current loss ratio. Express the VoL as a $ per car year.

35. The VoL Formula L C = Current losses P C = Current Loss Cost L N = New losses of business remaining After adverse selection P N = New Loss Cost After adverse selection E C = Current exposure in car years

36. The VoL Formula The numerator represents $ value of the potential cost of competitors skimming the cream. Dividing by E C expresses this value as a $ value per car year.

37. Value of Lift Results

38. Customized Model  1 …  5 ≡ 1 in industry model Severity model customized similarly

39. Summary Model estimates loss cost as a function of business, demographic and weather conditions. Demonstrated model diagnostics Demonstrated lift Indicated how to customize the model