SAS Global Forum 2009 Marty Ellingsworth (iiA)

1. The views expressed by the presenter does not necessarily represent the views, positions, or opinions of ISO. SAS Global Forum 2009 Marty Ellingsworth (iiA)

2. Overview • Analytic Environment • About ISO • Analytics Framework • Ecosystem • Innovation process • Data opportunities • Sample Problem • What’s next – Good to Great

3. Business Environment Why things are becoming so data driven. The Market • Electronic connectivity is expected • Touch point knowledge is anticipated • Personalized service is assumed • Ease of doing business is desired • Low tolerance for not learning Each Company • Define, attract, retain, and grow “good” customers • Match offering to customer • Improve ‘customer facing processes’ • Reduce expenses while building skills

4. Producer Segmentation Market Planning Revenue Forecasting Cross sell and Up sell Retention and Profitability Sales and Distribution Underwriting Claims Risk Selection and Pricing Portfolio Management Premium Adequacy Billing and Collections Management Payment Accuracy Claim Collaboration > Fraud Detection > Subrogation > Risk Transfer > 3rd Party Deductible > Reinsurance Recoverable General Organizational Overview An information business focused on risk taking. Make. Sell. Serve.

5. Analytic Value Effort Framework Reporting = “Having the data” Timeliness and accuracy Reports and Tables Surfacing data with agility Descriptive Analyses = “Seeing the data” Scorecards / Measurements Profiles and Exceptions Segmentation Analytic Modeling = “Knowing the data” Understand Trends Evaluate Business Practices Choice Models and “What ifs” Predictive Analytics = “Acting on the data” Informed decision-making Actionable Information Engines

6. ISO’s Strategy Better Analytics Better Data Best Customer Decisions property/casualty insurance mortgage lending healthcare government, and human resources.  Better Decision Support

7. Domus Systems ISO Family Of Companies

8. Strategic Space (2008+) Assets Hazards Losses Risk Data Analytics & Decision Support Government Employment Decisions P & C Insurance Mortgage Lending Enterprise Risk Mgmt Healthcare Next?

9. World-Class Staff We have more than 400 individuals with advanced degrees, certifications, and professional designations in such fields as: • Actuarial science • Data management • Mathematics • Statistical modeling and predictive analytics • Operations Research • Economics • Chemical, environmental, electrical, and other engineering disciplines • Healthcare • Soil mechanics • Geology • Remote sensing • Meteorology • Atmospheric and climate science • Oceanography • Applied physics • Many other disciplines

10. Domus Systems ISO Family Of Companies

11. Emerging Value in the Enterprise • What way can we create value together? • What are we already doing? • What’s working / not working? • Some ideas on next steps

12. The iiA Role

13. Critical Success Factors • Technical Expertise • in Statistical Modeling, Data Mining, and Data Management • Intimate Market Awareness • Strong Coordination • with other company units • Underwriting, Loss Control, Claims, Sales/Agents • Senior Executive Commitment and Support • Access to Data • Project selection and execution

14. Golden Rule of Analysis Your product is not computers, application software systems, user interfaces or database connections Your product is reliable information that helps answer compelling business questions.

15. Predictive Modeling Projects you should do Loss Control Fraud Prevention Property Inspections Assess Work sites Re-underwriting Cost Avoidance Automate Manual Work Appetite Qualification Underwriting Guides Redundant Processes Vendor Sourcing Spend Analysis Cash-flow Opportunity Subrogation Credit to Loss Third Party Deductible Premium Audit (Comm) Account Identification Audit Ordering Insured to Value (PI) Better Decision Making Risk Selection Renewal (Attrition) New (Acquisition) Cross-sell & Up-sell Portfolio Management Broker/Agent Profiles Medical Management Litigation Management Large Loss Reserving Improved Collaboration

16. Roles in the analytic process

17. Predictive Modeling Staff Portfolio Challenge Limited Resources People – need to train Recruiting/retaining Limited Time Decision on whether and/or how to audit Limited Funds Need to show value of audit process ROI More work than people Predictive Model Development Group Identified Concerns • Pressures • Time, turnaround, goal attainment • Identify "best bang for buck" • Measure of Project’s value/success • Market getting softer (turning) • More price competition • Less U/W accuracy • More “oops” moments reveal themselves Key need is to efficiently allocate scarce resources to optimize your efforts across the Insurance Value Chain

