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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.

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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

discussion themes
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
some areas that insurance predictive modeling can address
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
before insurance predictive modeling class underwriting
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

a predictive modeling approach
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 technologiesA 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

with insurance predictive modeling individual risk scoring
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

what the process is not what it is
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
using the lift curve for business applications renewal business
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%

using the lift curve for business applications new business
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%

end to end implementation making models come alive

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
end to end implementation making models come alive1

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?
end to end implementation making models come alive2

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
end to end implementation making models come alive3

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
end to end implementation making models come alive4

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?
end to end implementation making models come alive5

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
end to end implementation making models come alive6

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
contact information
Contact Information

John Lucker

Principal

Deloitte Consulting

860-543-7322

[email protected]

Michele Yeagley

Asst. Vice President

Harleysville Insurance

215-256-5403

[email protected]

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