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2007 CAS PREDICTIVE MODELING SEMINAR PROJECT MANAGEMENT FOR PREDICTIVE MODELS

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Project Needs. Team Skills. Data management. Analytical/statistical. Technology. Business Knowledge ... project is the data preparation and data management? ... – PowerPoint PPT presentation

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Title: 2007 CAS PREDICTIVE MODELING SEMINAR PROJECT MANAGEMENT FOR PREDICTIVE MODELS


1
2007 CAS PREDICTIVE MODELING SEMINAR PROJECT
MANAGEMENT FOR PREDICTIVE MODELS
  • BETH FITZGERALD, ISO

2
Accomplishing Business Goals
  • Project Management
  • Implementation
  • Future

3
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

4
Project Needs
  • Team Skills
  • Data management
  • Analytical/statistical
  • Technology
  • Business Knowledge
  • Data
  • Statistical Tools
  • Computer Capacity

5
Prior to Modeling
  • Formulate the Problem
  • Evaluate Possible Data Sources
  • Prepare the Data
  • Explore the Data with Simple Modeling Techniques

6
What percent of a model building project is the
data preparation and data management?
  • 25
  • 50
  • 75
  • 85

7
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

8
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

9
Modeling Procedures and Diagnostics
  • Basic modeling training GLM, Data Mining
  • Decide on appropriate diagnostics
  • Evaluate diagnostics

10
Modeling Process
Data Gathering
Data Linking
Data Cleansing
Analyze Variables
Evaluation
Business Knowledge
Determine Predictive Variables
Modeling
11
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?

12
Model Performance
13
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

14
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

15
Implementation of Model
  • Workflows
  • Underwriting
  • New Business
  • Renewal business
  • Rating
  • Pricing
  • Coverage Adjustment

16
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

17
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

18
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

19
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

20
Rollout Implementation of Model
  • Prepare Announcement/Training Material for
    Internal External Customers
  • Coordinate Implementation Phases
  • Monitor Feedback/Adjust Implementation
  • Monitor Results against Strategic Goals

21
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
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