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

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Presentation on theme: "1 SAS Global Forum 2009 Marty Ellingsworth (iiA) The views expressed by the presenter does not necessarily represent the views, positions, or opinions."— Presentation transcript:

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2 1 SAS Global Forum 2009 Marty Ellingsworth (iiA) The views expressed by the presenter does not necessarily represent the views, positions, or opinions of ISO.

3 2 Overview Analytic Environment About ISO Analytics Framework – Ecosystem – Innovation process – Data opportunities – Sample Problem Whats next – Good to Great Analytic Environment About ISO Analytics Framework – Ecosystem – Innovation process – Data opportunities – Sample Problem Whats next – Good to Great

4 3 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 Business Environment Why things are becoming so data driven. Each Company Define, attract, retain, and grow good customers Match offering to customer Improve customer facing processes Reduce expenses while building skills

5 4 Sales and Distribution Producer Segmentation Market Planning Revenue Forecasting Cross sell and Up sell Retention and Profitability Underwriting Risk Selection and Pricing Portfolio Management Premium Adequacy Billing and Collections Management Claims Payment Accuracy Claim Collaboration > Fraud Detection > Subrogation > Risk Transfer > 3 rd Party Deductible > Reinsurance Recoverable General Organizational Overview An information business focused on risk taking. Make. Sell. Serve.

6 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

7 6 Better Analytics Better Data Better Decision Support ISOs Strategy Best Customer Decisions property/casualty insurance mortgage lending healthcare government, and human resources.

8 7 ISO Family Of Companies Domus Systems

9 8 Strategic Space (2008+) DATA LOSS PREDICTION RISK SELECTION & PRICING FRAUD DETECTION & PREVENTION LOSS QUANTIFICATION COMPLIANCE & REPORTING HazardsLosses Assets Risk Analytics & Decision Support Data Employment Decisions Enterprise Risk Mgmt Healthcare P & C Insurance Mortgage Lending Government Next?

10 9 World-Class Staff Actuarial science Data management Mathematics Statistical modeling and predictive analytics Operations Research Economics Chemical, environmental, electrical, and other engineering disciplines 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 We have more than 400 individuals with advanced degrees, certifications, and professional designations in such fields as:

11 10 ISO Family Of Companies Domus Systems

12 11 Emerging Value in the Enterprise What way can we create value together? What are we already doing? Whats working / not working? Some ideas on next steps What way can we create value together? What are we already doing? Whats working / not working? Some ideas on next steps

13 12 The iiA Role

14 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 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

15 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.

16 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

17 16 Roles in the analytic process

18 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 Projects value/success Market getting softer (turning) – More price competition – Less U/W accuracy – More oops moments reveal themselves Pressures – Time, turnaround, goal attainment Identify "best bang for buck" Measure of Projects 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

19 18 innovation

20 19 7 SOURCES OF INNOVATION IMPULSES (Drucker) INTERNAL 1. unexpected event 2. contradiction 3. change of work process 4. change in the structure of industry or market EXTERNAL 5. Demographic changes 6. Changes in the world view 7. New knowledge INTERNAL 1. unexpected event 2. contradiction 3. change of work process 4. change in the structure of industry or market EXTERNAL 5. Demographic changes 6. Changes in the world view 7. New knowledge

21 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 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

22 21 Whats in analysis? Information Theory Applied Statistics Machine Learning Algorithms Database Management ANALYTICS High Performance Computers Visualization New Techniques More/Better Data FEEDBACK

23 22 Why text works – academic origins…

24 23 Improve the Quality of Knowledge Transform Knowledge Up the Value Taxonomy Capability Expertise Knowledge Information Data Sensory

25 24 Types of Capabilities Actuarial Statistical analysis Visualization Geospatial Text mining New Data Better Data

26 25 The Role of Synergy Synergy means that the whole is more than the sum of the parts. Synergy leads to: 1. Increased customer and shareholder value 2. Strategic focus in the management process 3. Efficient operating costs 4. Savvy investment through collaboration 5. Serendipitous Opportunities Synergy means that the whole is more than the sum of the parts. Synergy leads to: 1. Increased customer and shareholder value 2. Strategic focus in the management process 3. Efficient operating costs 4. Savvy investment through collaboration 5. Serendipitous Opportunities

27 26 Expect the Unexpected Results: –Trend Following –Need Spotting –Market Research –Solution Search –Serendipity Source: Expect the Unexpected, The Economist Technology Quarterly, September 2003 Success to Failure Rates 1:3 2:1 4:1 7:1 13:1 Serendipity => Taking advantage of unplanned opportunity Creating Successful Innovations