18. innovation

19. 7 SOURCES OF INNOVATION IMPULSES(Drucker) INTERNAL • unexpected event • contradiction • change of work process • change in the structure of industry or market EXTERNAL • Demographic changes • Changes in the world view • New knowledge

20. # 7. New knowledge • Based on convergence or synergy of various kinds of knowledge, their success requires, high rate of risk • Thorough analysis of all factors. identify the “missing elements” of the chain and possibilities of their supplementing or substitution; • Focus on winning the strategic position at the market. the second chance usually does not come; • Entrepreneurial management style. Quality is not what is technically perfect but what adds the product its value for the end user

21. What’s in ‘analysis’? • Information Theory • Database Management • Visualization • High Performance Computers ANALYTICS • Applied Statistics • Algorithms • Machine Learning • New Techniques • More/Better Data • FEEDBACK

22. Why text works – academic origins…

23. Transform Knowledge Up the Value Taxonomy Capability Expertise Knowledge Information Data Sensory Improve the Quality of Knowledge

24. Actuarial Statistical analysis Visualization Geospatial Text mining New Data Better Data Types of Capabilities

25. The Role of Synergy • Synergy means that the whole is more than the sum of the parts. • Synergy leads to: • Increased customer and shareholder value • Strategic focus in the management process • Efficient operating costs • Savvy investment through collaboration • Serendipitous Opportunities

26. Expect the Unexpected Creating Successful Innovations • Results: • Trend Following • Need Spotting • Market Research • Solution Search • Serendipity • Success to Failure Rates • 1:3 • 2:1 • 4:1 • 7:1 • 13:1 Serendipity => Taking advantage of unplanned opportunity Source: Expect the Unexpected, The Economist Technology Quarterly, September 2003

27. Types of Data and the Data Opportunity Structured data Semi-structured data Unstructured data Text Pictographic Graphics Multimedia Voice Video Geospatial Multi-Spectral Climatologic Atmospheric

28. What to learn from Structured Data Significant pre-processing of raw data is needed to create useful informational features. • Repeatable Patterns • Trends, Seasons, Cycle • Propensities, Likelihood • Causation and Interaction • Ratios between Dollars and Distances • Stakeholder Behavior • Unlikely Occurrences • Proximity of stakeholders • Ownership interests of stakeholders Data Fusion and Learning is the key to successful Data Mining

29. Deriving Data = Power Depending on the target variable, there are many factors that may be relevant for modeling. • Totals: Household Income • Trends: Rate of Medical Bill Increases • Ratios: Claims/Premium, Target/Median • Friction: Level of inconvenience, ratio of rental to damage • Sequences: Lawyer-Doctor, Auto-Life Policy • Circumstances: Minimal Impact Severe Trauma • Temporal: Loss shortly after adding collision • Spatial: Distance to Service, proximity of stakeholders • Logged: Progress Notes, Diaries, • Who did it, When, “Why”

30. Deriving Data = Power (Cont’d) Depending on the target variable, there are many factors that may be relevant for modeling. • Behavioral: Deviation from past usage, spike buying • Experience Profiles: Vendor, Doctor, Premium Audit • Channel: How applied, How reported, Service Chain • Legal Jurisdiction: Venue Disposition, Rules • Demographics: Working, Weekly wage, lost income • Firmographics: Industry Class Code Vs Injuries Claimed • Inflation: Wage, Medical, Goods, Auto, COLA • Gov’t Statistics: Crime Rate, Employment, Traffic • Other Stats: Rents, Occupancy, Zoning, Mgd Care

31. Extraction Engines • Identify and type language features • Examples: • People names • Company names • Geographic location names • Dates • Monetary amount • Phone numbers • Others… (domain specific)

32. Date of First Report of Injury: Employer Insurer Date of 1st Payment Date of Return to Work Date Claim Re-Open Date of Injury Date of 1st Treatment Date Accepted or Denied Date of MMI or P & S Date Claim Closed Date Claim Re-Closed Building Chronologies can be very useful Process flow and cash flow are traceable.

33. Roll up and roll down the data for the proper level of analysis. Claim System Claim File $x,xxx.xx Bill Review Vendor Payments Medical Payments Medical Bill Review Systems Bill Record Indemnity Payments Expense Payments Bill Line Item Detail • Reduction Reasons • Charged versus Paid • Bill Review Rule • Fee Schedule • U&C Repricing • PPO Discount • Other Savings Reserves Bill Review Rule Reasons

34. See for yourself ---The importance and relevance of text Source: U.S. Department of Labor Occupational Safety & Health Administration Accident Report DetailAccident Investigation Summaries (OSHA-170 form) which result from OSHA accident inspections.

