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2007 CAS PREDICTIVE MODELING SEMINAR PROJECT MANAGEMENT FOR PREDICTIVE MODELS BETH FITZGERALD, ISO Accomplishing Business Goals • • • 2 Project Management Implementation Future Project Management • Determine business processes that support strategic goals – Underwriting decisions – Pricing decisions • Develop project plan aligned with strategic goals – Model Building – Technology Development – Implementation Phases • Determine project needs • Monitor actual vs. planned costs/milestones 3 Project Needs • Team Skills – Data management – Analytical/statistical – Technology – Business Knowledge • Data • Statistical Tools • Computer Capacity 4 Prior to Modeling • Formulate the Problem • Evaluate Possible Data Sources • Prepare the Data • Explore the Data with Simple Modeling Techniques 5 What percent of a model building project is the data preparation and data management? 25% 50% 75% 85% 6 Prepare the Data • Do quality checks in level of detail needed for project • Understand how to prepare individual variables for use in models • Need to be practical about number of classification categories models can handle • Need to decide on truncation and bucketing of variables that are continuous • Create new variables 7 Data Management Issues • Matching additional internal policy information to premium/loss data – Different points in time – Tracking & balancing audited exposures • Different summarization keys – handling of mid-term endorsements • Address scrubbing • Matching to external data for correct point in time • Significance of missing values within variable 8 Modeling Procedures and Diagnostics • Basic modeling training – GLM, Data Mining • Decide on appropriate diagnostics • Evaluate diagnostics 9 Modeling Process Data Gathering Data Linking Data Cleansing Evaluation Business Knowledge Determine Predictive Variables 10 Analyze Variables Modeling Business Questions • What goals are you trying to achieve? • What results do you expect to see? • How will you know if the results are reasonable? • How do you ensure sufficient knowledge transfer to business staff? 11 Model Performance Relative Loss Ratio Lift Optimal Model Loss Ratio Relativity 1.3 1.2 1.1 LR Relativity by Decile 1 0.9 0.8 0.7 1 2 3 4 5 6 7 8 Decile of Worst to Best Risk 12 9 10 Model Input/Output • Model input considerations – Access to data – Robustness/quality of data – Timeliness of refreshed data • Design Model output for users – Definition of output – expected loss ratio, pure premium, loss ratio relativity? – Provide support for output – reason codes 13 Business Implementation of Model • Model usage determined by strategic goals – Underwriting risk decision – Pricing of risks – Support of market growth • Integration of Model into business workflow decisions – Consistency in underwriting/pricing decisions – Compliance with regulations based on implementation decisions 14 Implementation of Model Workflows: • Underwriting – – New Business Renewal business • Rating – – 15 Pricing Coverage Adjustment Implementation of Model • New Business decision options – – – – Write risk Request additional info on risk Decline risk Adjust price/coverage • Consider model output alone or along with other information available from application • Model output needed within seconds for quick decision 16 Implementation of Model • Renewal decision options – Automatic renewal – Flag for non-renewal – Adjust coverage level for risk – Adjust pricing for risk • Initial Year – review all in-force policies on weekly or monthly basis • Subsequent years – establish schedule for reevaluation based on specific underwriting guidelines 17 Implementation of Model Rating • Model O/P represents relative loss ratio factor • Determine rating selections • Determine rating process – Modify application of IRPM plan – Implement new rating factors based on Model – Tier risks into different insurers within insurer group 18 Technology Development • Incorporate business implementation decisions • Decide on how Model will be accessible electronically – Web-based interface – Integrated into existing workflow – Batch processing • Develop/Modify Systems – Phase-in technology • Model uses information from a third-party vendor • Determine I/P and O/P criteria 19 Rollout Implementation of Model • Prepare Announcement/Training Material for Internal & External Customers • Coordinate Implementation Phases • Monitor Feedback/Adjust Implementation • Monitor Results against Strategic Goals 20 Future of Predictive Modeling • More refined rating plans – Industry-sourced or internally developed – Combination of internally-developed & industry- sourced risk component variables • Ongoing updating and maintenance of Models – Refresh data – New data sources/variables – New tools/techniques – React to new market environments 21