28 27 Structured data Semi-structured data Unstructured data Text Pictographic Graphics Multimedia Voice Video Geospatial Multi-Spectral Climatologic Atmospheric Types of Data and the Data Opportunity

29 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

30 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

31 30 Deriving Data = Power (Contd) 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 Govt Statistics: Crime Rate, Employment, Traffic Other Stats: Rents, Occupancy, Zoning, Mgd Care

32 31 Extraction Engines Identify and type language features Examples: People names Company names Geographic location names Dates Monetary amount Phone numbers Others… (domain specific) Identify and type language features Examples: People names Company names Geographic location names Dates Monetary amount Phone numbers Others… (domain specific)

33 32 Building Chronologies can be very useful Process flow and cash flow are traceable. Date of Injury Date of 1st Treatment Date of First Report of Injury: Employer Insurer Date Accepted or Denied Date of Return to Work Date of MMI or P & S Date of 1st Payment Date Claim Closed Date Claim Re-Open Date Claim Re-Closed

34 33 Roll up and roll down the data for the proper level of analysis. Claim System Claim File $x,xxx.xx Medical Payments Medical Bill Review Systems Bill Record Payments Reserves Indemnity Payments Expense PaymentsBill Line Item Detail Reduction Reasons Charged versus Paid Bill Review Rule Fee Schedule U&C Repricing PPO Discount Other Savings Bill Review Rule Reasons Bill Review Vendor

35 34 Accident: 170824130 - Employee Injured In Fall From Second-Floor Decking InspectionOpen DateSICEstablishment Name 12736636707/29/19961521 xxxxxxxxxxxxxxxxxxxxxxxx Employee #1 was atop of the second floor decking of a newly constructed home, connecting frame work for a wall. He fell 18 ft 6 in., sustaining injuries that required hospitalization. Employee #1 was not tied off, nor were any other means of fall protection in use. He had not been trained in working from an elevated work surface, the company did not have a written safety program, and regular inspections were not performed. Keywords: decking, fall, tie-off, untrained, work rules, fall protection, construction InspectionAgeSexDegreeNatureOccupation 112736636 7 29MHospitalize d injuries Cut/Lacerati on Carpenters Source: U.S. Department of Labor Occupational Safety & Health Administration Accident Report Detail Accident Investigation Summaries (OSHA-170 form) which result from OSHA accident inspections. See for yourself ---The importance and relevance of text not tied off, nor were any other means of fall protection in use. He had not been trained in working from elevated work surface the company did not have a written safety program, and regular inspections were not performed.

36 35 GeoSpatial layers –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 Location Analyst taps into ISO GIS Repository:

37 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 * 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

38 37 P&C Personal Lines Situation

39 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 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

40 39 Number of Companies writing Personal Auto Insurance in the US Indication of Increased Competition 1/3 of companies gone in 12 years

41 40 Below 50 now has only 9% for remaining 280 groups Indication of Increased Competition

42 41 How Analytics Fuel Competition $600 $800$1000 $600 $800$1000 $600 $800$1000 My Book of Business (Actual Cost per Policy) My Rate (Average) If your competitor has advanced analytics, your book and your profitability are vulnerable Total Revenue $1000 $900 $1800 $800 $2400

43 42 Predictive Analytics for the Community Environment The Environment is the Exposure

44 43 In Depth for Auto Weather Component Coverage Frequency Traffic Generators Traffic Composition Weather Neural Net Weather RBF Weather Temperature Scale Clusters & Other Summaries Weather Summary Variables 35 Years of Weather Data Weather Precipitation Scale Neural Net Weather MLP 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

45 44 Combining Environmental Variables at a Particular Garage Address Individually, the geographic variables have a predictable effect on accident rate and severity. 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. Variables for a particular location could have a combination of positive and negative effects.

46 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 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

47 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 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 Analytics Factors Affecting Auto Loss Experience

48 47 State Territory Vehicle Age & Symbol Limits & Deductibles Special Adjustments Environmental Risk Module: Weather, Street, Businesses, Traffic Density, Driving Patterns etc Vehicle Risk Module: Weight, Engine Size, etc. Class Refined Points Module No Change Policy Risk Module Interactions of all indicators State VIN Address Personal Identifiers Address, Drivers, Vehicles Rating Plan ISO Risk Analyzer Input Credit Module (optional) ISO Risk Analyzer ® Personal Auto Framework

49 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 Major innovations in an historically static rate plan Increased competition Profitable growth for adopters of advanced analytics Hunger for the next innovation

50 49 Good to Great

51 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 KSAs sometimes outside 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 KSAs sometimes outside