35. GeoSpatial layers Location Analyst taps into ISO GIS Repository: • TeleAtlas Dynamap 2000 Files (includes a Roadbase, Landmarks, Water bodies, etc.) • Zip Code Boundaries • State/County/Municipal Boundaries • Census boundaries: Track > Block Group > Block • Aerial Imagery – DigitalGlobe/GlobeXplorer • All LOCATION GIS Layers • FireLine and historical wildfire burn perimeters • ISO statistical data and related analytics (ZIP-level) • CAP Index Crime Information • USGS Topography • US Census Demographics • Government promulgated natural catastrophe and historical weather layers • Coastlines • US Labor Statistics • Custom datasets (e.g., customer portfolios/individual risks) • County Tax Assessor data, for 75M homes • Flood Information Mapping • Current weather conditions/current wildfire activity feeds

36. What can help? • Integration of data with other frauds • Bridging to new data sources • Smarter transformation of data • Text Mining – expose information • GIS Platform – geospatial elements • Graph mining – highlight social networks • Grid computing – diagonal scaling * *diagonal scaling = you can scale up and out at the same time

37. P&C Personal Lines Situation

38. Market Demand - Opportunity • Top carriers control large markets • E.g., Personal Auto – Top 25 carriers hold over 80% of market (over $120B of a total market >$160B) • Strong motivation to – • “Protect” market share • Grow against stiff odds • Predictive analytics has gained senior leadership attention as a mechanism to – • Execute risk-based pricing and segmentation • Create competitive/strategic differentiation • Generate operational efficiencies

39. Indication of Increased Competition Number of Companies writing Personal Auto Insurance in the US 1/3 of companies gone in 12 years

40. Indication of Increased Competition Below 50 now has only 9% for remaining 280 groups

41. How Analytics Fuel Competition My Book of Business(Actual Cost per Policy) My Rate (Average) Total Revenue $600 $800 $1000 $800 $2400 $600 $800 $1000 $900 $1800 $600 $800 $1000 $1000 $1000 If your competitor has advanced analytics, your book and your profitability are vulnerable

42. Predictive Analytics for theCommunity Environment The Environment is the Exposure

43. In Depth for Auto Weather Component Environmental Model Loss Cost by Coverage Frequency × Severity Causes of Loss Frequency Sub Model Data Summary Variable Raw Data

44. Combining Environmental Variablesat 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.

45. Techniques Employed in Variable Reduction • EDA (Exploratory Data Analysis) – univariate analysis, transformations, known relationships • Statistical Techniques – greedy selection, machine learning techniques • Sampling – cross validation, bootstrap • Sub models/data reduction – neural nets, splines, principal component analysis, variable clustering • Spatial Smoothing – At various distances and/or with parameters related to auto insurance loss patterns

46. Weather: Measures of snowfall, rainfall, temperature Traffic Density and Driving Patterns: Commute patterns Public transportation usage Traffic Composition: Size of vehicles Age and cost of vehicles 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 Breakthroughs in Personal Auto AnalyticsFactors Affecting Auto Loss Experience

47. ISO Risk Analyzer ® Personal Auto Framework ISO Risk Analyzer Input Rating Plan State State Environmental Risk Module: Weather, Street, Businesses, Traffic Density, Driving Patterns etc Territory Address Vehicle Age & Symbol VIN Vehicle Risk Module: Weight, Engine Size, etc. Class Refined Points Module Personal Identifiers Credit Module (optional) Limits & Deductibles No Change Special Adjustments No Change Address, Drivers, Vehicles Policy Risk Module Interactions of all indicators

48. What has the impact been? • Major innovations in an historically static rate plan • Increased competition • Profitable growth for adopters of advanced analytics • Hunger for the next innovation

49. Good to Great

50. What was Not Working • Infrastructure impacting work productivity • Constant appetite for more “computing” capacity • Limited ability to process large datasets • Need to build core capabilities – • Data access • Leveraging multiple modeling methodologies • Geo-spatial analysis • Managing and maintaining multiple versions of models • Text analytics (e.g. cause of loss and entity extraction) • Identity resolution • ISO Search and Retrieve information • Remote team collaboration is cumbersome • Critical KSA’s sometimes ‘outside’