Going Beyond Averages, Using Spatial Going Beyond Averages, Using Spatial Data to Analyze Insurance Risk Data to Analyze Insurance Risk

Scott Tracy, QPC Scott Tracy, QPC Jennifer Lemus, ISO Innovative Analytics Jennifer Lemus, ISO Innovative Analytics

David Lapp, Farallon Geographics David Lapp, Farallon Geographics

Location Intelligence, San Francisco, 4/2006Location Intelligence, San Francisco, 4/2006

What determines how much you pay for What determines how much you pay for Auto Insurance? Auto Insurance?

Personal History (Accidents/Violations) Type of Vehicles Driving Conditions

Historically, these have been evaluated by averaging the loss experience in a given geographic area (typically a collection of zip codes)

Issues with Current Insurance PracticesIssues with Current Insurance Practices

Zip codes were constructed for the convenience of the Post Office

Zip codes are not homogenous when it comes to insurance risk, demographics

San Francisco Rating TerritoriesSan Francisco Rating Territories

ZIP Code 94109: A TourZIP Code 94109: A Tour

The Tenderloin: "...the haunt of the low and vile of every kind. ….Licentiousness, debauchery, pollution, loathsome disease, insanity from dissipation, misery, poverty, blasphemy and death are there. And Hell, yawning to receive the putrid mass, is there also. “

Robert Louis Stevenson declared "Nob Hill, the Hill of Palaces, must certainly be counted the best part of San Francisco."

Japantown

Fishermen’s Wharf

Factors that affect Driving ConditionsFactors that affect Driving Conditions

Businesses Street Types Use of Mass Transit Weather Commute Patterns Population Density

Geoprocessing RequirementsGeoprocessing Requirements

Past: ~300 parameters per Block Group... ~60M values

Block Groups (~200K)

Number of features per type (SIC, CFCC etc) per distance

Distance to nearest per type

Elevation statistics (avg, var, min, max)

Nearest traffic data

Business locations (~3M)

Landmark locations (~1M)

Traffic data locations (~1M)

Elevation data (~250M)

Present: ~500 parameters per Block Group...~100M values

Future: Process ~20M Policy Locations... Billions of values!

QPC predictive analysis Traditional non location-based analytic data

Technology evaluationTechnology evaluation

Technical Requirements Scalability Performance Flexibility

Selected Oracle 10g Spatial for geoprocessing• Server-side processing• Extremely scalable to support expected growth• Interoperable with GIS when functionality becomes necessary

Technology Options In-house software

development GIS Spatial RDBMS

Geoprocessing overviewGeoprocessing overview

Oracle 10g Spatial

GIS platforms Web mapping apps SQL/Java API

Nation-wide Source Locations (Census Block Groups, Policy Locations etc)

Nation-wide Target Locations (Businesses, Landmarks, Traffic, Elevation etc)

Geoprocessing results

Standard Oracle development best practices applied to geoprocessing:

Indexing (incl Spatial), Partitioning (incl Spatial), and many other optimization techniques

Rapid construction of spatial data warehouse

~400 location-based values / sec

~30M location-based values / day(on less than state-of-the art h/w)

QPC analysis platforms

Test bed geoprocessing

Asynchronous complete geoprocessing

Results review and extraction for SAS etc

Geoprocessing User InterfaceGeoprocessing User Interface

Effect of Different Traffic GeneratorsEffect of Different Traffic Generators

Top Tier Bottom TierRestaurant 30% Racetrack or amusement

park11%

Grocery Store 26% Hotel, motel resort, or spa 5%

Elementary or Secondary School

26% National park or forest 4%

Bank 25% Local or community park 3%

Car Dealer 23% Airport 2%

Gas Station 22% Doctor’s office or clinic 1%

Liquor Store 18% Religious institution -10%

Increase in physical damage claims by living within one mile of:

Zip Code 94106: Model Differentiation Zip Code 94106: Model Differentiation

Improve over current practicesImprove over current practices

Current Practices

IIA

Model

%

Improvement

Bodily Injury 16.47% 20.13% 22%

Physical

Damage

8.28% 11.83% 42%

Collision 9.34% 11.28% 20%

Comprehensive 17.66% 20.07% 13%

Gini Indexes (representing gains over random model)

Thanks for attending!

stracy@qualityplanning.com

jlemus@qualityplanning.com

dlapp@fargeo.com