Casualty Actuarial Society - Predictive Modeling Special Interest Seminar September 2005

1. Implementation of Predictive Models – Making Models Come Alive • John Lucker – Principal – Deloitte Consulting LLP • Michele Yeagley, ACAS, MAAA – Asst. Vice President - Harleysville Insurance Casualty Actuarial Society - Predictive Modeling Special Interest Seminar September 2005

2. Discussion Themes • Predictive Modeling provides the toolset to assist with a variety of critical business operations • The market is softening and better risk assessment capabilities are needed to avoid inappropriate soft market dynamics and naïve reactions • Predictive Models can be implemented in ways that do not require complex, expensive efforts • The financial benefits that can be realized from predictive models are very significant – phased and rapid implementation can help fund additional future analytics and more complex implementations • The three most important parts of any predictive modeling project are (1) implementation; (2) implementation; and (3) implementation – building a great model should be a given

3. Some Predictive Modeling Basics 2

4. Some Areas that Insurance Predictive Modeling Can Address • Customer Management • Cross-Sell Success Rates • Client View • Geo / Market Expansion • Book Rollover/Transfer • Proactive Call Centers • Audit & Billing Options • Reinsurance • Assumed Biz Scoring • Retro Placement/Retention • M&A • Pre-Deal Assessment • Post-Deal Remediation • Risk Profitability Assessment • Risk Attraction • Risk Retention • Risk Avoidance • Non-Renewals • Right-Pricing • Claims Management • Claim Triage • Duration Reduction • Fraud Propensity • Producer Management • Profitable Production • Production Retention • Recruitment

5. Before Insurance Predictive Modeling – Class Underwriting Numbers are Illustrative Workers’ Compensation Commercial Auto CMP / BOP Property General Liability Private Passenger Auto Homeowners Roofers Youthful Drivers 140% 90% 135% 87% Overall Loss Ratio of 75% 125% 82% 115% Florists Middle Aged Drivers 78% 110% 75% 100% 72% 90% 68% 80% 65% 70% 63% 60% Below average Average Above average

6. Building and deploying predictive models requires a specialized combination of skills covering data management, data cleansing, data mart construction, actuarial and statistical analysis, data mining and modeling, and insurance operational and business processing and technologies A Predictive Modeling Approach Data Aggregation & Data Cleansing Score For Each Policy 180% 160% External Data 140% 120% 110% 100% 90% 80% 70% 60% Predicted loss ratio Internal Data Evaluate and Create Variables Business Rules Engine Synthetic Variables Develop Predictive Model Score Driven Business Applications

7. Y = A + B(Var 1) + C(Var 2) + D(Var 3) + E(Var 4) + F(Var 5) … With Insurance Predictive Modeling – Individual Risk Scoring Internal / External Data Predicted Loss Ratio 120 Bob’s Flower Shop = 821 Numbers are Illustrative 90 Linda’s Flower Shop = 324 82 PredictedLoss Ratio Overall L.R. 75% 78 74 70 66 62 58 50

8. Laying the Groundwork for an End-To-End Implementation 7

9. What The Process IS NOT – What it IS • What it IS NOT • A Black Box approach • Stock delivery • Replacement for underwriters • Score used to communicate decision • Score drives results • A single variable magic bullet • Actuarial and/or systems project • Class plan underwriting • What it IS • Scoring drivers are known / understood • Collaborative throughout the business • Additional underwriting toolset • Reason codes / messages are developed • Implementation drives results • Relationship among variables is power • Business initiative • Efficient segmentation of policyholders

10. Using the Lift Curve for Business Applications – Renewal Business • Highly Profitable • Risk Attraction • Retention Priority • Less Loss Control • Less Premium Audit • More Pricing Flexibility Best 25% • Renewal Business • Underwriter • Workflow • Highly Unprofitable • Risk Avoidance • Repricing Priority • Non-Renewals • Loss Control • Premium Audit Worst 25%

11. Using the Lift Curve for Business Applications – New Business • Highly Profitable • Risk Attraction • Retention Priority • Less Loss Control • Less Premium Audit • More Pricing Flexibility Best 25% • New Business • Underwriter • Workflow • Highly Unprofitable • Risk Avoidance • Repricing Priority • Non-Renewals • Loss Control • Premium Audit Worst 25%

12. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • Predictive Models must be effectively implemented to derive their benefit potential • The financial benefits can be so significant that urgency should drive the pace of the project • Create a benefit analysis and use the benefits to drive the project – a complex process (PIF counts, LR management, retention, not written, etc) • Competitive jockeying should also drive project pace – first adopter advantages • A best practice is to create a continuum of implementation solutions and phases • Initial implementation should focus on extracting value from models before automation • Tactical implementation can be achieved in 2-4 months • Planning, planning, and then some more planning

13. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • Steering Committee and Project Committee Structure • Phased structure and focus on 80:20 Rule • Development of End-State-Vision & Project Planning Document – some key questions are: • How will predictive modeling guide decision making, pricing, and tier placement? • How will predictive models impact existing business processes (e.g. by line / account)? • How will predictive models be blended into the field and agency management process? • What key performance measures must be achieved? • How will underwriters/raters/other personnel’s compliance be measured? • What level of automation is desired for various business processes?

14. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • Extract, Transform, Load process for Internal, External, and Synthetic Data • Management of external data vendors and external data acquisition • Data quality and cleansing issues • Construction of Scoring Engine(s) (real time, batch, manual, etc) • Construction of Business Rules Engine or similar process • Construction of operational data mart • Design of technical architecture, data flow, messaging, data management, etc. • Model maintenance

15. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • Underwriting workflow (renewal business, new business, touch level) • How model scores will be used in the U/W process (scores, reason codes, action thresholds, risk avoidance, risk acquisition, retention management, account vs. monoline, etc) • How will models be used as risks proceed from new to renewals (disruption issues)? • Use of model scores for downstream processes • Relationship of model usage to field and producer management • Business rule creation, optimization and maintenance

16. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • What systems modifications are required to accommodate the process? • What will different people in different roles see throughout the process?

17. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • Identification of all new process stakeholders (Underwriting, actuarial, systems, executive, legal/regulatory, claims, field, agency, training, project management, etc) • How will predictive modeling be described internally and externally to all stakeholders? • Manage any legal/regulatory issues and concerns • Once communicated, how to deal with questions, concerns, issues, etc. from Underwriters, field personnel, agents, market analysts, etc. • Development of change management, communication, and training activities and materials • Development of necessary implementation materials for all stakeholders • Development of feedback mechanisms using objective and subjective criteria

18. Biz & Technical Planning Predictive Modeling Technical Developmt Business Process Redesign Biz & Systems Integration Organize Change Mgmt Perform Metrics & Reporting End-To-End Implementation – Making Models Come Alive • Creation of management reports and metrics measurement processes including dashboards • Communication of model usage, results tracking, and management metrics at all process points to all constituencies • Loop back processes to manage compliance or deviation of model usage business plan

19. Contact Information John Lucker Principal Deloitte Consulting 860-543-7322 JLucker@Deloitte.com Michele Yeagley Asst. Vice President Harleysville Insurance 215-256-5403 MYeagley@HarleysvilleGroup.com