52 51 Next Generation iiA Systems Analytics Platform – Hardware Exploring a single large analytics server or a grid solution that ties together many commodity processors either solution will be a true client/server analytics – Software – SAS Enterprise Miner Industry standard predictive analytics software suite Will increase analyst productivity as well as the quality of the final models and documentation Analytics Data Store – Goal: Professional management of the data used by iiA for model development and production model scoring – Characteristics Professional Scalable Well-documented

53 52 Highlights of the Proposed Solution SAS GRID computing infrastructure – Allows diagonal scalability Add higher-capacity machines to grid to support future growth Protects and increases life-span of investment in hardware – Holy grail of scalable, adaptive, on-demand computing SAS EnterpriseMiner – Full-function, grid-enabled data mining platform Extensive suite of data processing and modeling methodologies – One of two top Analytics products in the market – Industry-tested stability and reliability – wide usage SAS JMP Visual BI – Powerful visualization and visual data exploration software SAS Model Manager – Seamless management of models – assessing new models, archiving old models, and deploying/using current models in production SAS GRID computing infrastructure – Allows diagonal scalability Add higher-capacity machines to grid to support future growth Protects and increases life-span of investment in hardware – Holy grail of scalable, adaptive, on-demand computing SAS EnterpriseMiner – Full-function, grid-enabled data mining platform Extensive suite of data processing and modeling methodologies – One of two top Analytics products in the market – Industry-tested stability and reliability – wide usage SAS JMP Visual BI – Powerful visualization and visual data exploration software SAS Model Manager – Seamless management of models – assessing new models, archiving old models, and deploying/using current models in production

54 53 Highlights of the Proposed Solution Benefits of choosing SAS – ISO is a long-standing SAS customer (since 1982) Can leverage loyalty discounts Known vendor with proven value to ISO Additional discounts obtained in other SAS licenses (e.g., Mainframe) – SAS is the most common platform in the industry Easier to find candidates with SAS/Eminer knowledge and experience – SAS offers comprehensive training (compared to other competitors) Easier to keep staff on the cutting-edge of new modeling methodologies and business applications Benefits of choosing SAS – ISO is a long-standing SAS customer (since 1982) Can leverage loyalty discounts Known vendor with proven value to ISO Additional discounts obtained in other SAS licenses (e.g., Mainframe) – SAS is the most common platform in the industry Easier to find candidates with SAS/Eminer knowledge and experience – SAS offers comprehensive training (compared to other competitors) Easier to keep staff on the cutting-edge of new modeling methodologies and business applications

55 54 Grid Processing Improves Speed & Capacity Increasing Job Size Increasing Number of Users & Jobs Optimize the Efficiency and Utilization of Computing Resources

56 55 SAS Enterprise Miner – Parallelized Workload Balancing Parallel Processing Reduces Time to Results

57 56 Key Benefits of Infrastructure Investment Stable, high-availability platform Increased bandwidth for simultaneous users One platform offering multiple tools/methods Build models quicker and fail faster for better models Visualization capabilities will significantly reduce data exploration timelines Model assessment and comparison capabilities built-in – no separate coding necessary Significant risk mitigation in model maintenance and archiving Data warehousing capability will shorten the cycle on re-use of data in other initiatives Stable, high-availability platform Increased bandwidth for simultaneous users One platform offering multiple tools/methods Build models quicker and fail faster for better models Visualization capabilities will significantly reduce data exploration timelines Model assessment and comparison capabilities built-in – no separate coding necessary Significant risk mitigation in model maintenance and archiving Data warehousing capability will shorten the cycle on re-use of data in other initiatives

58 57 Summary Centralized, shared environment Dynamic resource allocation to meet peak demand Policies and prioritization for use of resources Run large more complex analysis De-couple applications from infrastructure Ease maintenance of computing infrastructure Improve price/performance with commodity hardware Scale out cost effectively as needs grow Centralized, shared environment Dynamic resource allocation to meet peak demand Policies and prioritization for use of resources Run large more complex analysis De-couple applications from infrastructure Ease maintenance of computing infrastructure Improve price/performance with commodity hardware Scale out cost effectively as needs grow Bar size 2048

59 58 Why are we really here… Why will we be back here next year… CONCLUSION Why things are becoming so data driven. More data-savvy Executives Ever improving analytic solutions Industry, Third party, and Government Data Structured, Unstructured, and Location Data Faster, Cheaper, Better – Processors, Storage, & Tools Growing Skill Sets of Staff and Vendors

60 59 Marty Ellingsworth mellingsworth@iso.com The views expressed by the presenter does not necessarily represent the views, positions, or opinions of ISO.


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